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2402.08780 | Sagar Pathak | Sagar Pathak, Bidhya Shrestha and Kritish Pahi | Enhanced Deep Q-Learning for 2D Self-Driving Cars: Implementation and
Evaluation on a Custom Track Environment | 8 pages, 8 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This research project presents the implementation of a Deep Q-Learning
Network (DQN) for a self-driving car on a 2-dimensional (2D) custom track, with
the objective of enhancing the DQN network's performance. It encompasses the
development of a custom driving environment using Pygame on a track surrounding
the University of Memphis map, as well as the design and implementation of the
DQN model. The algorithm utilizes data from 7 sensors installed in the car,
which measure the distance between the car and the track. These sensors are
positioned in front of the vehicle, spaced 20 degrees apart, enabling them to
sense a wide area ahead. We successfully implemented the DQN and also a
modified version of the DQN with a priority-based action selection mechanism,
which we refer to as modified DQN. The model was trained over 1000 episodes,
and the average reward received by the agent was found to be around 40, which
is approximately 60% higher than the original DQN and around 50% higher than
the vanilla neural network.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 20:29:36 GMT"
}
] | 1,707,955,200,000 | [
[
"Pathak",
"Sagar",
""
],
[
"Shrestha",
"Bidhya",
""
],
[
"Pahi",
"Kritish",
""
]
] |
2402.08806 | Gioele Barabucci | Gioele Barabucci, Victor Shia, Eugene Chu, Benjamin Harack, Nathan Fu | Combining Insights From Multiple Large Language Models Improves
Diagnostic Accuracy | 5 pages, 2 figures, 1 table | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Background: Large language models (LLMs) such as OpenAI's GPT-4 or Google's
PaLM 2 are proposed as viable diagnostic support tools or even spoken of as
replacements for "curbside consults". However, even LLMs specifically trained
on medical topics may lack sufficient diagnostic accuracy for real-life
applications.
Methods: Using collective intelligence methods and a dataset of 200 clinical
vignettes of real-life cases, we assessed and compared the accuracy of
differential diagnoses obtained by asking individual commercial LLMs (OpenAI
GPT-4, Google PaLM 2, Cohere Command, Meta Llama 2) against the accuracy of
differential diagnoses synthesized by aggregating responses from combinations
of the same LLMs.
Results: We find that aggregating responses from multiple, various LLMs leads
to more accurate differential diagnoses (average accuracy for 3 LLMs:
$75.3\%\pm 1.6pp$) compared to the differential diagnoses produced by single
LLMs (average accuracy for single LLMs: $59.0\%\pm 6.1pp$).
Discussion: The use of collective intelligence methods to synthesize
differential diagnoses combining the responses of different LLMs achieves two
of the necessary steps towards advancing acceptance of LLMs as a diagnostic
support tool: (1) demonstrate high diagnostic accuracy and (2) eliminate
dependence on a single commercial vendor.
| [
{
"version": "v1",
"created": "Tue, 13 Feb 2024 21:24:21 GMT"
}
] | 1,707,955,200,000 | [
[
"Barabucci",
"Gioele",
""
],
[
"Shia",
"Victor",
""
],
[
"Chu",
"Eugene",
""
],
[
"Harack",
"Benjamin",
""
],
[
"Fu",
"Nathan",
""
]
] |
2402.08859 | Yingpeng Du | Yingpeng Du, Ziyan Wang, Zhu Sun, Haoyan Chua, Hongzhi Liu, Zhonghai
Wu, Yining Ma, Jie Zhang, Youchen Sun | Large Language Model with Graph Convolution for Recommendation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, efforts have been made to use text information for better
user profiling and item characterization in recommendations. However, text
information can sometimes be of low quality, hindering its effectiveness for
real-world applications. With knowledge and reasoning capabilities capsuled in
Large Language Models (LLMs), utilizing LLMs emerges as a promising way for
description improvement. However, existing ways of prompting LLMs with raw
texts ignore structured knowledge of user-item interactions, which may lead to
hallucination problems like inconsistent description generation. To this end,
we propose a Graph-aware Convolutional LLM method to elicit LLMs to capture
high-order relations in the user-item graph. To adapt text-based LLMs with
structured graphs, We use the LLM as an aggregator in graph processing,
allowing it to understand graph-based information step by step. Specifically,
the LLM is required for description enhancement by exploring multi-hop
neighbors layer by layer, thereby propagating information progressively in the
graph. To enable LLMs to capture large-scale graph information, we break down
the description task into smaller parts, which drastically reduces the context
length of the token input with each step. Extensive experiments on three
real-world datasets show that our method consistently outperforms
state-of-the-art methods.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 00:04:33 GMT"
}
] | 1,707,955,200,000 | [
[
"Du",
"Yingpeng",
""
],
[
"Wang",
"Ziyan",
""
],
[
"Sun",
"Zhu",
""
],
[
"Chua",
"Haoyan",
""
],
[
"Liu",
"Hongzhi",
""
],
[
"Wu",
"Zhonghai",
""
],
[
"Ma",
"Yining",
""
],
[
"Zhang",
"Jie",
""
],
[
"Sun",
"Youchen",
""
]
] |
2402.08869 | Stefan Erben | Stefan Erben and Andreas Waldis | ScamSpot: Fighting Financial Fraud in Instagram Comments | EACL 2024 Demo Paper, 11 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The long-standing problem of spam and fraudulent messages in the comment
sections of Instagram pages in the financial sector claims new victims every
day. Instagram's current spam filter proves inadequate, and existing research
approaches are primarily confined to theoretical concepts. Practical
implementations with evaluated results are missing. To solve this problem, we
propose ScamSpot, a comprehensive system that includes a browser extension, a
fine-tuned BERT model and a REST API. This approach ensures public
accessibility of our results for Instagram users using the Chrome browser.
Furthermore, we conduct a data annotation study, shedding light on the reasons
and causes of the problem and evaluate the system through user feedback and
comparison with existing models. ScamSpot is an open-source project and is
publicly available at https://scamspot.github.io/.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 00:30:18 GMT"
}
] | 1,707,955,200,000 | [
[
"Erben",
"Stefan",
""
],
[
"Waldis",
"Andreas",
""
]
] |
2402.08961 | Zhao Li | Zhao Li, Xin Wang, Jun Zhao, Wenbin Guo, Jianxin Li | HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional
Embedding | 14 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge hypergraph embedding models are usually computationally expensive
due to the inherent complex semantic information. However, existing works
mainly focus on improving the effectiveness of knowledge hypergraph embedding,
making the model architecture more complex and redundant. It is desirable and
challenging for knowledge hypergraph embedding to reach a trade-off between
model effectiveness and efficiency. In this paper, we propose an end-to-end
efficient n-ary knowledge hypergraph embedding model, HyCubE, which designs a
novel 3D circular convolutional neural network and the alternate mask stack
strategy to enhance the interaction and extraction of feature information
comprehensively. Furthermore, our proposed model achieves a better trade-off
between effectiveness and efficiency by adaptively adjusting the 3D circular
convolutional layer structure to handle different arity knowledge hypergraphs
with fewer parameters. In addition, we use 1-N multilinear scoring based on the
entity mask mechanism to further accelerate the model training efficiency.
Finally, extensive experimental results on all datasets demonstrate that our
proposed model consistently outperforms state-of-the-art baselines, with an
average improvement of 7.30%-9.53% and a maximum improvement of 33.82% across
all metrics. Meanwhile, HyCubE is 4.12x faster, GPU memory usage is 52.19%
lower, and the number of parameters is reduced by 85.21% compared with the
average metric of the latest state-of-the-art baselines.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 06:05:37 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Jun 2024 15:17:46 GMT"
}
] | 1,717,459,200,000 | [
[
"Li",
"Zhao",
""
],
[
"Wang",
"Xin",
""
],
[
"Zhao",
"Jun",
""
],
[
"Guo",
"Wenbin",
""
],
[
"Li",
"Jianxin",
""
]
] |
2402.08968 | Siwon Kim | Siwon Kim, Shuyang Dai, Mohammad Kachuee, Shayan Ray, Tara Taghavi,
and Sungroh Yoon | GrounDial: Human-norm Grounded Safe Dialog Response Generation | Accepted to findings of EACL 2024 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Current conversational AI systems based on large language models (LLMs) are
known to generate unsafe responses, agreeing to offensive user input or
including toxic content. Previous research aimed to alleviate the toxicity, by
fine-tuning LLM with manually annotated safe dialogue histories. However, the
dependency on additional tuning requires substantial costs. To remove the
dependency, we propose GrounDial, where response safety is achieved by
grounding responses to commonsense social rules without requiring fine-tuning.
A hybrid approach of in-context learning and human-norm-guided decoding of
GrounDial enables the response to be quantitatively and qualitatively safer
even without additional data or tuning.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 06:25:50 GMT"
}
] | 1,707,955,200,000 | [
[
"Kim",
"Siwon",
""
],
[
"Dai",
"Shuyang",
""
],
[
"Kachuee",
"Mohammad",
""
],
[
"Ray",
"Shayan",
""
],
[
"Taghavi",
"Tara",
""
],
[
"Yoon",
"Sungroh",
""
]
] |
2402.09047 | Tuo Leng | Yiming He, Jia Zou, Xiaokai Zhang, Na Zhu, Tuo Leng | FGeo-TP: A Language Model-Enhanced Solver for Geometry Problems | 16 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The application of contemporary artificial intelligence techniques to address
geometric problems and automated deductive proof has always been a grand
challenge to the interdiscipline field of mathematics and artificial
Intelligence. This is the fourth article in a series of our works, in our
previous work, we established of a geometric formalized system known as
FormalGeo. Moreover we annotated approximately 7000 geometric problems, forming
the FormalGeo7k dataset. Despite the FGPS (Formal Geometry Problem Solver) can
achieve interpretable algebraic equation solving and human-like deductive
reasoning, it often experiences timeouts due to the complexity of the search
strategy. In this paper, we introduced FGeo-TP (Theorem Predictor), which
utilizes the language model to predict theorem sequences for solving geometry
problems. We compared the effectiveness of various Transformer architectures,
such as BART or T5, in theorem prediction, implementing pruning in the search
process of FGPS, thereby improving its performance in solving geometry
problems. Our results demonstrate a significant increase in the problem-solving
rate of the language model-enhanced FGeo-TP on the FormalGeo7k dataset, rising
from 39.7% to 80.86%. Furthermore, FGeo-TP exhibits notable reductions in
solving time and search steps across problems of varying difficulty levels.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 09:44:28 GMT"
}
] | 1,707,955,200,000 | [
[
"He",
"Yiming",
""
],
[
"Zou",
"Jia",
""
],
[
"Zhang",
"Xiaokai",
""
],
[
"Zhu",
"Na",
""
],
[
"Leng",
"Tuo",
""
]
] |
2402.09051 | Tuo Leng | Jia Zou, Xiaokai Zhang, Yiming He, Na Zhu, Tuo Leng | FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep
Reinforcement Learning | 15 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human-like automatic deductive reasoning has always been one of the most
challenging open problems in the interdiscipline of mathematics and artificial
intelligence. This paper is the third in a series of our works. We built a
neural-symbolic system, called FGeoDRL, to automatically perform human-like
geometric deductive reasoning. The neural part is an AI agent based on
reinforcement learning, capable of autonomously learning problem-solving
methods from the feedback of a formalized environment, without the need for
human supervision. It leverages a pre-trained natural language model to
establish a policy network for theorem selection and employ Monte Carlo Tree
Search for heuristic exploration. The symbolic part is a reinforcement learning
environment based on geometry formalization theory and FormalGeo, which models
GPS as a Markov Decision Process. In this formal symbolic system, the known
conditions and objectives of the problem form the state space, while the set of
theorems forms the action space. Leveraging FGeoDRL, we have achieved readable
and verifiable automated solutions to geometric problems. Experiments conducted
on the formalgeo7k dataset have achieved a problem-solving success rate of
86.40%. The project is available at https://github.com/PersonNoName/FGeoDRL.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 09:48:39 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Feb 2024 04:50:52 GMT"
}
] | 1,708,041,600,000 | [
[
"Zou",
"Jia",
""
],
[
"Zhang",
"Xiaokai",
""
],
[
"He",
"Yiming",
""
],
[
"Zhu",
"Na",
""
],
[
"Leng",
"Tuo",
""
]
] |
2402.09052 | Yutaro Yamada | Yutaro Yamada, Khyathi Chandu, Yuchen Lin, Jack Hessel, Ilker
Yildirim, Yejin Choi | L3GO: Language Agents with Chain-of-3D-Thoughts for Generating
Unconventional Objects | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Diffusion-based image generation models such as DALL-E 3 and Stable
Diffusion-XL demonstrate remarkable capabilities in generating images with
realistic and unique compositions. Yet, these models are not robust in
precisely reasoning about physical and spatial configurations of objects,
especially when instructed with unconventional, thereby out-of-distribution
descriptions, such as "a chair with five legs". In this paper, we propose a
language agent with chain-of-3D-thoughts (L3GO), an inference-time approach
that can reason about part-based 3D mesh generation of unconventional objects
that current data-driven diffusion models struggle with. More concretely, we
use large language models as agents to compose a desired object via
trial-and-error within the 3D simulation environment. To facilitate our
investigation, we develop a new benchmark, Unconventionally Feasible Objects
(UFO), as well as SimpleBlenv, a wrapper environment built on top of Blender
where language agents can build and compose atomic building blocks via API
calls. Human and automatic GPT-4V evaluations show that our approach surpasses
the standard GPT-4 and other language agents (e.g., ReAct and Reflexion) for 3D
mesh generation on ShapeNet. Moreover, when tested on our UFO benchmark, our
approach outperforms other state-of-the-art text-to-2D image and text-to-3D
models based on human evaluation.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 09:51:05 GMT"
}
] | 1,707,955,200,000 | [
[
"Yamada",
"Yutaro",
""
],
[
"Chandu",
"Khyathi",
""
],
[
"Lin",
"Yuchen",
""
],
[
"Hessel",
"Jack",
""
],
[
"Yildirim",
"Ilker",
""
],
[
"Choi",
"Yejin",
""
]
] |
2402.09085 | Oliver Broadrick | Oliver Broadrick, Honghua Zhang, Guy Van den Broeck | Polynomial Semantics of Tractable Probabilistic Circuits | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Probabilistic circuits compute multilinear polynomials that represent
multivariate probability distributions. They are tractable models that support
efficient marginal inference. However, various polynomial semantics have been
considered in the literature (e.g., network polynomials, likelihood
polynomials, generating functions, and Fourier transforms). The relationships
between circuit representations of these polynomial encodings of distributions
is largely unknown. In this paper, we prove that for distributions over binary
variables, each of these probabilistic circuit models is equivalent in the
sense that any circuit for one of them can be transformed into a circuit for
any of the others with only a polynomial increase in size. They are therefore
all tractable for marginal inference on the same class of distributions.
Finally, we explore the natural extension of one such polynomial semantics,
called probabilistic generating circuits, to categorical random variables, and
establish that inference becomes #P-hard.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 11:02:04 GMT"
},
{
"version": "v2",
"created": "Sun, 28 Apr 2024 19:34:38 GMT"
}
] | 1,714,435,200,000 | [
[
"Broadrick",
"Oliver",
""
],
[
"Zhang",
"Honghua",
""
],
[
"Broeck",
"Guy Van den",
""
]
] |
2402.09099 | Xiongye Xiao | Xiongye Xiao, Chenyu Zhou, Heng Ping, Defu Cao, Yaxing Li, Yizhuo
Zhou, Shixuan Li, Paul Bogdan | Exploring Neuron Interactions and Emergence in LLMs: From the
Multifractal Analysis Perspective | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Prior studies on the emergence in large models have primarily focused on how
the functional capabilities of large language models (LLMs) scale with model
size. Our research, however, transcends this traditional paradigm, aiming to
deepen our understanding of the emergence within LLMs by placing a special
emphasis not just on the model size but more significantly on the complex
behavior of neuron interactions during the training process. By introducing the
concepts of "self-organization" and "multifractal analysis," we explore how
neuron interactions dynamically evolve during training, leading to "emergence,"
mirroring the phenomenon in natural systems where simple micro-level
interactions give rise to complex macro-level behaviors. To quantitatively
analyze the continuously evolving interactions among neurons in large models
during training, we propose the Neuron-based Multifractal Analysis (NeuroMFA).
Utilizing NeuroMFA, we conduct a comprehensive examination of the emergent
behavior in LLMs through the lens of both model size and training process,
paving new avenues for research into the emergence in large models.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 11:20:09 GMT"
},
{
"version": "v2",
"created": "Mon, 4 Mar 2024 11:22:38 GMT"
},
{
"version": "v3",
"created": "Tue, 5 Mar 2024 10:44:36 GMT"
},
{
"version": "v4",
"created": "Thu, 21 Mar 2024 05:33:23 GMT"
}
] | 1,711,065,600,000 | [
[
"Xiao",
"Xiongye",
""
],
[
"Zhou",
"Chenyu",
""
],
[
"Ping",
"Heng",
""
],
[
"Cao",
"Defu",
""
],
[
"Li",
"Yaxing",
""
],
[
"Zhou",
"Yizhuo",
""
],
[
"Li",
"Shixuan",
""
],
[
"Bogdan",
"Paul",
""
]
] |
2402.09147 | Teddy Ferdinan | Teddy Ferdinan, Jan Koco\'n, Przemys{\l}aw Kazienko | Into the Unknown: Self-Learning Large Language Models | 16 pages, 12 figures, 4 tables, submitted to ACL SRW 2024 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We address the main problem of self-learning LLM: the question of what to
learn. We propose a self-learning LLM framework that enables an LLM to
independently learn previously unknown knowledge through selfassessment of
their own hallucinations. Using the hallucination score, we introduce a new
concept of Points in the Unknown (PiUs), along with one extrinsic and three
intrinsic methods for automatic PiUs identification. It facilitates the
creation of a self-learning loop that focuses exclusively on the knowledge gap
in Points in the Unknown, resulting in a reduced hallucination score. We also
developed evaluation metrics for gauging an LLM's self-learning capability. Our
experiments revealed that 7B-Mistral models that have been finetuned or aligned
and RWKV5-Eagle are capable of self-learning considerably well. Our
self-learning concept allows more efficient LLM updates and opens new
perspectives for knowledge exchange. It may also increase public trust in AI.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 12:56:58 GMT"
},
{
"version": "v2",
"created": "Tue, 4 Jun 2024 12:44:46 GMT"
}
] | 1,717,545,600,000 | [
[
"Ferdinan",
"Teddy",
""
],
[
"Kocoń",
"Jan",
""
],
[
"Kazienko",
"Przemysław",
""
]
] |
2402.09266 | Andres Molares-Ulloa | Andres Molares-Ulloa, Enrique Fernandez-Blanco, Alejandro Pazos and
Daniel Rivero | Machine Learning in management of precautionary closures caused by
lipophilic biotoxins | null | Computers and Electronics in Agriculture, 197, 106956. (2022) | 10.1016/j.compag.2022.106956 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Mussel farming is one of the most important aquaculture industries. The main
risk to mussel farming is harmful algal blooms (HABs), which pose a risk to
human consumption. In Galicia, the Spanish main producer of cultivated mussels,
the opening and closing of the production areas is controlled by a monitoring
program. In addition to the closures resulting from the presence of toxicity
exceeding the legal threshold, in the absence of a confirmatory sampling and
the existence of risk factors, precautionary closures may be applied. These
decisions are made by experts without the support or formalisation of the
experience on which they are based. Therefore, this work proposes a predictive
model capable of supporting the application of precautionary closures.
Achieving sensitivity, accuracy and kappa index values of 97.34%, 91.83% and
0.75 respectively, the kNN algorithm has provided the best results. This allows
the creation of a system capable of helping in complex situations where
forecast errors are more common.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 15:51:58 GMT"
}
] | 1,707,955,200,000 | [
[
"Molares-Ulloa",
"Andres",
""
],
[
"Fernandez-Blanco",
"Enrique",
""
],
[
"Pazos",
"Alejandro",
""
],
[
"Rivero",
"Daniel",
""
]
] |
2402.09334 | Maryam Amirizaniani | Maryam Amirizaniani, Tanya Roosta, Aman Chadha, Chirag Shah | AuditLLM: A Tool for Auditing Large Language Models Using Multiprobe
Approach | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As Large Language Models (LLMs) gain wider adoption in various contexts, it
becomes crucial to ensure they are reasonably safe, consistent, and reliable
for an application at hand. This may require probing or auditing them. Probing
LLMs with varied iterations of a single question could reveal potential
inconsistencies in their knowledge or functionality. However, a tool for
performing such audits with simple workflow and low technical threshold is
lacking. In this demo, we introduce "AuditLLM," a novel tool designed to
evaluate the performance of various LLMs in a methodical way. AuditLLM's core
functionality lies in its ability to test a given LLM by auditing it using
multiple probes generated from a single question, thereby identifying any
inconsistencies in the model's understanding or operation. A reasonably robust,
reliable, and consistent LLM should output semantically similar responses for a
question asked differently or by different people. Based on this assumption,
AuditLLM produces easily interpretable results regarding the LLM's
consistencies from a single question that the user enters. A certain level of
inconsistency has been shown to be an indicator of potential bias,
hallucinations, and other issues. One could then use the output of AuditLLM to
further investigate issues with the aforementioned LLM. To facilitate
demonstration and practical uses, AuditLLM offers two key modes: (1) Live mode
which allows instant auditing of LLMs by analyzing responses to real-time
queries; (2) Batch mode which facilitates comprehensive LLM auditing by
processing multiple queries at once for in-depth analysis. This tool is
beneficial for both researchers and general users, as it enhances our
understanding of LLMs' capabilities in generating responses, using a
standardized auditing platform.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 17:31:04 GMT"
}
] | 1,707,955,200,000 | [
[
"Amirizaniani",
"Maryam",
""
],
[
"Roosta",
"Tanya",
""
],
[
"Chadha",
"Aman",
""
],
[
"Shah",
"Chirag",
""
]
] |
2402.09346 | Maryam Amirizaniani | Maryam Amirizaniani, Jihan Yao, Adrian Lavergne, Elizabeth Snell
Okada, Aman Chadha, Tanya Roosta, Chirag Shah | LLMAuditor: A Framework for Auditing Large Language Models Using
Human-in-the-Loop | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | As Large Language Models (LLMs) become more pervasive across various users
and scenarios, identifying potential issues when using these models becomes
essential. Examples of such issues include: bias, inconsistencies, and
hallucination. Although auditing the LLM for these problems is often warranted,
such a process is neither easy nor accessible for most. An effective method is
to probe the LLM using different versions of the same question. This could
expose inconsistencies in its knowledge or operation, indicating potential for
bias or hallucination. However, to operationalize this auditing method at
scale, we need an approach to create those probes reliably and automatically.
In this paper we propose the LLMAuditor framework which is an automatic, and
scalable solution, where one uses a different LLM along with human-in-the-loop
(HIL). This approach offers verifiability and transparency, while avoiding
circular reliance on the same LLM, and increasing scientific rigor and
generalizability. Specifically, LLMAuditor includes two phases of verification
using humans: standardized evaluation criteria to verify responses, and a
structured prompt template to generate desired probes. A case study using
questions from the TruthfulQA dataset demonstrates that we can generate a
reliable set of probes from one LLM that can be used to audit inconsistencies
in a different LLM. This process is enhanced by our structured prompt template
with HIL, which not only boosts the reliability of our approach in auditing but
also yields the delivery of less hallucinated results. The novelty of our
research stems from the development of a comprehensive, general-purpose
framework that includes a HIL verified prompt template for auditing responses
generated by LLMs.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 17:49:31 GMT"
},
{
"version": "v2",
"created": "Fri, 16 Feb 2024 16:58:20 GMT"
},
{
"version": "v3",
"created": "Wed, 22 May 2024 17:17:03 GMT"
}
] | 1,716,508,800,000 | [
[
"Amirizaniani",
"Maryam",
""
],
[
"Yao",
"Jihan",
""
],
[
"Lavergne",
"Adrian",
""
],
[
"Okada",
"Elizabeth Snell",
""
],
[
"Chadha",
"Aman",
""
],
[
"Roosta",
"Tanya",
""
],
[
"Shah",
"Chirag",
""
]
] |
2402.09388 | Harrison Delecki | Harrison Delecki, Marcell Vazquez-Chanlatte, Esen Yel, Kyle Wray,
Tomer Arnon, Stefan Witwicki, Mykel J. Kochenderfer | Entropy-regularized Point-based Value Iteration | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model-based planners for partially observable problems must accommodate both
model uncertainty during planning and goal uncertainty during objective
inference. However, model-based planners may be brittle under these types of
uncertainty because they rely on an exact model and tend to commit to a single
optimal behavior. Inspired by results in the model-free setting, we propose an
entropy-regularized model-based planner for partially observable problems.
Entropy regularization promotes policy robustness for planning and objective
inference by encouraging policies to be no more committed to a single action
than necessary. We evaluate the robustness and objective inference performance
of entropy-regularized policies in three problem domains. Our results show that
entropy-regularized policies outperform non-entropy-regularized baselines in
terms of higher expected returns under modeling errors and higher accuracy
during objective inference.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 18:37:47 GMT"
}
] | 1,707,955,200,000 | [
[
"Delecki",
"Harrison",
""
],
[
"Vazquez-Chanlatte",
"Marcell",
""
],
[
"Yel",
"Esen",
""
],
[
"Wray",
"Kyle",
""
],
[
"Arnon",
"Tomer",
""
],
[
"Witwicki",
"Stefan",
""
],
[
"Kochenderfer",
"Mykel J.",
""
]
] |
2402.09413 | Joseph Y. Halpern | Joseph Y. Halpern | Mathematical Explanations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A definition of what counts as an explanation of mathematical statement, and
when one explanation is better than another, is given. Since all mathematical
facts must be true in all causal models, and hence known by an agent,
mathematical facts cannot be part of an explanation (under the standard notion
of explanation). This problem is solved using impossible possible worlds.
| [
{
"version": "v1",
"created": "Sun, 31 Dec 2023 17:07:28 GMT"
}
] | 1,708,041,600,000 | [
[
"Halpern",
"Joseph Y.",
""
]
] |
2402.09498 | Jos\'e Alberto Ben\'itez-Andrades Ph.D. | Jos\'e Alberto Ben\'itez-Andrades, Mar\'ia Teresa Garc\'ia-Ord\'as,
Mar\'ia \'Alvarez-Gonz\'alez, Raquel Leir\'os-Rodr\'iguez and Ana F L\'opez
Rodr\'iguez | Detection of the most influential variables for preventing postpartum
urinary incontinence using machine learning techniques | null | Digital Health, Volume 8, 2022, 20552076221111289 | 10.1177/20552076221111289 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Background: Postpartum urinary incontinence (PUI) is a common issue among
postnatal women. Previous studies identified potential related variables, but
lacked analysis on certain intrinsic and extrinsic patient variables during
pregnancy.
Objective: The study aims to evaluate the most influential variables in PUI
using machine learning, focusing on intrinsic, extrinsic, and combined variable
groups.
Methods: Data from 93 pregnant women were analyzed using machine learning and
oversampling techniques. Four key variables were predicted: occurrence,
frequency, intensity of urinary incontinence, and stress urinary incontinence.
Results: Models using extrinsic variables were most accurate, with 70%
accuracy for urinary incontinence, 77% for frequency, 71% for intensity, and
93% for stress urinary incontinence.
Conclusions: The study highlights extrinsic variables as significant
predictors of PUI issues. This suggests that PUI prevention might be achievable
through healthy habits during pregnancy, although further research is needed
for confirmation.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 16:45:10 GMT"
}
] | 1,708,041,600,000 | [
[
"Benítez-Andrades",
"José Alberto",
""
],
[
"García-Ordás",
"María Teresa",
""
],
[
"Álvarez-González",
"María",
""
],
[
"Leirós-Rodríguez",
"Raquel",
""
],
[
"Rodríguez",
"Ana F López",
""
]
] |
2402.09565 | Linfeng Cao | Linfeng Cao, Haoran Deng, Yang Yang, Chunping Wang, Lei Chen | Graph-Skeleton: ~1% Nodes are Sufficient to Represent Billion-Scale
Graph | 21 pages, 11 figures, In Proceedings of the ACM Web Conference 2024
(WWW'24) | null | 10.1145/3589334.3645452 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to the ubiquity of graph data on the web, web graph mining has become a
hot research spot. Nonetheless, the prevalence of large-scale web graphs in
real applications poses significant challenges to storage, computational
capacity and graph model design. Despite numerous studies to enhance the
scalability of graph models, a noticeable gap remains between academic research
and practical web graph mining applications. One major cause is that in most
industrial scenarios, only a small part of nodes in a web graph are actually
required to be analyzed, where we term these nodes as target nodes, while
others as background nodes. In this paper, we argue that properly fetching and
condensing the background nodes from massive web graph data might be a more
economical shortcut to tackle the obstacles fundamentally. To this end, we make
the first attempt to study the problem of massive background nodes compression
for target nodes classification. Through extensive experiments, we reveal two
critical roles played by the background nodes in target node classification:
enhancing structural connectivity between target nodes, and feature correlation
with target nodes. Followingthis, we propose a novel Graph-Skeleton1 model,
which properly fetches the background nodes, and further condenses the semantic
and topological information of background nodes within similar
target-background local structures. Extensive experiments on various web graph
datasets demonstrate the effectiveness and efficiency of the proposed method.
In particular, for MAG240M dataset with 0.24 billion nodes, our generated
skeleton graph achieves highly comparable performance while only containing
1.8% nodes of the original graph.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 20:33:11 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Mar 2024 22:22:33 GMT"
}
] | 1,709,856,000,000 | [
[
"Cao",
"Linfeng",
""
],
[
"Deng",
"Haoran",
""
],
[
"Yang",
"Yang",
""
],
[
"Wang",
"Chunping",
""
],
[
"Chen",
"Lei",
""
]
] |
2402.09656 | Wanli Yang | Wanli Yang, Fei Sun, Xinyu Ma, Xun Liu, Dawei Yin, Xueqi Cheng | The Butterfly Effect of Model Editing: Few Edits Can Trigger Large
Language Models Collapse | Accepted at Findings of ACL 2024 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although model editing has shown promise in revising knowledge in Large
Language Models (LLMs), its impact on the inherent capabilities of LLMs is
often overlooked. In this work, we reveal a critical phenomenon: even a single
edit can trigger model collapse, manifesting as significant performance
degradation in various benchmark tasks. However, benchmarking LLMs after each
edit, while necessary to prevent such collapses, is impractically
time-consuming and resource-intensive. To mitigate this, we propose using
perplexity as a surrogate metric, validated by extensive experiments
demonstrating changes in an edited model's perplexity are strongly correlated
with its downstream task performances. We further conduct an in-depth study on
sequential editing, a practical setting for real-world scenarios, across
various editing methods and LLMs, focusing on hard cases from our previous
single edit studies. The results indicate that nearly all examined editing
methods result in model collapse after only few edits. To facilitate further
research, we have utilized GPT-3.5 to develop a new dataset, HardEdit, based on
those hard cases. This dataset aims to establish the foundation for pioneering
research in reliable model editing and the mechanisms underlying
editing-induced model collapse. We hope this work can draw the community's
attention to the potential risks inherent in model editing practices.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 01:50:38 GMT"
},
{
"version": "v2",
"created": "Sun, 18 Feb 2024 08:00:46 GMT"
},
{
"version": "v3",
"created": "Thu, 14 Mar 2024 11:18:21 GMT"
},
{
"version": "v4",
"created": "Wed, 5 Jun 2024 09:43:00 GMT"
}
] | 1,717,632,000,000 | [
[
"Yang",
"Wanli",
""
],
[
"Sun",
"Fei",
""
],
[
"Ma",
"Xinyu",
""
],
[
"Liu",
"Xun",
""
],
[
"Yin",
"Dawei",
""
],
[
"Cheng",
"Xueqi",
""
]
] |
2402.09734 | Paulo Garcia | Paulo Garcia | Agents Need Not Know Their Purpose | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Ensuring artificial intelligence behaves in such a way that is aligned with
human values is commonly referred to as the alignment challenge. Prior work has
shown that rational agents, behaving in such a way that maximizes a utility
function, will inevitably behave in such a way that is not aligned with human
values, especially as their level of intelligence goes up. Prior work has also
shown that there is no "one true utility function"; solutions must include a
more holistic approach to alignment. This paper describes oblivious agents:
agents that are architected in such a way that their effective utility function
is an aggregation of a known and hidden sub-functions. The hidden component, to
be maximized, is internally implemented as a black box, preventing the agent
from examining it. The known component, to be minimized, is knowledge of the
hidden sub-function. Architectural constraints further influence how agent
actions can evolve its internal environment model. We show that an oblivious
agent, behaving rationally, constructs an internal approximation of designers'
intentions (i.e., infers alignment), and, as a consequence of its architecture
and effective utility function, behaves in such a way that maximizes alignment;
i.e., maximizing the approximated intention function. We show that,
paradoxically, it does this for whatever utility function is used as the hidden
component and, in contrast with extant techniques, chances of alignment
actually improve as agent intelligence grows.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 06:15:46 GMT"
}
] | 1,708,041,600,000 | [
[
"Garcia",
"Paulo",
""
]
] |
2402.09764 | Dexun Li | Dexun Li, Cong Zhang, Kuicai Dong, Derrick Goh Xin Deik, Ruiming Tang,
Yong Liu | Aligning Crowd Feedback via Distributional Preference Reward Modeling | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep Reinforcement Learning is widely used for aligning Large Language Models
(LLM) with human preference. However, the conventional reward modelling is
predominantly dependent on human annotations provided by a select cohort of
individuals. Such dependence may unintentionally result in skewed models that
reflect the inclinations of these annotators, thereby failing to adequately
represent the wider population's expectations. We propose the Distributional
Preference Reward Model (DPRM), a simple yet effective framework to align large
language models with diverse human preferences. To this end, we characterize
multiple preferences by a categorical distribution and introduce a Bayesian
updater to accommodate shifted or new preferences. On top of that, we design an
optimal-transportation-based loss to calibrate DPRM to align with the
preference distribution. Finally, the expected reward is utilized to fine-tune
an LLM policy to generate responses favoured by the population. Our experiments
show that DPRM significantly enhances the alignment of LLMs with population
preference, yielding more accurate, unbiased, and contextually appropriate
responses.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 07:29:43 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Feb 2024 07:56:28 GMT"
},
{
"version": "v3",
"created": "Thu, 30 May 2024 15:39:17 GMT"
}
] | 1,717,113,600,000 | [
[
"Li",
"Dexun",
""
],
[
"Zhang",
"Cong",
""
],
[
"Dong",
"Kuicai",
""
],
[
"Deik",
"Derrick Goh Xin",
""
],
[
"Tang",
"Ruiming",
""
],
[
"Liu",
"Yong",
""
]
] |
2402.09765 | Zangir Iklassov | Zangir Iklassov and Ikboljon Sobirov and Ruben Solozabal and Martin
Takac | Reinforcement Learning for Solving Stochastic Vehicle Routing Problem
with Time Windows | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces a reinforcement learning approach to optimize the
Stochastic Vehicle Routing Problem with Time Windows (SVRP), focusing on
reducing travel costs in goods delivery. We develop a novel SVRP formulation
that accounts for uncertain travel costs and demands, alongside specific
customer time windows. An attention-based neural network trained through
reinforcement learning is employed to minimize routing costs. Our approach
addresses a gap in SVRP research, which traditionally relies on heuristic
methods, by leveraging machine learning. The model outperforms the Ant-Colony
Optimization algorithm, achieving a 1.73% reduction in travel costs. It
uniquely integrates external information, demonstrating robustness in diverse
environments, making it a valuable benchmark for future SVRP studies and
industry application.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 07:35:29 GMT"
}
] | 1,708,041,600,000 | [
[
"Iklassov",
"Zangir",
""
],
[
"Sobirov",
"Ikboljon",
""
],
[
"Solozabal",
"Ruben",
""
],
[
"Takac",
"Martin",
""
]
] |
2402.09769 | Ayon Borthakur | Aditya Somasundaram, Pushkal Mishra, Ayon Borthakur | Representation Learning Using a Single Forward Pass | Under review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a neuroscience-inspired Solo Pass Embedded Learning Algorithm
(SPELA). SPELA is a prime candidate for training and inference applications in
Edge AI devices. At the same time, SPELA can optimally cater to the need for a
framework to study perceptual representation learning and formation. SPELA has
distinctive features such as neural priors (in the form of embedded vectors),
no weight transport, no update locking of weights, complete local Hebbian
learning, single forward pass with no storage of activations, and single weight
update per sample. Juxtaposed with traditional approaches, SPELA operates
without the need for backpropagation. We show that our algorithm can perform
nonlinear classification on a noisy boolean operation dataset. Additionally, we
exhibit high performance using SPELA across MNIST, KMNIST, and Fashion MNIST.
Lastly, we show the few-shot and 1-epoch learning capabilities of SPELA on
MNIST, KMNIST, and Fashion MNIST, where it consistently outperforms
backpropagation.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 07:47:10 GMT"
}
] | 1,708,041,600,000 | [
[
"Somasundaram",
"Aditya",
""
],
[
"Mishra",
"Pushkal",
""
],
[
"Borthakur",
"Ayon",
""
]
] |
2402.09836 | Chenyang Shao | Chenyang Shao, Fengli Xu, Bingbing Fan, Jingtao Ding, Yuan Yuan, Meng
Wang, Yong Li | Chain-of-Planned-Behaviour Workflow Elicits Few-Shot Mobility Generation
in LLMs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The powerful reasoning capabilities of large language models (LLMs) have
brought revolutionary changes to many fields, but their performance in human
behaviour generation has not yet been extensively explored. This gap likely
emerges because the internal processes governing behavioral intentions cannot
be solely explained by abstract reasoning. Instead, they are also influenced by
a multitude of factors, including social norms and personal preference.
Inspired by the Theory of Planned Behaviour (TPB), we develop a LLM workflow
named Chain-of-Planned Behaviour (CoPB) for mobility behaviour generation,
which reflects the important spatio-temporal dynamics of human activities.
Through exploiting the cognitive structures of attitude, subjective norms, and
perceived behaviour control in TPB, CoPB significantly enhance the ability of
LLMs to reason the intention of next movement. Specifically, CoPB substantially
reduces the error rate of mobility intention generation from 57.8% to 19.4%. To
improve the scalability of the proposed CoPB workflow, we further explore the
synergy between LLMs and mechanistic models. We find mechanistic mobility
models, such as gravity model, can effectively map mobility intentions to
physical mobility behaviours. The strategy of integrating CoPB with gravity
model can reduce the token cost by 97.7% and achieve better performance
simultaneously. Besides, the proposed CoPB workflow can facilitate GPT-4-turbo
to automatically generate high quality labels for mobility behavior reasoning.
We show such labels can be leveraged to fine-tune the smaller-scale, open
source LLaMA 3-8B, which significantly reduces usage costs without sacrificing
the quality of the generated behaviours.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 09:58:23 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jun 2024 09:27:42 GMT"
}
] | 1,717,632,000,000 | [
[
"Shao",
"Chenyang",
""
],
[
"Xu",
"Fengli",
""
],
[
"Fan",
"Bingbing",
""
],
[
"Ding",
"Jingtao",
""
],
[
"Yuan",
"Yuan",
""
],
[
"Wang",
"Meng",
""
],
[
"Li",
"Yong",
""
]
] |
2402.09844 | Quentin Gallou\'edec | Quentin Gallou\'edec and Edward Beeching and Cl\'ement Romac and
Emmanuel Dellandr\'ea | Jack of All Trades, Master of Some, a Multi-Purpose Transformer Agent | Under review | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The search for a general model that can operate seamlessly across multiple
domains remains a key goal in machine learning research. The prevailing
methodology in Reinforcement Learning (RL) typically limits models to a single
task within a unimodal framework, a limitation that contrasts with the broader
vision of a versatile, multi-domain model. In this paper, we present Jack of
All Trades (JAT), a transformer-based model with a unique design optimized for
handling sequential decision-making tasks and multimodal data types. The JAT
model demonstrates its robust capabilities and versatility by achieving strong
performance on very different RL benchmarks, along with promising results on
Computer Vision (CV) and Natural Language Processing (NLP) tasks, all using a
single set of weights. The JAT model marks a significant step towards more
general, cross-domain AI model design, and notably, it is the first model of
its kind to be fully open-sourced (see https://huggingface.co/jat-project/jat),
including a pioneering general-purpose dataset.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 10:01:55 GMT"
},
{
"version": "v2",
"created": "Mon, 22 Apr 2024 09:47:31 GMT"
}
] | 1,713,830,400,000 | [
[
"Gallouédec",
"Quentin",
""
],
[
"Beeching",
"Edward",
""
],
[
"Romac",
"Clément",
""
],
[
"Dellandréa",
"Emmanuel",
""
]
] |
2402.09877 | Alberto Pozanco | Alberto Pozanco, Daniel Borrajo, Manuela Veloso | On Computing Plans with Uniform Action Costs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many real-world planning applications, agents might be interested in
finding plans whose actions have costs that are as uniform as possible. Such
plans provide agents with a sense of stability and predictability, which are
key features when humans are the agents executing plans suggested by planning
tools. This paper adapts three uniformity metrics to automated planning, and
introduce planning-based compilations that allow to lexicographically optimize
sum of action costs and action costs uniformity. Experimental results both in
well-known and novel planning benchmarks show that the reformulated tasks can
be effectively solved in practice to generate uniform plans.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 11:00:28 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Apr 2024 13:20:49 GMT"
},
{
"version": "v3",
"created": "Fri, 24 May 2024 09:19:23 GMT"
}
] | 1,716,768,000,000 | [
[
"Pozanco",
"Alberto",
""
],
[
"Borrajo",
"Daniel",
""
],
[
"Veloso",
"Manuela",
""
]
] |
2402.09919 | Katarzyna Micha{\l}owska | Katarzyna Micha{\l}owska, Helga Margrete Bodahl Holmestad, Signe
Riemer-S{\o}rensen | Road Graph Generator: Mapping roads at construction sites from GPS data | 18 pages, 4 figures, 3 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We propose a new method for inferring roads from GPS trajectories to map
construction sites. This task presents a unique challenge due to the erratic
and non-standard movement patterns of construction machinery, which
significantly diverge from typical vehicular traffic on established roads. Our
proposed method first identifies intersections in the road network that serve
as critical decision points, and then connects them with edges to produce a
graph, which can subsequently be used for planning and task-allocation. We
demonstrate the approach by mapping roads at a real-life construction site in
Norway. The method is validated on four increasingly complex segments of the
map. In our tests, the method achieved perfect accuracy in detecting
intersections and inferring roads in data with no or low noise, while its
performance was reduced in map areas with significant noise and consistently
missing GPS updates.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 12:53:25 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Apr 2024 11:41:21 GMT"
}
] | 1,712,707,200,000 | [
[
"Michałowska",
"Katarzyna",
""
],
[
"Holmestad",
"Helga Margrete Bodahl",
""
],
[
"Riemer-Sørensen",
"Signe",
""
]
] |
2402.10011 | Cong Liu | Cong Liu, David Ruhe, Floor Eijkelboom, Patrick Forr\'e | Clifford Group Equivariant Simplicial Message Passing Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Clifford Group Equivariant Simplicial Message Passing Networks,
a method for steerable E(n)-equivariant message passing on simplicial
complexes. Our method integrates the expressivity of Clifford group-equivariant
layers with simplicial message passing, which is topologically more intricate
than regular graph message passing. Clifford algebras include higher-order
objects such as bivectors and trivectors, which express geometric features
(e.g., areas, volumes) derived from vectors. Using this knowledge, we represent
simplex features through geometric products of their vertices. To achieve
efficient simplicial message passing, we share the parameters of the message
network across different dimensions. Additionally, we restrict the final
message to an aggregation of the incoming messages from different dimensions,
leading to what we term shared simplicial message passing. Experimental results
show that our method is able to outperform both equivariant and simplicial
graph neural networks on a variety of geometric tasks.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 15:18:53 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Feb 2024 17:12:49 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Mar 2024 12:38:09 GMT"
}
] | 1,710,288,000,000 | [
[
"Liu",
"Cong",
""
],
[
"Ruhe",
"David",
""
],
[
"Eijkelboom",
"Floor",
""
],
[
"Forré",
"Patrick",
""
]
] |
2402.10083 | Yuhe Ke | Ting Fang Tan, Kabilan Elangovan, Liyuan Jin, Yao Jie, Li Yong, Joshua
Lim, Stanley Poh, Wei Yan Ng, Daniel Lim, Yuhe Ke, Nan Liu, Daniel Shu Wei
Ting | Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots
in Ophthalmology and LLM-based evaluation using GPT-4 | 13 Pages, 1 Figure, 8 Tables | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Purpose: To assess the alignment of GPT-4-based evaluation to human clinician
experts, for the evaluation of responses to ophthalmology-related patient
queries generated by fine-tuned LLM chatbots. Methods: 400 ophthalmology
questions and paired answers were created by ophthalmologists to represent
commonly asked patient questions, divided into fine-tuning (368; 92%), and
testing (40; 8%). We find-tuned 5 different LLMs, including LLAMA2-7b,
LLAMA2-7b-Chat, LLAMA2-13b, and LLAMA2-13b-Chat. For the testing dataset,
additional 8 glaucoma QnA pairs were included. 200 responses to the testing
dataset were generated by 5 fine-tuned LLMs for evaluation. A customized
clinical evaluation rubric was used to guide GPT-4 evaluation, grounded on
clinical accuracy, relevance, patient safety, and ease of understanding. GPT-4
evaluation was then compared against ranking by 5 clinicians for clinical
alignment. Results: Among all fine-tuned LLMs, GPT-3.5 scored the highest
(87.1%), followed by LLAMA2-13b (80.9%), LLAMA2-13b-chat (75.5%),
LLAMA2-7b-Chat (70%) and LLAMA2-7b (68.8%) based on the GPT-4 evaluation. GPT-4
evaluation demonstrated significant agreement with human clinician rankings,
with Spearman and Kendall Tau correlation coefficients of 0.90 and 0.80
respectively; while correlation based on Cohen Kappa was more modest at 0.50.
Notably, qualitative analysis and the glaucoma sub-analysis revealed clinical
inaccuracies in the LLM-generated responses, which were appropriately
identified by the GPT-4 evaluation. Conclusion: The notable clinical alignment
of GPT-4 evaluation highlighted its potential to streamline the clinical
evaluation of LLM chatbot responses to healthcare-related queries. By
complementing the existing clinician-dependent manual grading, this efficient
and automated evaluation could assist the validation of future developments in
LLM applications for healthcare.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 16:43:41 GMT"
}
] | 1,708,041,600,000 | [
[
"Tan",
"Ting Fang",
""
],
[
"Elangovan",
"Kabilan",
""
],
[
"Jin",
"Liyuan",
""
],
[
"Jie",
"Yao",
""
],
[
"Yong",
"Li",
""
],
[
"Lim",
"Joshua",
""
],
[
"Poh",
"Stanley",
""
],
[
"Ng",
"Wei Yan",
""
],
[
"Lim",
"Daniel",
""
],
[
"Ke",
"Yuhe",
""
],
[
"Liu",
"Nan",
""
],
[
"Ting",
"Daniel Shu Wei",
""
]
] |
2402.10133 | Davor Hafnar | Davor Hafnar (1), Jure Dem\v{s}ar (1 and 2) ((1) Faculty of Computer
and Information Science, University of Ljubljana (2) Department of
Psychology, Faculty of Arts, University of Ljubljana) | Zero-Shot Reasoning: Personalized Content Generation Without the Cold
Start Problem | 9 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedural content generation uses algorithmic techniques to create large
amounts of new content for games at much lower production costs. In newer
approaches, procedural content generation utilizes machine learning. However,
these methods usually require expensive collection of large amounts of data, as
well as the development and training of fairly complex learning models, which
can be both extremely time-consuming and expensive. The core of our research is
to explore whether we can lower the barrier to the use of personalized
procedural content generation through a more practical and generalizable
approach with large language models. Matching game content with player
preferences benefits both players, who enjoy the game more, and developers, who
increasingly depend on players enjoying the game before being able to monetize
it. Therefore, this paper presents a novel approach to achieving
personalization by using large language models to propose levels based on the
gameplay data continuously collected from individual players. We compared the
levels generated using our approach with levels generated with more traditional
procedural generation techniques. Our easily reproducible method has proven
viable in a production setting and outperformed levels generated by traditional
methods in the probability that a player will not quit the game mid-level.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 17:37:25 GMT"
}
] | 1,708,041,600,000 | [
[
"Hafnar",
"Davor",
"",
"1 and 2"
],
[
"Demšar",
"Jure",
"",
"1 and 2"
]
] |
2402.10290 | Jonathan Dodge | Sujay Nagesh Koujalgi and Jonathan Dodge | Experiments with Encoding Structured Data for Neural Networks | 18 pages, 8 figures, 2 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The project's aim is to create an AI agent capable of selecting good actions
in a game-playing domain called Battlespace. Sequential domains like
Battlespace are important testbeds for planning problems, as such, the
Department of Defense uses such domains for wargaming exercises. The agents we
developed combine Monte Carlo Tree Search (MCTS) and Deep Q-Network (DQN)
techniques in an effort to navigate the game environment, avoid obstacles,
interact with adversaries, and capture the flag. This paper will focus on the
encoding techniques we explored to present complex structured data stored in a
Python class, a necessary precursor to an agent.
| [
{
"version": "v1",
"created": "Thu, 15 Feb 2024 19:45:15 GMT"
}
] | 1,708,300,800,000 | [
[
"Koujalgi",
"Sujay Nagesh",
""
],
[
"Dodge",
"Jonathan",
""
]
] |
2402.10705 | Yiwen Sun | Yiwen Sun, Xianyin Zhang, Shiyu Huang, Shaowei Cai, BingZhen Zhang, Ke
Wei | AutoSAT: Automatically Optimize SAT Solvers via Large Language Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heuristics are crucial in SAT solvers, but no heuristic rules are suitable
for all SAT problems. Therefore, it is helpful to refine specific heuristics
for specific problems. In this context, we present AutoSAT, a novel framework
for automatically optimizing heuristics in SAT solvers. AutoSAT is based on
Large Language Models (LLMs) which is able to autonomously generate codes,
conduct evaluation, and then utilize feedback to further optimize heuristics,
thereby reducing human intervention and enhancing solver capabilities. AutoSAT
operates on a plug-and-play basis, eliminating the need for extensive
enterprise and model training, and fosters a Multi-Agent-based collaborative
process with fault tolerance to ensure robust heuristic optimization. We
implement AutoSAT on a lightweight Conflict-Driven Clause Learning (CDCL)
solver EasySAT (the volume of EasySAT is about one-fiftieth of the
State-of-the-Art hybrid solver Kissat) and extensive experiments on seven
datasets demonstrate its superior performance. Out of the seven testing
datasets, AutoSAT shows a superior performance to Kissat in two datasets and
displays an overall similar performance in three datasets. Some heuristics
generated by AutoSAT are even counter-intuitive but are very effective.
| [
{
"version": "v1",
"created": "Fri, 16 Feb 2024 14:04:56 GMT"
},
{
"version": "v2",
"created": "Fri, 31 May 2024 11:38:00 GMT"
}
] | 1,717,372,800,000 | [
[
"Sun",
"Yiwen",
""
],
[
"Zhang",
"Xianyin",
""
],
[
"Huang",
"Shiyu",
""
],
[
"Cai",
"Shaowei",
""
],
[
"Zhang",
"BingZhen",
""
],
[
"Wei",
"Ke",
""
]
] |
2402.10726 | Tomas Balyo | Tom\'a\v{s} Balyo, Martin Suda, Luk\'a\v{s} Chrpa, Dominik
\v{S}afr\'anek, Filip Dvo\v{r}\'ak, Roman Bart\'ak, G. Michael Youngblood | Learning Planning Action Models from State Traces | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Previous STRIPS domain model acquisition approaches that learn from state
traces start with the names and parameters of the actions to be learned.
Therefore their only task is to deduce the preconditions and effects of the
given actions. In this work, we explore learning in situations when the
parameters of learned actions are not provided. We define two levels of trace
quality based on which information is provided and present an algorithm for
each. In one level (L1), the states in the traces are labeled with action
names, so we can deduce the number and names of the actions, but we still need
to work out the number and types of parameters. In the other level (L2), the
states are additionally labeled with objects that constitute the parameters of
the corresponding grounded actions. Here we still need to deduce the types of
the parameters in the learned actions. We experimentally evaluate the proposed
algorithms and compare them with the state-of-the-art learning tool FAMA on a
large collection of IPC benchmarks. The evaluation shows that our new
algorithms are faster, can handle larger inputs and provide better results in
terms of learning action models more similar to reference models.
| [
{
"version": "v1",
"created": "Fri, 16 Feb 2024 14:36:58 GMT"
}
] | 1,708,300,800,000 | [
[
"Balyo",
"Tomáš",
""
],
[
"Suda",
"Martin",
""
],
[
"Chrpa",
"Lukáš",
""
],
[
"Šafránek",
"Dominik",
""
],
[
"Dvořák",
"Filip",
""
],
[
"Barták",
"Roman",
""
],
[
"Youngblood",
"G. Michael",
""
]
] |
2402.10762 | Danae Pla Karidi | Christos Fragkathoulas, Vasiliki Papanikou, Danae Pla Karidi,
Evaggelia Pitoura | On Explaining Unfairness: An Overview | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Algorithmic fairness and explainability are foundational elements for
achieving responsible AI. In this paper, we focus on their interplay, a
research area that is recently receiving increasing attention. To this end, we
first present two comprehensive taxonomies, each representing one of the two
complementary fields of study: fairness and explanations. Then, we categorize
explanations for fairness into three types: (a) Explanations to enhance
fairness metrics, (b) Explanations to help us understand the causes of
(un)fairness, and (c) Explanations to assist us in designing methods for
mitigating unfairness. Finally, based on our fairness and explanation
taxonomies, we present undiscovered literature paths revealing gaps that can
serve as valuable insights for future research.
| [
{
"version": "v1",
"created": "Fri, 16 Feb 2024 15:38:00 GMT"
}
] | 1,708,300,800,000 | [
[
"Fragkathoulas",
"Christos",
""
],
[
"Papanikou",
"Vasiliki",
""
],
[
"Karidi",
"Danae Pla",
""
],
[
"Pitoura",
"Evaggelia",
""
]
] |
2402.10967 | Jos\'e Alberto Ben\'itez-Andrades Ph.D. | Jos\'e Alberto Ben\'itez-Andrades, Isa\'ias Garc\'ia-Rodr\'iguez,
Carmen Benavides, H\'ector Alaiz-Moret\'on and Alejandro
Rodr\'iguez-Gonz\'alez | Social network analysis for personalized characterization and risk
assessment of alcohol use disorders in adolescents using semantic
technologies | null | Future Generation Computer Systems, Volume 106, May 2020, Pages
154-170 | 10.1016/j.future.2020.01.002 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Alcohol Use Disorder (AUD) is a major concern for public health organizations
worldwide, especially as regards the adolescent population. The consumption of
alcohol in adolescents is known to be influenced by seeing friends and even
parents drinking alcohol. Building on this fact, a number of studies into
alcohol consumption among adolescents have made use of Social Network Analysis
(SNA) techniques to study the different social networks (peers, friends,
family, etc.) with whom the adolescent is involved. These kinds of studies need
an initial phase of data gathering by means of questionnaires and a subsequent
analysis phase using the SNA techniques. The process involves a number of
manual data handling stages that are time consuming and error-prone. The use of
knowledge engineering techniques (including the construction of a domain
ontology) to represent the information, allows the automation of all the
activities, from the initial data collection to the results of the SNA study.
This paper shows how a knowledge model is constructed, and compares the results
obtained using the traditional method with this, fully automated model,
detailing the main advantages of the latter. In the case of the SNA analysis,
the validity of the results obtained with the knowledge engineering approach
are compared to those obtained manually using the UCINET, Cytoscape, Pajek and
Gephi to test the accuracy of the knowledge model.
| [
{
"version": "v1",
"created": "Wed, 14 Feb 2024 16:09:05 GMT"
}
] | 1,708,387,200,000 | [
[
"Benítez-Andrades",
"José Alberto",
""
],
[
"García-Rodríguez",
"Isaías",
""
],
[
"Benavides",
"Carmen",
""
],
[
"Alaiz-Moretón",
"Héctor",
""
],
[
"Rodríguez-González",
"Alejandro",
""
]
] |
2402.11403 | Liying Han | Liying Han, Mani B. Srivastava | An Empirical Evaluation of Neural and Neuro-symbolic Approaches to
Real-time Multimodal Complex Event Detection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robots and autonomous systems require an understanding of complex events
(CEs) from sensor data to interact with their environments and humans
effectively. Traditional end-to-end neural architectures, despite processing
sensor data efficiently, struggle with long-duration events due to limited
context sizes and reasoning capabilities. Recent advances in neuro-symbolic
methods, which integrate neural and symbolic models leveraging human knowledge,
promise improved performance with less data. This study addresses the gap in
understanding these approaches' effectiveness in complex event detection (CED),
especially in temporal reasoning. We investigate neural and neuro-symbolic
architectures' performance in a multimodal CED task, analyzing IMU and acoustic
data streams to recognize CE patterns. Our methodology includes (i) end-to-end
neural architectures for direct CE detection from sensor embeddings, (ii)
two-stage concept-based neural models mapping sensor embeddings to atomic
events (AEs) before CE detection, and (iii) a neuro-symbolic approach using a
symbolic finite-state machine for CE detection from AEs. Empirically, the
neuro-symbolic architecture significantly surpasses purely neural models,
demonstrating superior performance in CE recognition, even with extensive
training data and ample temporal context for neural approaches.
| [
{
"version": "v1",
"created": "Sat, 17 Feb 2024 23:34:50 GMT"
},
{
"version": "v2",
"created": "Sun, 3 Mar 2024 22:07:50 GMT"
}
] | 1,709,596,800,000 | [
[
"Han",
"Liying",
""
],
[
"Srivastava",
"Mani B.",
""
]
] |
2402.11461 | Tuo Leng | Xiaokai Zhang, Na Zhu, Cheng Qin, Yang Li, Zhenbing Zeng, Tuo Leng | FGeo-HyperGNet: Geometric Problem Solving Integrating Formal Symbolic
System and Hypergraph Neural Network | 13 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Geometric problem solving has always been a long-standing challenge in the
fields of automated reasoning and artificial intelligence. We built a
neural-symbolic system to automatically perform human-like geometric deductive
reasoning. The symbolic part is a formal system built on FormalGeo, which can
automatically perform geomertic relational reasoning and algebraic calculations
and organize the solving process into a solution hypertree with conditions as
hypernodes and theorems as hyperedges. The neural part, called HyperGNet, is a
hypergraph neural network based on the attention mechanism, including a encoder
to effectively encode the structural and semantic information of the hypertree,
and a solver to provide problem-solving guidance. The neural part predicts
theorems according to the hypertree, and the symbolic part applies theorems and
updates the hypertree, thus forming a predict-apply cycle to ultimately achieve
readable and traceable automatic solving of geometric problems. Experiments
demonstrate the correctness and effectiveness of this neural-symbolic
architecture. We achieved a step-wised accuracy of 87.65% and an overall
accuracy of 85.53% on the formalgeo7k datasets.
| [
{
"version": "v1",
"created": "Sun, 18 Feb 2024 05:23:15 GMT"
},
{
"version": "v2",
"created": "Mon, 22 Apr 2024 07:31:15 GMT"
}
] | 1,713,830,400,000 | [
[
"Zhang",
"Xiaokai",
""
],
[
"Zhu",
"Na",
""
],
[
"Qin",
"Cheng",
""
],
[
"Li",
"Yang",
""
],
[
"Zeng",
"Zhenbing",
""
],
[
"Leng",
"Tuo",
""
]
] |
2402.11893 | Xiaowei Yuan | Xiaowei Yuan, Zhao Yang, Yequan Wang, Shengping Liu, Jun Zhao, Kang
Liu | Discerning and Resolving Knowledge Conflicts through Adaptive Decoding
with Contextual Information-Entropy Constraint | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large language models internalize enormous parametric knowledge during
pre-training. Concurrently, realistic applications necessitate external
contextual knowledge to aid models on the underlying tasks. This raises a
crucial dilemma known as knowledge conflicts, where the contextual knowledge
clashes with the However, existing decoding works are specialized in resolving
knowledge conflicts and could inadvertently deteriorate performance in absence
of conflicts. In this paper, we propose an adaptive decoding method, termed as
contextual information-entropy constraint decoding (COIECD), to discern whether
the knowledge conflicts occur and resolve them. It can improve the model's
faithfulness to conflicting context, and simultaneously maintain high
performance among non- Our experiments show that COIECD exhibits strong
performance and robustness over knowledge conflicts in realistic datasets. Code
is available.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 07:10:30 GMT"
}
] | 1,708,387,200,000 | [
[
"Yuan",
"Xiaowei",
""
],
[
"Yang",
"Zhao",
""
],
[
"Wang",
"Yequan",
""
],
[
"Liu",
"Shengping",
""
],
[
"Zhao",
"Jun",
""
],
[
"Liu",
"Kang",
""
]
] |
2402.11901 | Wiktor Piotrowski | Wiktor Piotrowski, Alexandre Perez | Real-World Planning with PDDL+ and Beyond | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Real-world applications of AI Planning often require a highly expressive
modeling language to accurately capture important intricacies of target
systems. Hybrid systems are ubiquitous in the real-world, and PDDL+ is the
standardized modeling language for capturing such systems as planning domains.
PDDL+ enables accurate encoding of mixed discrete-continuous system dynamics,
exogenous activity, and many other interesting features exhibited in realistic
scenarios. However, the uptake in usage of PDDL+ has been slow and
apprehensive, largely due to a general shortage of PDDL+ planning software, and
rigid limitations of the few existing planners. To overcome this chasm, we
present Nyx, a novel PDDL+ planner built to emphasize lightness, simplicity,
and, most importantly, adaptability. The planner is designed to be effortlessly
customizable to expand its capabilities well beyond the scope of PDDL+. As a
result, Nyx can be tailored to virtually any potential real-world application
requiring some form of AI Planning, paving the way for wider adoption of
planning methods for solving real-world problems.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 07:35:49 GMT"
}
] | 1,708,387,200,000 | [
[
"Piotrowski",
"Wiktor",
""
],
[
"Perez",
"Alexandre",
""
]
] |
2402.12074 | Yongquan He | Yongquan He and Peng Zhang and Luchen Liu and Qi Liang and Wenyuan
Zhang and Chuang Zhang | HIP Network: Historical Information Passing Network for Extrapolation
Reasoning on Temporal Knowledge Graph | 7 pages, 3 figures | IJCAI (2021) 1915-1921 | 10.24963/IJCAI.2021/264 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, temporal knowledge graph (TKG) reasoning has received
significant attention. Most existing methods assume that all timestamps and
corresponding graphs are available during training, which makes it difficult to
predict future events. To address this issue, recent works learn to infer
future events based on historical information. However, these methods do not
comprehensively consider the latent patterns behind temporal changes, to pass
historical information selectively, update representations appropriately and
predict events accurately. In this paper, we propose the Historical Information
Passing (HIP) network to predict future events. HIP network passes information
from temporal, structural and repetitive perspectives, which are used to model
the temporal evolution of events, the interactions of events at the same time
step, and the known events respectively. In particular, our method considers
the updating of relation representations and adopts three scoring functions
corresponding to the above dimensions. Experimental results on five benchmark
datasets show the superiority of HIP network, and the significant improvements
on Hits@1 prove that our method can more accurately predict what is going to
happen.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 11:50:30 GMT"
}
] | 1,708,560,000,000 | [
[
"He",
"Yongquan",
""
],
[
"Zhang",
"Peng",
""
],
[
"Liu",
"Luchen",
""
],
[
"Liang",
"Qi",
""
],
[
"Zhang",
"Wenyuan",
""
],
[
"Zhang",
"Chuang",
""
]
] |
2402.12132 | Ruiyi Yang | Ruiyi Yang, Flora D. Salim and Hao Xue | SSTKG: Simple Spatio-Temporal Knowledge Graph for Intepretable and
Versatile Dynamic Information Embedding | for Web conf 2024. 8 pages context | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graphs (KGs) have been increasingly employed for link prediction
and recommendation using real-world datasets. However, the majority of current
methods rely on static data, neglecting the dynamic nature and the hidden
spatio-temporal attributes of real-world scenarios. This often results in
suboptimal predictions and recommendations. Although there are effective
spatio-temporal inference methods, they face challenges such as scalability
with large datasets and inadequate semantic understanding, which impede their
performance. To address these limitations, this paper introduces a novel
framework - Simple Spatio-Temporal Knowledge Graph (SSTKG), for constructing
and exploring spatio-temporal KGs. To integrate spatial and temporal data into
KGs, our framework exploited through a new 3-step embedding method. Output
embeddings can be used for future temporal sequence prediction and spatial
information recommendation, providing valuable insights for various
applications such as retail sales forecasting and traffic volume prediction.
Our framework offers a simple but comprehensive way to understand the
underlying patterns and trends in dynamic KG, thereby enhancing the accuracy of
predictions and the relevance of recommendations. This work paves the way for
more effective utilization of spatio-temporal data in KGs, with potential
impacts across a wide range of sectors.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 13:28:43 GMT"
}
] | 1,708,387,200,000 | [
[
"Yang",
"Ruiyi",
""
],
[
"Salim",
"Flora D.",
""
],
[
"Xue",
"Hao",
""
]
] |
2402.12183 | Mafalda Malafaia | Mafalda Malafaia, Thalea Schlender, Peter A. N. Bosman, Tanja
Alderliesten | MultiFIX: An XAI-friendly feature inducing approach to building models
from multimodal data | 8 pages, 9 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the health domain, decisions are often based on different data modalities.
Thus, when creating prediction models, multimodal fusion approaches that can
extract and combine relevant features from different data modalities, can be
highly beneficial. Furthermore, it is important to understand how each modality
impacts the final prediction, especially in high-stake domains, so that these
models can be used in a trustworthy and responsible manner. We propose
MultiFIX: a new interpretability-focused multimodal data fusion pipeline that
explicitly induces separate features from different data types that can
subsequently be combined to make a final prediction. An end-to-end deep
learning architecture is used to train a predictive model and extract
representative features of each modality. Each part of the model is then
explained using explainable artificial intelligence techniques. Attention maps
are used to highlight important regions in image inputs. Inherently
interpretable symbolic expressions, learned with GP-GOMEA, are used to describe
the contribution of tabular inputs. The fusion of the extracted features to
predict the target label is also replaced by a symbolic expression, learned
with GP-GOMEA. Results on synthetic problems demonstrate the strengths and
limitations of MultiFIX. Lastly, we apply MultiFIX to a publicly available
dataset for the detection of malignant skin lesions.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 14:45:46 GMT"
}
] | 1,708,387,200,000 | [
[
"Malafaia",
"Mafalda",
""
],
[
"Schlender",
"Thalea",
""
],
[
"Bosman",
"Peter A. N.",
""
],
[
"Alderliesten",
"Tanja",
""
]
] |
2402.12422 | Murray Shanahan | Murray Shanahan | Simulacra as Conscious Exotica | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The advent of conversational agents with increasingly human-like behaviour
throws old philosophical questions into new light. Does it, or could it, ever
make sense to speak of AI agents built out of generative language models in
terms of consciousness, given that they are "mere" simulacra of human
behaviour, and that what they do can be seen as "merely" role play? Drawing on
the later writings of Wittgenstein, this paper attempts to tackle this question
while avoiding the pitfalls of dualistic thinking.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 13:53:10 GMT"
}
] | 1,708,473,600,000 | [
[
"Shanahan",
"Murray",
""
]
] |
2402.12608 | Subash Neupane | Hassan S. Al Khatib, Subash Neupane, Harish Kumar Manchukonda,
Noorbakhsh Amiri Golilarz, Sudip Mittal, Amin Amirlatifi, Shahram Rahimi | Patient-Centric Knowledge Graphs: A Survey of Current Methods,
Challenges, and Applications | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in
healthcare that focuses on individualized patient care by mapping the patient's
health information in a holistic and multi-dimensional way. PCKGs integrate
various types of health data to provide healthcare professionals with a
comprehensive understanding of a patient's health, enabling more personalized
and effective care. This literature review explores the methodologies,
challenges, and opportunities associated with PCKGs, focusing on their role in
integrating disparate healthcare data and enhancing patient care through a
unified health perspective. In addition, this review also discusses the
complexities of PCKG development, including ontology design, data integration
techniques, knowledge extraction, and structured representation of knowledge.
It highlights advanced techniques such as reasoning, semantic search, and
inference mechanisms essential in constructing and evaluating PCKGs for
actionable healthcare insights. We further explore the practical applications
of PCKGs in personalized medicine, emphasizing their significance in improving
disease prediction and formulating effective treatment plans. Overall, this
review provides a foundational perspective on the current state-of-the-art and
best practices of PCKGs, guiding future research and applications in this
dynamic field.
| [
{
"version": "v1",
"created": "Tue, 20 Feb 2024 00:07:55 GMT"
}
] | 1,708,473,600,000 | [
[
"Khatib",
"Hassan S. Al",
""
],
[
"Neupane",
"Subash",
""
],
[
"Manchukonda",
"Harish Kumar",
""
],
[
"Golilarz",
"Noorbakhsh Amiri",
""
],
[
"Mittal",
"Sudip",
""
],
[
"Amirlatifi",
"Amin",
""
],
[
"Rahimi",
"Shahram",
""
]
] |
2402.12685 | Yu Xiong | Yu Xiong, Zhipeng Hu, Ye Huang, Runze Wu, Kai Guan, Xingchen Fang, Ji
Jiang, Tianze Zhou, Yujing Hu, Haoyu Liu, Tangjie Lyu, Changjie Fan | XRL-Bench: A Benchmark for Evaluating and Comparing Explainable
Reinforcement Learning Techniques | 10 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Reinforcement Learning (RL) has demonstrated substantial potential across
diverse fields, yet understanding its decision-making process, especially in
real-world scenarios where rationality and safety are paramount, is an ongoing
challenge. This paper delves in to Explainable RL (XRL), a subfield of
Explainable AI (XAI) aimed at unravelling the complexities of RL models. Our
focus rests on state-explaining techniques, a crucial subset within XRL
methods, as they reveal the underlying factors influencing an agent's actions
at any given time. Despite their significant role, the lack of a unified
evaluation framework hinders assessment of their accuracy and effectiveness. To
address this, we introduce XRL-Bench, a unified standardized benchmark tailored
for the evaluation and comparison of XRL methods, encompassing three main
modules: standard RL environments, explainers based on state importance, and
standard evaluators. XRL-Bench supports both tabular and image data for state
explanation. We also propose TabularSHAP, an innovative and competitive XRL
method. We demonstrate the practical utility of TabularSHAP in real-world
online gaming services and offer an open-source benchmark platform for the
straightforward implementation and evaluation of XRL methods. Our contributions
facilitate the continued progression of XRL technology.
| [
{
"version": "v1",
"created": "Tue, 20 Feb 2024 03:20:37 GMT"
}
] | 1,708,473,600,000 | [
[
"Xiong",
"Yu",
""
],
[
"Hu",
"Zhipeng",
""
],
[
"Huang",
"Ye",
""
],
[
"Wu",
"Runze",
""
],
[
"Guan",
"Kai",
""
],
[
"Fang",
"Xingchen",
""
],
[
"Jiang",
"Ji",
""
],
[
"Zhou",
"Tianze",
""
],
[
"Hu",
"Yujing",
""
],
[
"Liu",
"Haoyu",
""
],
[
"Lyu",
"Tangjie",
""
],
[
"Fan",
"Changjie",
""
]
] |
2402.12887 | Steven Mascaro | Steven Mascaro, Owen Woodberry, Yue Wu, Ann E. Nicholson | The practice of qualitative parameterisation in the development of
Bayesian networks | 6 pages, 2 figures, technical note | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The typical phases of Bayesian network (BN) structured development include
specification of purpose and scope, structure development, parameterisation and
validation. Structure development is typically focused on qualitative issues
and parameterisation quantitative issues, however there are qualitative and
quantitative issues that arise in both phases. A common step that occurs after
the initial structure has been developed is to perform a rough parameterisation
that only captures and illustrates the intended qualitative behaviour of the
model. This is done prior to a more rigorous parameterisation, ensuring that
the structure is fit for purpose, as well as supporting later development and
validation. In our collective experience and in discussions with other
modellers, this step is an important part of the development process, but is
under-reported in the literature. Since the practice focuses on qualitative
issues, despite being quantitative in nature, we call this step qualitative
parameterisation and provide an outline of its role in the BN development
process.
| [
{
"version": "v1",
"created": "Tue, 20 Feb 2024 10:30:36 GMT"
}
] | 1,708,473,600,000 | [
[
"Mascaro",
"Steven",
""
],
[
"Woodberry",
"Owen",
""
],
[
"Wu",
"Yue",
""
],
[
"Nicholson",
"Ann E.",
""
]
] |
2402.13058 | Tianxiang Zhan | Tianxiang Zhan, Zhen Li, Yong Deng | Random Graph Set and Evidence Pattern Reasoning Model | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Evidence theory is widely used in decision-making and reasoning systems. In
previous research, Transferable Belief Model (TBM) is a commonly used
evidential decision making model, but TBM is a non-preference model. In order
to better fit the decision making goals, the Evidence Pattern Reasoning Model
(EPRM) is proposed. By defining pattern operators and decision making
operators, corresponding preferences can be set for different tasks. Random
Permutation Set (RPS) expands order information for evidence theory. It is hard
for RPS to characterize the complex relationship between samples such as
cycling, paralleling relationships. Therefore, Random Graph Set (RGS) were
proposed to model complex relationships and represent more event types. In
order to illustrate the significance of RGS and EPRM, an experiment of aircraft
velocity ranking was designed and 10,000 cases were simulated. The
implementation of EPRM called Conflict Resolution Decision optimized 18.17\% of
the cases compared to Mean Velocity Decision, effectively improving the
aircraft velocity ranking. EPRM provides a unified solution for evidence-based
decision making.
| [
{
"version": "v1",
"created": "Tue, 20 Feb 2024 14:52:52 GMT"
},
{
"version": "v2",
"created": "Sat, 9 Mar 2024 08:43:20 GMT"
}
] | 1,710,201,600,000 | [
[
"Zhan",
"Tianxiang",
""
],
[
"Li",
"Zhen",
""
],
[
"Deng",
"Yong",
""
]
] |
2402.13264 | Tingting Wang | Tingting Wang, Guilin Qi, Tianxing Wu | KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph
Convolutional Neural Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Fault localization is challenging in online micro-service due to the wide
variety of monitoring data volume, types, events and complex interdependencies
in service and components. Faults events in services are propagative and can
trigger a cascade of alerts in a short period of time. In the industry, fault
localization is typically conducted manually by experienced personnel. This
reliance on experience is unreliable and lacks automation. Different modules
present information barriers during manual localization, making it difficult to
quickly align during urgent faults. This inefficiency lags stability assurance
to minimize fault detection and repair time. Though actionable methods aimed to
automatic the process, the accuracy and efficiency are less than satisfactory.
The precision of fault localization results is of paramount importance as it
underpins engineers trust in the diagnostic conclusions, which are derived from
multiple perspectives and offer comprehensive insights. Therefore, a more
reliable method is required to automatically identify the associative
relationships among fault events and propagation path. To achieve this, KGroot
uses event knowledge and the correlation between events to perform root cause
reasoning by integrating knowledge graphs and GCNs for RCA. FEKG is built based
on historical data, an online graph is constructed in real-time when a failure
event occurs, and the similarity between each knowledge graph and online graph
is compared using GCNs to pinpoint the fault type through a ranking strategy.
Comprehensive experiments demonstrate KGroot can locate the root cause with
accuracy of 93.5% top 3 potential causes in second-level. This performance
matches the level of real-time fault diagnosis in the industrial environment
and significantly surpasses state-of-the-art baselines in RCA in terms of
effectiveness and efficiency.
| [
{
"version": "v1",
"created": "Sun, 11 Feb 2024 10:30:38 GMT"
}
] | 1,708,560,000,000 | [
[
"Wang",
"Tingting",
""
],
[
"Qi",
"Guilin",
""
],
[
"Wu",
"Tianxing",
""
]
] |
2402.13290 | Goonmeet Bajaj | Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit
Sheth | Grounding from an AI and Cognitive Science Lens | null | IEEE Intelligent Systems, 2024 | 10.1109/MIS.2024.3366669 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Grounding is a challenging problem, requiring a formal definition and
different levels of abstraction. This article explores grounding from both
cognitive science and machine learning perspectives. It identifies the
subtleties of grounding, its significance for collaborative agents, and
similarities and differences in grounding approaches in both communities. The
article examines the potential of neuro-symbolic approaches tailored for
grounding tasks, showcasing how they can more comprehensively address
grounding. Finally, we discuss areas for further exploration and development in
grounding.
| [
{
"version": "v1",
"created": "Mon, 19 Feb 2024 17:44:34 GMT"
}
] | 1,708,560,000,000 | [
[
"Bajaj",
"Goonmeet",
""
],
[
"Parthasarathy",
"Srinivasan",
""
],
[
"Shalin",
"Valerie L.",
""
],
[
"Sheth",
"Amit",
""
]
] |
2402.13399 | Ninell Oldenburg | Ninell Oldenburg and Tan Zhi-Xuan | Learning and Sustaining Shared Normative Systems via Bayesian Rule
Induction in Markov Games | Accepted to the 23rd International Conference on Autonomous Agents
and Multi-Agent Systems, 8 pages (excl. references), 6 figures/tables,
(Appendix: 7 pages, 6 figures/tables). Code available at:
https://github.com/ninell-oldenburg/social-contracts | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | A universal feature of human societies is the adoption of systems of rules
and norms in the service of cooperative ends. How can we build learning agents
that do the same, so that they may flexibly cooperate with the human
institutions they are embedded in? We hypothesize that agents can achieve this
by assuming there exists a shared set of norms that most others comply with
while pursuing their individual desires, even if they do not know the exact
content of those norms. By assuming shared norms, a newly introduced agent can
infer the norms of an existing population from observations of compliance and
violation. Furthermore, groups of agents can converge to a shared set of norms,
even if they initially diverge in their beliefs about what the norms are. This
in turn enables the stability of the normative system: since agents can
bootstrap common knowledge of the norms, this leads the norms to be widely
adhered to, enabling new entrants to rapidly learn those norms. We formalize
this framework in the context of Markov games and demonstrate its operation in
a multi-agent environment via approximately Bayesian rule induction of
obligative and prohibitive norms. Using our approach, agents are able to
rapidly learn and sustain a variety of cooperative institutions, including
resource management norms and compensation for pro-social labor, promoting
collective welfare while still allowing agents to act in their own interests.
| [
{
"version": "v1",
"created": "Tue, 20 Feb 2024 21:58:40 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Feb 2024 15:46:21 GMT"
}
] | 1,708,646,400,000 | [
[
"Oldenburg",
"Ninell",
""
],
[
"Zhi-Xuan",
"Tan",
""
]
] |
2402.13419 | Zhiyu An | Zhiyu An, Xianzhong Ding, Wan Du | Reward Bound for Behavioral Guarantee of Model-based Planning Agents | To be published in ICLR 24 tiny paper track | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent years have seen an emerging interest in the trustworthiness of machine
learning-based agents in the wild, especially in robotics, to provide safety
assurance for the industry. Obtaining behavioral guarantees for these agents
remains an important problem. In this work, we focus on guaranteeing a
model-based planning agent reaches a goal state within a specific future time
step. We show that there exists a lower bound for the reward at the goal state,
such that if the said reward is below that bound, it is impossible to obtain
such a guarantee. By extension, we show how to enforce preferences over
multiple goals.
| [
{
"version": "v1",
"created": "Tue, 20 Feb 2024 23:17:07 GMT"
}
] | 1,708,560,000,000 | [
[
"An",
"Zhiyu",
""
],
[
"Ding",
"Xianzhong",
""
],
[
"Du",
"Wan",
""
]
] |
2402.13782 | Vincent Derkinderen | Vincent Derkinderen, Robin Manhaeve, Pedro Zuidberg Dos Martires, Luc
De Raedt | Semirings for Probabilistic and Neuro-Symbolic Logic Programming | null | International Journal of Approximate Reasoning (2024): 109130 | 10.1016/j.ijar.2024.109130 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The field of probabilistic logic programming (PLP) focuses on integrating
probabilistic models into programming languages based on logic. Over the past
30 years, numerous languages and frameworks have been developed for modeling,
inference and learning in probabilistic logic programs. While originally PLP
focused on discrete probability, more recent approaches have incorporated
continuous distributions as well as neural networks, effectively yielding
neural-symbolic methods. We provide a unified algebraic perspective on PLP,
showing that many if not most of the extensions of PLP can be cast within a
common algebraic logic programming framework, in which facts are labeled with
elements of a semiring and disjunction and conjunction are replaced by addition
and multiplication. This does not only hold for the PLP variations itself but
also for the underlying execution mechanism that is based on (algebraic) model
counting.
| [
{
"version": "v1",
"created": "Wed, 21 Feb 2024 13:06:52 GMT"
}
] | 1,708,560,000,000 | [
[
"Derkinderen",
"Vincent",
""
],
[
"Manhaeve",
"Robin",
""
],
[
"Martires",
"Pedro Zuidberg Dos",
""
],
[
"De Raedt",
"Luc",
""
]
] |
2402.13785 | Florent Delgrange | Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling, Ann
Now\'e, and Guillermo A. P\'erez | Synthesis of Hierarchical Controllers Based on Deep Reinforcement
Learning Policies | 19 pages main text, 17 pages Appendix (excluding references) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose a novel approach to the problem of controller design for
environments modeled as Markov decision processes (MDPs). Specifically, we
consider a hierarchical MDP a graph with each vertex populated by an MDP called
a "room". We first apply deep reinforcement learning (DRL) to obtain low-level
policies for each room, scaling to large rooms of unknown structure. We then
apply reactive synthesis to obtain a high-level planner that chooses which
low-level policy to execute in each room. The central challenge in synthesizing
the planner is the need for modeling rooms. We address this challenge by
developing a DRL procedure to train concise "latent" policies together with PAC
guarantees on their performance. Unlike previous approaches, ours circumvents a
model distillation step. Our approach combats sparse rewards in DRL and enables
reusability of low-level policies. We demonstrate feasibility in a case study
involving agent navigation amid moving obstacles.
| [
{
"version": "v1",
"created": "Wed, 21 Feb 2024 13:10:58 GMT"
}
] | 1,708,560,000,000 | [
[
"Delgrange",
"Florent",
""
],
[
"Avni",
"Guy",
""
],
[
"Lukina",
"Anna",
""
],
[
"Schilling",
"Christian",
""
],
[
"Nowé",
"Ann",
""
],
[
"Pérez",
"Guillermo A.",
""
]
] |
2402.13927 | Yun-Shiuan Chuang | Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers | The Delusional Hedge Algorithm as a Model of Human Learning from Diverse
Opinions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Whereas cognitive models of learning often assume direct experience with both
the features of an event and with a true label or outcome, much of everyday
learning arises from hearing the opinions of others, without direct access to
either the experience or the ground truth outcome. We consider how people can
learn which opinions to trust in such scenarios by extending the hedge
algorithm: a classic solution for learning from diverse information sources. We
first introduce a semi-supervised variant we call the delusional hedge capable
of learning from both supervised and unsupervised experiences. In two
experiments, we examine the alignment between human judgments and predictions
from the standard hedge, the delusional hedge, and a heuristic baseline model.
Results indicate that humans effectively incorporate both labeled and unlabeled
information in a manner consistent with the delusional hedge algorithm --
suggesting that human learners not only gauge the accuracy of information
sources but also their consistency with other reliable sources. The findings
advance our understanding of human learning from diverse opinions, with
implications for the development of algorithms that better capture how people
learn to weigh conflicting information sources.
| [
{
"version": "v1",
"created": "Wed, 21 Feb 2024 16:48:07 GMT"
}
] | 1,708,560,000,000 | [
[
"Chuang",
"Yun-Shiuan",
""
],
[
"Zhu",
"Jerry",
""
],
[
"Rogers",
"Timothy T.",
""
]
] |
2402.14083 | Lucas Lehnert | Lucas Lehnert, Sainbayar Sukhbaatar, DiJia Su, Qinqing Zheng, Paul
Mcvay, Michael Rabbat, Yuandong Tian | Beyond A*: Better Planning with Transformers via Search Dynamics
Bootstrapping | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While Transformers have enabled tremendous progress in various application
settings, such architectures still trail behind traditional symbolic planners
for solving complex decision making tasks. In this work, we demonstrate how to
train Transformers to solve complex planning tasks. This is accomplished by
training an encoder-decoder Transformer model to predict the search dynamics of
the $A^*$ search algorithm. We fine tune this model to obtain a Searchformer, a
Transformer model that optimally solves previously unseen Sokoban puzzles 93.7%
of the time, while using up to 26.8% fewer search steps than the $A^*$
implementation that was used for training initially. In our training method,
$A^*$'s search dynamics are expressed as a token sequence outlining when task
states are added and removed into the search tree during symbolic planning.
Searchformer significantly outperforms baselines that predict the optimal plan
directly with a 5-10$\times$ smaller model size and a 10$\times$ smaller
training dataset. Lastly, we demonstrate how Searchformer scales to larger and
more complex decision making tasks with improved percentage of solved tasks and
shortened search dynamics.
| [
{
"version": "v1",
"created": "Wed, 21 Feb 2024 19:17:28 GMT"
},
{
"version": "v2",
"created": "Fri, 26 Apr 2024 21:05:19 GMT"
}
] | 1,714,435,200,000 | [
[
"Lehnert",
"Lucas",
""
],
[
"Sukhbaatar",
"Sainbayar",
""
],
[
"Su",
"DiJia",
""
],
[
"Zheng",
"Qinqing",
""
],
[
"Mcvay",
"Paul",
""
],
[
"Rabbat",
"Michael",
""
],
[
"Tian",
"Yuandong",
""
]
] |
2402.14460 | Th\'eophile Champion | Th\'eophile Champion, Howard Bowman, Dimitrije Markovi\'c, Marek
Grze\'s | Reframing the Expected Free Energy: Four Formulations and a Unification | 17 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Active inference is a leading theory of perception, learning and decision
making, which can be applied to neuroscience, robotics, psychology, and machine
learning. Active inference is based on the expected free energy, which is
mostly justified by the intuitive plausibility of its formulations, e.g., the
risk plus ambiguity and information gain / pragmatic value formulations. This
paper seek to formalize the problem of deriving these formulations from a
single root expected free energy definition, i.e., the unification problem.
Then, we study two settings, each one having its own root expected free energy
definition. In the first setting, no justification for the expected free energy
has been proposed to date, but all the formulations can be recovered from it.
However, in this setting, the agent cannot have arbitrary prior preferences
over observations. Indeed, only a limited class of prior preferences over
observations is compatible with the likelihood mapping of the generative model.
In the second setting, a justification of the root expected free energy
definition is known, but this setting only accounts for two formulations, i.e.,
the risk over states plus ambiguity and entropy plus expected energy
formulations.
| [
{
"version": "v1",
"created": "Thu, 22 Feb 2024 11:38:43 GMT"
}
] | 1,708,646,400,000 | [
[
"Champion",
"Théophile",
""
],
[
"Bowman",
"Howard",
""
],
[
"Marković",
"Dimitrije",
""
],
[
"Grześ",
"Marek",
""
]
] |
2402.14596 | Amin Ullah | Amin Ullah, Guilin Qi, Saddam Hussain, Irfan Ullah, Zafar Ali | The Role of LLMs in Sustainable Smart Cities: Applications, Challenges,
and Future Directions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Smart cities stand as pivotal components in the ongoing pursuit of elevating
urban living standards, facilitating the rapid expansion of urban areas while
efficiently managing resources through sustainable and scalable innovations. In
this regard, as emerging technologies like Artificial Intelligence (AI), the
Internet of Things (IoT), big data analytics, and fog and edge computing have
become increasingly prevalent, smart city applications grapple with various
challenges, including the potential for unauthorized disclosure of confidential
and sensitive data. The seamless integration of emerging technologies has
played a vital role in sustaining the dynamic pace of their development. This
paper explores the substantial potential and applications of Deep Learning
(DL), Federated Learning (FL), IoT, Blockchain, Natural Language Processing
(NLP), and large language models (LLMs) in optimizing ICT processes within
smart cities. We aim to spotlight the vast potential of these technologies as
foundational elements that technically strengthen the realization and
advancement of smart cities, underscoring their significance in driving
innovation within this transformative urban milieu. Our discourse culminates
with an exploration of the formidable challenges that DL, FL, IoT, Blockchain,
NLP, and LLMs face within these contexts, and we offer insights into potential
future directions.
| [
{
"version": "v1",
"created": "Wed, 7 Feb 2024 05:22:10 GMT"
}
] | 1,708,646,400,000 | [
[
"Ullah",
"Amin",
""
],
[
"Qi",
"Guilin",
""
],
[
"Hussain",
"Saddam",
""
],
[
"Ullah",
"Irfan",
""
],
[
"Ali",
"Zafar",
""
]
] |
2402.14600 | Wei Du | Wenxuan Fang and Wei Du and Renchu He and Yang Tang and Yaochu Jin and
Gary G. Yen | Diffusion Model-Based Multiobjective Optimization for Gasoline Blending
Scheduling | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Gasoline blending scheduling uses resource allocation and operation
sequencing to meet a refinery's production requirements. The presence of
nonlinearity, integer constraints, and a large number of decision variables
adds complexity to this problem, posing challenges for traditional and
evolutionary algorithms. This paper introduces a novel multiobjective
optimization approach driven by a diffusion model (named DMO), which is
designed specifically for gasoline blending scheduling. To address integer
constraints and generate feasible schedules, the diffusion model creates
multiple intermediate distributions between Gaussian noise and the feasible
domain. Through iterative processes, the solutions transition from Gaussian
noise to feasible schedules while optimizing the objectives using the gradient
descent method. DMO achieves simultaneous objective optimization and constraint
adherence. Comparative tests are conducted to evaluate DMO's performance across
various scales. The experimental results demonstrate that DMO surpasses
state-of-the-art multiobjective evolutionary algorithms in terms of efficiency
when solving gasoline blending scheduling problems.
| [
{
"version": "v1",
"created": "Sun, 4 Feb 2024 05:46:28 GMT"
}
] | 1,708,646,400,000 | [
[
"Fang",
"Wenxuan",
""
],
[
"Du",
"Wei",
""
],
[
"He",
"Renchu",
""
],
[
"Tang",
"Yang",
""
],
[
"Jin",
"Yaochu",
""
],
[
"Yen",
"Gary G.",
""
]
] |
2402.14757 | Divija Swetha Gadiraju | Divija Swetha Gadiraju, Saeed Eftekhar Azam and Deepak Khazanchi | SHM-Traffic: DRL and Transfer learning based UAV Control for Structural
Health Monitoring of Bridges with Traffic | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This work focuses on using advanced techniques for structural health
monitoring (SHM) for bridges with Traffic. We propose an approach using deep
reinforcement learning (DRL)-based control for Unmanned Aerial Vehicle (UAV).
Our approach conducts a concrete bridge deck survey while traffic is ongoing
and detects cracks. The UAV performs the crack detection, and the location of
cracks is initially unknown. We use two edge detection techniques. First, we
use canny edge detection for crack detection. We also use a Convolutional
Neural Network (CNN) for crack detection and compare it with canny edge
detection. Transfer learning is applied using CNN with pre-trained weights
obtained from a crack image dataset. This enables the model to adapt and
improve its performance in identifying and localizing cracks. Proximal Policy
Optimization (PPO) is applied for UAV control and bridge surveys. The
experimentation across various scenarios is performed to evaluate the
performance of the proposed methodology. Key metrics such as task completion
time and reward convergence are observed to gauge the effectiveness of the
approach. We observe that the Canny edge detector offers up to 40\% lower task
completion time, while the CNN excels in up to 12\% better damage detection and
1.8 times better rewards.
| [
{
"version": "v1",
"created": "Thu, 22 Feb 2024 18:19:45 GMT"
}
] | 1,708,646,400,000 | [
[
"Gadiraju",
"Divija Swetha",
""
],
[
"Azam",
"Saeed Eftekhar",
""
],
[
"Khazanchi",
"Deepak",
""
]
] |
2402.15075 | Peng Lin | Peng Lin, Martin Neil and Norman Fenton | Stacking Factorizing Partitioned Expressions in Hybrid Bayesian Network
Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Hybrid Bayesian networks (HBN) contain complex conditional probabilistic
distributions (CPD) specified as partitioned expressions over discrete and
continuous variables. The size of these CPDs grows exponentially with the
number of parent nodes when using discrete inference, resulting in significant
inefficiency. Normally, an effective way to reduce the CPD size is to use a
binary factorization (BF) algorithm to decompose the statistical or arithmetic
functions in the CPD by factorizing the number of connected parent nodes to
sets of size two. However, the BF algorithm was not designed to handle
partitioned expressions. Hence, we propose a new algorithm called stacking
factorization (SF) to decompose the partitioned expressions. The SF algorithm
creates intermediate nodes to incrementally reconstruct the densities in the
original partitioned expression, allowing no more than two continuous parent
nodes to be connected to each child node in the resulting HBN. SF can be either
used independently or combined with the BF algorithm. We show that the SF+BF
algorithm significantly reduces the CPD size and contributes to lowering the
tree-width of a model, thus improving efficiency.
| [
{
"version": "v1",
"created": "Fri, 23 Feb 2024 03:33:06 GMT"
}
] | 1,708,905,600,000 | [
[
"Lin",
"Peng",
""
],
[
"Neil",
"Martin",
""
],
[
"Fenton",
"Norman",
""
]
] |
2402.15140 | Yonglin Jing | Yonglin Jing | A Relation-Interactive Approach for Message Passing in Hyper-relational
Knowledge Graphs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Hyper-relational knowledge graphs (KGs) contain additional key-value pairs,
providing more information about the relations. In many scenarios, the same
relation can have distinct key-value pairs, making the original triple fact
more recognizable and specific. Prior studies on hyper-relational KGs have
established a solid standard method for hyper-relational graph encoding. In
this work, we propose a message-passing-based graph encoder with global
relation structure awareness ability, which we call ReSaE. Compared to the
prior state-of-the-art approach, ReSaE emphasizes the interaction of relations
during message passing process and optimizes the readout structure for link
prediction tasks. Overall, ReSaE gives a encoding solution for hyper-relational
KGs and ensures stronger performance on downstream link prediction tasks. Our
experiments demonstrate that ReSaE achieves state-of-the-art performance on
multiple link prediction benchmarks. Furthermore, we also analyze the influence
of different model structures on model performance.
| [
{
"version": "v1",
"created": "Fri, 23 Feb 2024 06:55:04 GMT"
},
{
"version": "v2",
"created": "Sat, 2 Mar 2024 04:59:36 GMT"
}
] | 1,709,596,800,000 | [
[
"Jing",
"Yonglin",
""
]
] |
2402.15445 | Paolo Liberatore | Paolo Liberatore | Can we forget how we learned? Doxastic redundancy in iterated belief
revision | formerly part of arXiv:2305.09200 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How information was acquired may become irrelevant. An obvious case is when
something is confirmed many times. In terms of iterated belief revision, a
specific revision may become irrelevant in presence of others. Simple
repetitions are an example, but not the only case when this happens. Sometimes,
a revision becomes redundant even in presence of none equal, or even no else
implying it. A necessary and sufficient condition for the redundancy of the
first of a sequence of lexicographic revisions is given. The problem is
coNP-complete even with two propositional revisions only. Complexity is the
same in the Horn case but only with an unbounded number of revisions: it
becomes polynomial with two revisions. Lexicographic revisions are not only
relevant by themselves, but also because sequences of them are the most compact
of the common mechanisms used to represent the state of an iterated revision
process. Shortening sequences of lexicographic revisions is shortening the most
compact representations of iterated belief revision states.
| [
{
"version": "v1",
"created": "Fri, 23 Feb 2024 17:09:04 GMT"
}
] | 1,708,905,600,000 | [
[
"Liberatore",
"Paolo",
""
]
] |
2402.15522 | Enric Rodriguez Carbonell | Robert Nieuwenhuis, Albert Oliveras, Enric Rodriguez-Carbonell | IntSat: Integer Linear Programming by Conflict-Driven
Constraint-Learning | 48 pages. This is the Author's Original Manuscript of the journal
version | null | 10.1080/10556788.2023.2246167 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | State-of-the-art SAT solvers are nowadays able to handle huge real-world
instances. The key to this success is the so-called Conflict-Driven
Clause-Learning (CDCL) scheme, which encompasses a number of techniques that
exploit the conflicts that are encountered during the search for a solution. In
this article we extend these techniques to Integer Linear Programming (ILP),
where variables may take general integer values instead of purely binary ones,
constraints are more expressive than just propositional clauses, and there may
be an objective function to optimise. We explain how these methods can be
implemented efficiently, and discuss possible improvements. Our work is backed
with a basic implementation that shows that, even in this far less mature
stage, our techniques are already a useful complement to the state of the art
in ILP solving.
| [
{
"version": "v1",
"created": "Fri, 16 Feb 2024 12:48:40 GMT"
}
] | 1,708,992,000,000 | [
[
"Nieuwenhuis",
"Robert",
""
],
[
"Oliveras",
"Albert",
""
],
[
"Rodriguez-Carbonell",
"Enric",
""
]
] |
2402.15960 | Yuanhang Zheng | Yuanhang Zheng, Peng Li, Ming Yan, Ji Zhang, Fei Huang and Yang Liu | Budget-Constrained Tool Learning with Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite intensive efforts devoted to tool learning, the problem of
budget-constrained tool learning, which focuses on resolving user queries
within a specific budget constraint, has been widely overlooked. This paper
proposes a novel method for budget-constrained tool learning. Our approach
involves creating a preferable plan under the budget constraint before
utilizing the tools. This plan outlines the feasible tools and the maximum
number of times they can be employed, offering a comprehensive overview of the
tool learning process for large language models. This allows them to allocate
the budget from a broader perspective. To devise the plan without incurring
significant extra costs, we suggest initially estimating the usefulness of the
candidate tools based on past experience. Subsequently, we employ dynamic
programming to formulate the plan. Experimental results demonstrate that our
method can be integrated with various tool learning methods, significantly
enhancing their effectiveness under strict budget constraints.
| [
{
"version": "v1",
"created": "Sun, 25 Feb 2024 02:46:33 GMT"
}
] | 1,708,992,000,000 | [
[
"Zheng",
"Yuanhang",
""
],
[
"Li",
"Peng",
""
],
[
"Yan",
"Ming",
""
],
[
"Zhang",
"Ji",
""
],
[
"Huang",
"Fei",
""
],
[
"Liu",
"Yang",
""
]
] |
2402.16505 | J.-M. Chauvet | Jean-Marie Chauvet | Memory GAPS: Would LLMs pass the Tulving Test? | 15 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The Tulving Test was designed to investigate memory performance in
recognition and recall tasks. Its results help assess the relevance of the
"Synergistic Ecphory Model" of memory and similar RK paradigms in human
performance. This paper starts investigating whether the more than
forty-year-old framework sheds some light on LLMs' acts of remembering.
| [
{
"version": "v1",
"created": "Mon, 26 Feb 2024 11:40:51 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Feb 2024 15:40:31 GMT"
}
] | 1,709,164,800,000 | [
[
"Chauvet",
"Jean-Marie",
""
]
] |
2402.16924 | Marcin Jan Schroeder | Marcin J. Schroeder | Theoretical Unification of the Fractured Aspects of Information | 52 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The article has as its main objective the identification of fundamental
epistemological obstacles in the study of information related to unnecessary
methodological assumptions and the demystification of popular beliefs in the
fundamental divisions of the aspects of information that can be understood as
Bachelardian rupture of epistemological obstacles. These general considerations
are preceded by an overview of the motivations for the study of information and
the role of the concept of information in the conceptualization of
intelligence, complexity, and consciousness justifying the need for a
sufficiently general perspective in the study of information, and are followed
at the end of the article by a brief exposition of an example of a possible
application in the development of the unified theory of information free from
unnecessary divisions and claims of superiority of the existing preferences in
methodology. The reference to Gaston Bachelard and his ideas of epistemological
obstacles and epistemological ruptures seems highly appropriate for the
reflection on the development of information study, in particular in the
context of obstacles such as the absence of semantics of information,
negligence of its structural analysis, separation of its digital and analog
forms, and misguided use of mathematics.
| [
{
"version": "v1",
"created": "Mon, 26 Feb 2024 10:35:41 GMT"
}
] | 1,709,078,400,000 | [
[
"Schroeder",
"Marcin J.",
""
]
] |
2402.19195 | Tiroshan Madhushanka | Tiroshan Madushanka, Ryutaro Ichise | Negative Sampling in Knowledge Graph Representation Learning: A Review | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Knowledge graph representation learning (KGRL) or knowledge graph embedding
(KGE) plays a crucial role in AI applications for knowledge construction and
information exploration. These models aim to encode entities and relations
present in a knowledge graph into a lower-dimensional vector space. During the
training process of KGE models, using positive and negative samples becomes
essential for discrimination purposes. However, obtaining negative samples
directly from existing knowledge graphs poses a challenge, emphasizing the need
for effective generation techniques. The quality of these negative samples
greatly impacts the accuracy of the learned embeddings, making their generation
a critical aspect of KGRL. This comprehensive survey paper systematically
reviews various negative sampling (NS) methods and their contributions to the
success of KGRL. Their respective advantages and disadvantages are outlined by
categorizing existing NS methods into five distinct categories. Moreover, this
survey identifies open research questions that serve as potential directions
for future investigations. By offering a generalization and alignment of
fundamental NS concepts, this survey provides valuable insights for designing
effective NS methods in the context of KGRL and serves as a motivating force
for further advancements in the field.
| [
{
"version": "v1",
"created": "Thu, 29 Feb 2024 14:26:20 GMT"
}
] | 1,709,251,200,000 | [
[
"Madushanka",
"Tiroshan",
""
],
[
"Ichise",
"Ryutaro",
""
]
] |
2403.00685 | Loris Bozzato | Gabriele Sacco, Loris Bozzato, Oliver Kutz | Know your exceptions: Towards an Ontology of Exceptions in Knowledge
Representation | 18 pages, 4 pages are appendix. (v2 updates: minor revisions on
discussions, terminology and text editing) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Defeasible reasoning is a kind of reasoning where some generalisations may
not be valid in all circumstances, that is general conclusions may fail in some
cases. Various formalisms have been developed to model this kind of reasoning,
which is characteristic of common-sense contexts. However, it is not easy for a
modeller to choose among these systems the one that better fits its domain from
an ontological point of view. In this paper we first propose a framework based
on the notions of exceptionality and defeasibility in order to be able to
compare formalisms and reveal their ontological commitments. Then, we apply
this framework to compare four systems, showing the differences that may occur
from an ontological perspective.
| [
{
"version": "v1",
"created": "Fri, 1 Mar 2024 17:19:35 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Mar 2024 16:35:43 GMT"
}
] | 1,709,683,200,000 | [
[
"Sacco",
"Gabriele",
""
],
[
"Bozzato",
"Loris",
""
],
[
"Kutz",
"Oliver",
""
]
] |
2403.00690 | Dominik Jeurissen | Dominik Jeurissen and Diego Perez-Liebana and Jeremy Gow and Duygu
Cakmak and James Kwan | Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have shown great success as high-level planners
for zero-shot game-playing agents. However, these agents are primarily
evaluated on Minecraft, where long-term planning is relatively straightforward.
In contrast, agents tested in dynamic robot environments face limitations due
to simplistic environments with only a few objects and interactions. To fill
this gap in the literature, we present NetPlay, the first LLM-powered zero-shot
agent for the challenging roguelike NetHack. NetHack is a particularly
challenging environment due to its diverse set of items and monsters, complex
interactions, and many ways to die.
NetPlay uses an architecture designed for dynamic robot environments,
modified for NetHack. Like previous approaches, it prompts the LLM to choose
from predefined skills and tracks past interactions to enhance decision-making.
Given NetHack's unpredictable nature, NetPlay detects important game events to
interrupt running skills, enabling it to react to unforeseen circumstances.
While NetPlay demonstrates considerable flexibility and proficiency in
interacting with NetHack's mechanics, it struggles with ambiguous task
descriptions and a lack of explicit feedback. Our findings demonstrate that
NetPlay performs best with detailed context information, indicating the
necessity for dynamic methods in supplying context information for complex
games such as NetHack.
| [
{
"version": "v1",
"created": "Fri, 1 Mar 2024 17:22:16 GMT"
}
] | 1,709,510,400,000 | [
[
"Jeurissen",
"Dominik",
""
],
[
"Perez-Liebana",
"Diego",
""
],
[
"Gow",
"Jeremy",
""
],
[
"Cakmak",
"Duygu",
""
],
[
"Kwan",
"James",
""
]
] |
2403.00783 | Hankz Hankui Zhuo | Hankz Hankui Zhuo and Xin Chen and Rong Pan | On the Roles of LLMs in Planning: Embedding LLMs into Planning Graphs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Plan synthesis aims to generate a course of actions or policies to transit
given initial states to goal states, provided domain models that could be
designed by experts or learnt from training data or interactions with the
world. Intrigued by the claims of emergent planning capabilities in large
language models (LLMs), works have been proposed to investigate the planning
effectiveness of LLMs, without considering any utilization of off-the-shelf
planning techniques in LLMs. In this paper, we aim to further study the insight
of the planning capability of LLMs by investigating the roles of LLMs in
off-the-shelf planning frameworks. To do this, we investigate the effectiveness
of embedding LLMs into one of the well-known planning frameworks, graph-based
planning, proposing a novel LLMs-based planning framework with LLMs embedded in
two levels of planning graphs, i.e., mutual constraints generation level and
constraints solving level. We empirically exhibit the effectiveness of our
proposed framework in various planning domains.
| [
{
"version": "v1",
"created": "Sun, 18 Feb 2024 15:53:32 GMT"
}
] | 1,709,596,800,000 | [
[
"Zhuo",
"Hankz Hankui",
""
],
[
"Chen",
"Xin",
""
],
[
"Pan",
"Rong",
""
]
] |
2403.00833 | Bidipta Sarkar | Qiuyuan Huang, Naoki Wake, Bidipta Sarkar, Zane Durante, Ran Gong,
Rohan Taori, Yusuke Noda, Demetri Terzopoulos, Noboru Kuno, Ade Famoti,
Ashley Llorens, John Langford, Hoi Vo, Li Fei-Fei, Katsu Ikeuchi, Jianfeng
Gao | Position Paper: Agent AI Towards a Holistic Intelligence | 22 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:2401.03568 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent advancements in large foundation models have remarkably enhanced our
understanding of sensory information in open-world environments. In leveraging
the power of foundation models, it is crucial for AI research to pivot away
from excessive reductionism and toward an emphasis on systems that function as
cohesive wholes. Specifically, we emphasize developing Agent AI -- an embodied
system that integrates large foundation models into agent actions. The emerging
field of Agent AI spans a wide range of existing embodied and agent-based
multimodal interactions, including robotics, gaming, and healthcare systems,
etc. In this paper, we propose a novel large action model to achieve embodied
intelligent behavior, the Agent Foundation Model. On top of this idea, we
discuss how agent AI exhibits remarkable capabilities across a variety of
domains and tasks, challenging our understanding of learning and cognition.
Furthermore, we discuss the potential of Agent AI from an interdisciplinary
perspective, underscoring AI cognition and consciousness within scientific
discourse. We believe that those discussions serve as a basis for future
research directions and encourage broader societal engagement.
| [
{
"version": "v1",
"created": "Wed, 28 Feb 2024 16:09:56 GMT"
}
] | 1,709,596,800,000 | [
[
"Huang",
"Qiuyuan",
""
],
[
"Wake",
"Naoki",
""
],
[
"Sarkar",
"Bidipta",
""
],
[
"Durante",
"Zane",
""
],
[
"Gong",
"Ran",
""
],
[
"Taori",
"Rohan",
""
],
[
"Noda",
"Yusuke",
""
],
[
"Terzopoulos",
"Demetri",
""
],
[
"Kuno",
"Noboru",
""
],
[
"Famoti",
"Ade",
""
],
[
"Llorens",
"Ashley",
""
],
[
"Langford",
"John",
""
],
[
"Vo",
"Hoi",
""
],
[
"Fei-Fei",
"Li",
""
],
[
"Ikeuchi",
"Katsu",
""
],
[
"Gao",
"Jianfeng",
""
]
] |
2403.00980 | Saugat Aryal | Saugat Aryal, Mark T. Keane | Even-Ifs From If-Onlys: Are the Best Semi-Factual Explanations Found
Using Counterfactuals As Guides? | 16 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently, counterfactuals using "if-only" explanations have become very
popular in eXplainable AI (XAI), as they describe which changes to
feature-inputs of a black-box AI system result in changes to a (usually
negative) decision-outcome. Even more recently, semi-factuals using "even-if"
explanations have gained more attention. They elucidate the feature-input
changes that do not change the decision-outcome of the AI system, with a
potential to suggest more beneficial recourses. Some semi-factual methods use
counterfactuals to the query-instance to guide semi-factual production
(so-called counterfactual-guided methods), whereas others do not (so-called
counterfactual-free methods). In this work, we perform comprehensive tests of 8
semi-factual methods on 7 datasets using 5 key metrics, to determine whether
counterfactual guidance is necessary to find the best semi-factuals. The
results of these tests suggests not, but rather that computing other aspects of
the decision space lead to better semi-factual XAI.
| [
{
"version": "v1",
"created": "Fri, 1 Mar 2024 21:04:48 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Apr 2024 15:36:15 GMT"
}
] | 1,714,089,600,000 | [
[
"Aryal",
"Saugat",
""
],
[
"Keane",
"Mark T.",
""
]
] |
2403.01199 | Sankalpa Ghose | Sankalpa Ghose, Yip Fai Tse, Kasra Rasaee, Jeff Sebo, Peter Singer | The Case for Animal-Friendly AI | AAAI 2024 Workshop on Public Sector LLMs: Algorithmic and
Sociotechnical Design. 12 pages, 11 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence is seen as increasingly important, and potentially
profoundly so, but the fields of AI ethics and AI engineering have not fully
recognized that these technologies, including large language models (LLMs),
will have massive impacts on animals. We argue that this impact matters,
because animals matter morally.
As a first experiment in evaluating animal consideration in LLMs, we
constructed a proof-of-concept Evaluation System, which assesses LLM responses
and biases from multiple perspectives. This system evaluates LLM outputs by two
criteria: their truthfulness, and the degree of consideration they give to the
interests of animals. We tested OpenAI ChatGPT 4 and Anthropic Claude 2.1 using
a set of structured queries and predefined normative perspectives. Preliminary
results suggest that the outcomes of the tested models can be benchmarked
regarding the consideration they give to animals, and that generated positions
and biases might be addressed and mitigated with more developed and validated
systems.
Our research contributes one possible approach to integrating animal ethics
in AI, opening pathways for future studies and practical applications in
various fields, including education, public policy, and regulation, that
involve or relate to animals and society. Overall, this study serves as a step
towards more useful and responsible AI systems that better recognize and
respect the vital interests and perspectives of all sentient beings.
| [
{
"version": "v1",
"created": "Sat, 2 Mar 2024 12:41:11 GMT"
}
] | 1,709,596,800,000 | [
[
"Ghose",
"Sankalpa",
""
],
[
"Tse",
"Yip Fai",
""
],
[
"Rasaee",
"Kasra",
""
],
[
"Sebo",
"Jeff",
""
],
[
"Singer",
"Peter",
""
]
] |
2403.01508 | Weizhi Fei | Weizhi Fei, Zihao Wang, Hang Yin, Yang Duan, Hanghang Tong, Yangqiu
Song | Soft Reasoning on Uncertain Knowledge Graphs | 10 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The study of machine learning-based logical query-answering enables reasoning
with large-scale and incomplete knowledge graphs. This paper further advances
this line of research by considering the uncertainty in the knowledge. The
uncertain nature of knowledge is widely observed in the real world, but
\textit{does not} align seamlessly with the first-order logic underpinning
existing studies. To bridge this gap, we study the setting of soft queries on
uncertain knowledge, which is motivated by the establishment of soft constraint
programming. We further propose an ML-based approach with both forward
inference and backward calibration to answer soft queries on large-scale,
incomplete, and uncertain knowledge graphs. Theoretical discussions present
that our methods share the same complexity as state-of-the-art inference
algorithms for first-order queries. Empirical results justify the superior
performance of our approach against previous ML-based methods with number
embedding extensions.
| [
{
"version": "v1",
"created": "Sun, 3 Mar 2024 13:13:53 GMT"
}
] | 1,709,596,800,000 | [
[
"Fei",
"Weizhi",
""
],
[
"Wang",
"Zihao",
""
],
[
"Yin",
"Hang",
""
],
[
"Duan",
"Yang",
""
],
[
"Tong",
"Hanghang",
""
],
[
"Song",
"Yangqiu",
""
]
] |
2403.02053 | Zhipeng Ma | Zhipeng Ma, Bo N{\o}rregaard J{\o}rgensen, Zheng Ma | A Scoping Review of Energy-Efficient Driving Behaviors and Applied
State-of-the-Art AI Methods | null | Energies 2024, 17, 500 | 10.3390/en17020500 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The transportation sector remains a major contributor to greenhouse gas
emissions. The understanding of energy-efficient driving behaviors and
utilization of energy-efficient driving strategies are essential to reduce
vehicles' fuel consumption. However, there is no comprehensive investigation
into energy-efficient driving behaviors and strategies. Furthermore, many
state-of-the-art AI models have been applied for the analysis of eco-friendly
driving styles, but no overview is available. To fill the gap, this paper
conducts a thorough literature review on ecological driving behaviors and
styles and analyzes the driving factors influencing energy consumption and
state-of-the-art methodologies. With a thorough scoping review process, the
methodological and related data are compared. The results show that the factors
that impact driving behaviors can be summarized into eleven features including
speed, acceleration, deceleration, pedal, and so on. This paper finds that
supervised/unsupervised learning algorithms and reinforcement learning
frameworks have been popularly used to model the vehicle's energy consumption
with multi-dimensional data. Furthermore, the literature shows that the driving
data are collected from either simulators or real-world experiments, and the
real-world data are mainly stored and transmitted by meters, controller area
networks, onboard data services, smartphones, and additional sensors installed
in the vehicle. Based on driving behavior factors, driver characteristics, and
safety rules, this paper recommends nine energy-efficient driving styles
including four guidelines for the drivers' selection and adjustment of the
vehicle parameters, three recommendations for the energy-efficient driving
styles in different driving scenarios, and two subjective suggestions for
different types of drivers and employers.
| [
{
"version": "v1",
"created": "Mon, 4 Mar 2024 13:57:34 GMT"
}
] | 1,709,596,800,000 | [
[
"Ma",
"Zhipeng",
""
],
[
"Jørgensen",
"Bo Nørregaard",
""
],
[
"Ma",
"Zheng",
""
]
] |
2403.02054 | Shuvayan Brahmachary | Shuvayan Brahmachary, Subodh M. Joshi, Aniruddha Panda, Kaushik
Koneripalli, Arun Kumar Sagotra, Harshil Patel, Ankush Sharma, Ameya D.
Jagtap, Kaushic Kalyanaraman | Large Language Model-Based Evolutionary Optimizer: Reasoning with
elitism | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Large Language Models (LLMs) have demonstrated remarkable reasoning
abilities, prompting interest in their application as black-box optimizers.
This paper asserts that LLMs possess the capability for zero-shot optimization
across diverse scenarios, including multi-objective and high-dimensional
problems. We introduce a novel population-based method for numerical
optimization using LLMs called Language-Model-Based Evolutionary Optimizer
(LEO). Our hypothesis is supported through numerical examples, spanning
benchmark and industrial engineering problems such as supersonic nozzle shape
optimization, heat transfer, and windfarm layout optimization. We compare our
method to several gradient-based and gradient-free optimization approaches.
While LLMs yield comparable results to state-of-the-art methods, their
imaginative nature and propensity to hallucinate demand careful handling. We
provide practical guidelines for obtaining reliable answers from LLMs and
discuss method limitations and potential research directions.
| [
{
"version": "v1",
"created": "Mon, 4 Mar 2024 13:57:37 GMT"
}
] | 1,709,596,800,000 | [
[
"Brahmachary",
"Shuvayan",
""
],
[
"Joshi",
"Subodh M.",
""
],
[
"Panda",
"Aniruddha",
""
],
[
"Koneripalli",
"Kaushik",
""
],
[
"Sagotra",
"Arun Kumar",
""
],
[
"Patel",
"Harshil",
""
],
[
"Sharma",
"Ankush",
""
],
[
"Jagtap",
"Ameya D.",
""
],
[
"Kalyanaraman",
"Kaushic",
""
]
] |
2403.02454 | Asad Anjum | Asad Anjum, Yuting Li, Noelle Law, M Charity, and Julian Togelius | The Ink Splotch Effect: A Case Study on ChatGPT as a Co-Creative Game
Designer | 12 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper studies how large language models (LLMs) can act as effective,
high-level creative collaborators and ``muses'' for game design. We model the
design of this study after the exercises artists use by looking at amorphous
ink splotches for creative inspiration. Our goal is to determine whether
AI-assistance can improve, hinder, or provide an alternative quality to games
when compared to the creative intents implemented by human designers. The
capabilities of LLMs as game designers are stress tested by placing it at the
forefront of the decision making process. Three prototype games are designed
across 3 different genres: (1) a minimalist base game, (2) a game with features
and game feel elements added by a human game designer, and (3) a game with
features and feel elements directly implemented from prompted outputs of the
LLM, ChatGPT. A user study was conducted and participants were asked to blindly
evaluate the quality and their preference of these games. We discuss both the
development process of communicating creative intent to an AI chatbot and the
synthesized open feedback of the participants. We use this data to determine
both the benefits and shortcomings of AI in a more design-centric role.
| [
{
"version": "v1",
"created": "Mon, 4 Mar 2024 20:14:38 GMT"
}
] | 1,709,683,200,000 | [
[
"Anjum",
"Asad",
""
],
[
"Li",
"Yuting",
""
],
[
"Law",
"Noelle",
""
],
[
"Charity",
"M",
""
],
[
"Togelius",
"Julian",
""
]
] |
2403.02482 | Rahul Mihir Patel | Rahul Patel, Elias B. Khalil, David Bergman | MORBDD: Multiobjective Restricted Binary Decision Diagrams by Learning
to Sparsify | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In multicriteria decision-making, a user seeks a set of non-dominated
solutions to a (constrained) multiobjective optimization problem, the so-called
Pareto frontier. In this work, we seek to bring a state-of-the-art method for
exact multiobjective integer linear programming into the heuristic realm. We
focus on binary decision diagrams (BDDs) which first construct a graph that
represents all feasible solutions to the problem and then traverse the graph to
extract the Pareto frontier. Because the Pareto frontier may be exponentially
large, enumerating it over the BDD can be time-consuming. We explore how
restricted BDDs, which have already been shown to be effective as heuristics
for single-objective problems, can be adapted to multiobjective optimization
through the use of machine learning (ML). MORBDD, our ML-based BDD sparsifier,
first trains a binary classifier to eliminate BDD nodes that are unlikely to
contribute to Pareto solutions, then post-processes the sparse BDD to ensure
its connectivity via optimization. Experimental results on multiobjective
knapsack problems show that MORBDD is highly effective at producing very small
restricted BDDs with excellent approximation quality, outperforming
width-limited restricted BDDs and the well-known evolutionary algorithm
NSGA-II.
| [
{
"version": "v1",
"created": "Mon, 4 Mar 2024 21:04:54 GMT"
}
] | 1,709,683,200,000 | [
[
"Patel",
"Rahul",
""
],
[
"Khalil",
"Elias B.",
""
],
[
"Bergman",
"David",
""
]
] |
2403.02610 | Ruck Thawonmas | Pittawat Taveekitworachai, Febri Abdullah, Mury F. Dewantoro, Yi Xia,
Pratch Suntichaikul, Ruck Thawonmas, Julian Togelius, Jochen Renz | ChatGPT4PCG 2 Competition: Prompt Engineering for Science Birds Level
Generation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper presents the second ChatGPT4PCG competition at the 2024 IEEE
Conference on Games. In this edition of the competition, we follow the first
edition, but make several improvements and changes. We introduce a new
evaluation metric along with allowing a more flexible format for participants'
submissions and making several improvements to the evaluation pipeline.
Continuing from the first edition, we aim to foster and explore the realm of
prompt engineering (PE) for procedural content generation (PCG). While the
first competition saw success, it was hindered by various limitations; we aim
to mitigate these limitations in this edition. We introduce diversity as a new
metric to discourage submissions aimed at producing repetitive structures.
Furthermore, we allow submission of a Python program instead of a prompt text
file for greater flexibility in implementing advanced PE approaches, which may
require control flow, including conditions and iterations. We also make several
improvements to the evaluation pipeline with a better classifier for similarity
evaluation and better-performing function signatures. We thoroughly evaluate
the effectiveness of the new metric and the improved classifier. Additionally,
we perform an ablation study to select a function signature to instruct ChatGPT
for level generation. Finally, we provide implementation examples of various PE
techniques in Python and evaluate their preliminary performance. We hope this
competition serves as a resource and platform for learning about PE and PCG in
general.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 02:58:57 GMT"
}
] | 1,709,683,200,000 | [
[
"Taveekitworachai",
"Pittawat",
""
],
[
"Abdullah",
"Febri",
""
],
[
"Dewantoro",
"Mury F.",
""
],
[
"Xia",
"Yi",
""
],
[
"Suntichaikul",
"Pratch",
""
],
[
"Thawonmas",
"Ruck",
""
],
[
"Togelius",
"Julian",
""
],
[
"Renz",
"Jochen",
""
]
] |
2403.02635 | Ke Zhang | Ke Zhang, DanDan Zhu, Qiuhan Xu, Hao Zhou and Ce Zheng | PPS-QMIX: Periodically Parameter Sharing for Accelerating Convergence of
Multi-Agent Reinforcement Learning | 10 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training for multi-agent reinforcement learning(MARL) is a time-consuming
process caused by distribution shift of each agent. One drawback is that
strategy of each agent in MARL is independent but actually in cooperation.
Thus, a vertical issue in multi-agent reinforcement learning is how to
efficiently accelerate training process. To address this problem, current
research has leveraged a centralized function(CF) across multiple agents to
learn contribution of the team reward for each agent. However, CF based methods
introduce joint error from other agents in estimation of value network. In so
doing, inspired by federated learning, we propose three simple novel approaches
called Average Periodically Parameter Sharing(A-PPS), Reward-Scalability
Periodically Parameter Sharing(RS-PPS) and Partial Personalized Periodically
Parameter Sharing(PP-PPS) mechanism to accelerate training of MARL. Agents
share Q-value network periodically during the training process. Agents which
has same identity adapt collected reward as scalability and update partial
neural network during period to share different parameters. We apply our
approaches in classical MARL method QMIX and evaluate our approaches on various
tasks in StarCraft Multi-Agent Challenge(SMAC) environment. Performance of
numerical experiments yield enormous enhancement, with an average improvement
of 10\%-30\%, and enable to win tasks that QMIX cannot. Our code can be
downloaded from https://github.com/ColaZhang22/PPS-QMIX
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 03:59:01 GMT"
}
] | 1,709,683,200,000 | [
[
"Zhang",
"Ke",
""
],
[
"Zhu",
"DanDan",
""
],
[
"Xu",
"Qiuhan",
""
],
[
"Zhou",
"Hao",
""
],
[
"Zheng",
"Ce",
""
]
] |
2403.02719 | Yu Zhao | Yanbei Liu, Yu Zhao, Xiao Wang, Lei Geng and Zhitao Xiao | Multi-Scale Subgraph Contrastive Learning | The 32nd International Joint Conference on Artificial Intelligence
(IJCAI-2023) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Graph-level contrastive learning, aiming to learn the representations for
each graph by contrasting two augmented graphs, has attracted considerable
attention. Previous studies usually simply assume that a graph and its
augmented graph as a positive pair, otherwise as a negative pair. However, it
is well known that graph structure is always complex and multi-scale, which
gives rise to a fundamental question: after graph augmentation, will the
previous assumption still hold in reality? By an experimental analysis, we
discover the semantic information of an augmented graph structure may be not
consistent as original graph structure, and whether two augmented graphs are
positive or negative pairs is highly related with the multi-scale structures.
Based on this finding, we propose a multi-scale subgraph contrastive learning
architecture which is able to characterize the fine-grained semantic
information. Specifically, we generate global and local views at different
scales based on subgraph sampling, and construct multiple contrastive
relationships according to their semantic associations to provide richer
self-supervised signals. Extensive experiments and parametric analyzes on eight
graph classification real-world datasets well demonstrate the effectiveness of
the proposed method.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 07:17:18 GMT"
},
{
"version": "v2",
"created": "Thu, 11 Apr 2024 03:06:41 GMT"
},
{
"version": "v3",
"created": "Fri, 12 Apr 2024 01:15:01 GMT"
}
] | 1,713,139,200,000 | [
[
"Liu",
"Yanbei",
""
],
[
"Zhao",
"Yu",
""
],
[
"Wang",
"Xiao",
""
],
[
"Geng",
"Lei",
""
],
[
"Xiao",
"Zhitao",
""
]
] |
2403.02723 | Mengmei Zhang | Mengmei Zhang, Xiao Wang, Chuan Shi, Lingjuan Lyu, Tianchi Yang,
Junping Du | Minimum Topology Attacks for Graph Neural Networks | Published on WWW 2023. Proceedings of the ACM Web Conference 2023 | null | 10.1145/3543507.3583509 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the great popularity of Graph Neural Networks (GNNs), their robustness
to adversarial topology attacks has received significant attention. Although
many attack methods have been proposed, they mainly focus on fixed-budget
attacks, aiming at finding the most adversarial perturbations within a fixed
budget for target node. However, considering the varied robustness of each
node, there is an inevitable dilemma caused by the fixed budget, i.e., no
successful perturbation is found when the budget is relatively small, while if
it is too large, the yielding redundant perturbations will hurt the
invisibility. To break this dilemma, we propose a new type of topology attack,
named minimum-budget topology attack, aiming to adaptively find the minimum
perturbation sufficient for a successful attack on each node. To this end, we
propose an attack model, named MiBTack, based on a dynamic projected gradient
descent algorithm, which can effectively solve the involving non-convex
constraint optimization on discrete topology. Extensive results on three GNNs
and four real-world datasets show that MiBTack can successfully lead all target
nodes misclassified with the minimum perturbation edges. Moreover, the obtained
minimum budget can be used to measure node robustness, so we can explore the
relationships of robustness, topology, and uncertainty for nodes, which is
beyond what the current fixed-budget topology attacks can offer.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 07:29:12 GMT"
}
] | 1,709,683,200,000 | [
[
"Zhang",
"Mengmei",
""
],
[
"Wang",
"Xiao",
""
],
[
"Shi",
"Chuan",
""
],
[
"Lyu",
"Lingjuan",
""
],
[
"Yang",
"Tianchi",
""
],
[
"Du",
"Junping",
""
]
] |
2403.02760 | Xiaonan Xu | Xiaonan Xu, Yichao Wu, Penghao Liang, Yuhang He, Han Wang | Emerging Synergies Between Large Language Models and Machine Learning in
Ecommerce Recommendations | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the boom of e-commerce and web applications, recommender systems have
become an important part of our daily lives, providing personalized
recommendations based on the user's preferences. Although deep neural networks
(DNNs) have made significant progress in improving recommendation systems by
simulating the interaction between users and items and incorporating their
textual information, these DNN-based approaches still have some limitations,
such as the difficulty of effectively understanding users' interests and
capturing textual information. It is not possible to generalize to different
seen/unseen recommendation scenarios and reason about their predictions. At the
same time, the emergence of large language models (LLMs), represented by
ChatGPT and GPT-4, has revolutionized the fields of natural language processing
(NLP) and artificial intelligence (AI) due to their superior capabilities in
the basic tasks of language understanding and generation, and their impressive
generalization and reasoning capabilities. As a result, recent research has
sought to harness the power of LLM to improve recommendation systems. Given the
rapid development of this research direction in the field of recommendation
systems, there is an urgent need for a systematic review of existing LLM-driven
recommendation systems for researchers and practitioners in related fields to
gain insight into. More specifically, we first introduced a representative
approach to learning user and item representations using LLM as a feature
encoder. We then reviewed the latest advances in LLMs techniques for
collaborative filtering enhanced recommendation systems from the three
paradigms of pre-training, fine-tuning, and prompting. Finally, we had a
comprehensive discussion on the future direction of this emerging field.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 08:31:00 GMT"
},
{
"version": "v2",
"created": "Tue, 12 Mar 2024 11:29:07 GMT"
}
] | 1,710,288,000,000 | [
[
"Xu",
"Xiaonan",
""
],
[
"Wu",
"Yichao",
""
],
[
"Liang",
"Penghao",
""
],
[
"He",
"Yuhang",
""
],
[
"Wang",
"Han",
""
]
] |
2403.02783 | Sebastien Verel | S\'ebastien Verel (LISIC), Sarah Thomson, Omar Rifki (LISIC) | Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT
instances | null | Evolutionary Computation in Combinatorial Optimization Conference
(evoCOP), Apr 2024, Aberystwyth, United Kingdom | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Quadratic Assignment Problem (QAP) is one of the major domains in the
field of evolutionary computation, and more widely in combinatorial
optimization. This paper studies the phase transition of the QAP, which can be
described as a dramatic change in the problem's computational complexity and
satisfiability, within a narrow range of the problem parameters. To approach
this phenomenon, we introduce a new QAP-SAT design of the initial problem based
on submodularity to capture its difficulty with new features. This
decomposition is studied experimentally using branch-and-bound and tabu search
solvers. A phase transition parameter is then proposed. The critical parameter
of phase transition satisfaction and that of the solving effort are shown to be
highly correlated for tabu search, thus allowing the prediction of difficult
instances.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 08:56:30 GMT"
}
] | 1,709,683,200,000 | [
[
"Verel",
"Sébastien",
"",
"LISIC"
],
[
"Thomson",
"Sarah",
"",
"LISIC"
],
[
"Rifki",
"Omar",
"",
"LISIC"
]
] |
2403.02820 | Buda Baji\'c | Buda Baji\'c, Johannes A. J. Huber, Benedikt Neyses, Linus Olofsson,
Ozan \"Oktem | Reconstruction for Sparse View Tomography of Long Objects Applied to
Imaging in the Wood Industry | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the wood industry, logs are commonly quality screened by discrete X-ray
scans on a moving conveyor belt from a few source positions. Typically,
two-dimensional (2D) slice-wise measurements are obtained by a sequential
scanning geometry. Each 2D slice alone does not carry sufficient information
for a three-dimensional tomographic reconstruction in which biological features
of interest in the log are well preserved. In the present work, we propose a
learned iterative reconstruction method based on the Learned Primal-Dual neural
network, suited for sequential scanning geometries. Our method accumulates
information between neighbouring slices, instead of only accounting for single
slices during reconstruction. Our quantitative and qualitative evaluations with
as few as five source positions show that our method yields reconstructions of
logs that are sufficiently accurate to identify biological features like knots
(branches), heartwood and sapwood.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 09:44:19 GMT"
}
] | 1,709,683,200,000 | [
[
"Bajić",
"Buda",
""
],
[
"Huber",
"Johannes A. J.",
""
],
[
"Neyses",
"Benedikt",
""
],
[
"Olofsson",
"Linus",
""
],
[
"Öktem",
"Ozan",
""
]
] |
2403.02899 | Zhekai Du | Zhekai Du, Xinyao Li, Fengling Li, Ke Lu, Lei Zhu, Jingjing Li | Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Conventional Unsupervised Domain Adaptation (UDA) strives to minimize
distribution discrepancy between domains, which neglects to harness rich
semantics from data and struggles to handle complex domain shifts. A promising
technique is to leverage the knowledge of large-scale pre-trained
vision-language models for more guided adaptation. Despite some endeavors,
current methods often learn textual prompts to embed domain semantics for
source and target domains separately and perform classification within each
domain, limiting cross-domain knowledge transfer. Moreover, prompting only the
language branch lacks flexibility to adapt both modalities dynamically. To
bridge this gap, we propose Domain-Agnostic Mutual Prompting (DAMP) to exploit
domain-invariant semantics by mutually aligning visual and textual embeddings.
Specifically, the image contextual information is utilized to prompt the
language branch in a domain-agnostic and instance-conditioned way. Meanwhile,
visual prompts are imposed based on the domain-agnostic textual prompt to
elicit domain-invariant visual embeddings. These two branches of prompts are
learned mutually with a cross-attention module and regularized with a
semantic-consistency loss and an instance-discrimination contrastive loss.
Experiments on three UDA benchmarks demonstrate the superiority of DAMP over
state-of-the-art approaches.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 12:06:48 GMT"
}
] | 1,709,683,200,000 | [
[
"Du",
"Zhekai",
""
],
[
"Li",
"Xinyao",
""
],
[
"Li",
"Fengling",
""
],
[
"Lu",
"Ke",
""
],
[
"Zhu",
"Lei",
""
],
[
"Li",
"Jingjing",
""
]
] |
2403.02901 | Hanlei Jin | Hanlei Jin, Yang Zhang, Dan Meng, Jun Wang, Jinghua Tan | A Comprehensive Survey on Process-Oriented Automatic Text Summarization
with Exploration of LLM-Based Methods | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Automatic Text Summarization (ATS), utilizing Natural Language Processing
(NLP) algorithms, aims to create concise and accurate summaries, thereby
significantly reducing the human effort required in processing large volumes of
text. ATS has drawn considerable interest in both academic and industrial
circles. Many studies have been conducted in the past to survey ATS methods;
however, they generally lack practicality for real-world implementations, as
they often categorize previous methods from a theoretical standpoint. Moreover,
the advent of Large Language Models (LLMs) has altered conventional ATS
methods. In this survey, we aim to 1) provide a comprehensive overview of ATS
from a ``Process-Oriented Schema'' perspective, which is best aligned with
real-world implementations; 2) comprehensively review the latest LLM-based ATS
works; and 3) deliver an up-to-date survey of ATS, bridging the two-year gap in
the literature. To the best of our knowledge, this is the first survey to
specifically investigate LLM-based ATS methods.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 12:11:07 GMT"
}
] | 1,709,683,200,000 | [
[
"Jin",
"Hanlei",
""
],
[
"Zhang",
"Yang",
""
],
[
"Meng",
"Dan",
""
],
[
"Wang",
"Jun",
""
],
[
"Tan",
"Jinghua",
""
]
] |
2403.02914 | Hao Wu | Hao Wu, Haomin Wen, Guibin Zhang, Yutong Xia, Kai Wang, Yuxuan Liang,
Yu Zheng, Kun Wang | DynST: Dynamic Sparse Training for Resource-Constrained Spatio-Temporal
Forecasting | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The ever-increasing sensor service, though opening a precious path and
providing a deluge of earth system data for deep-learning-oriented earth
science, sadly introduce a daunting obstacle to their industrial level
deployment. Concretely, earth science systems rely heavily on the extensive
deployment of sensors, however, the data collection from sensors is constrained
by complex geographical and social factors, making it challenging to achieve
comprehensive coverage and uniform deployment. To alleviate the obstacle,
traditional approaches to sensor deployment utilize specific algorithms to
design and deploy sensors. These methods dynamically adjust the activation
times of sensors to optimize the detection process across each sub-region.
Regrettably, formulating an activation strategy generally based on historical
observations and geographic characteristics, which make the methods and
resultant models were neither simple nor practical. Worse still, the complex
technical design may ultimately lead to a model with weak generalizability. In
this paper, we introduce for the first time the concept of spatio-temporal data
dynamic sparse training and are committed to adaptively, dynamically filtering
important sensor distributions. To our knowledge, this is the first proposal
(termed DynST) of an industry-level deployment optimization concept at the data
level. However, due to the existence of the temporal dimension, pruning of
spatio-temporal data may lead to conflicts at different timestamps. To achieve
this goal, we employ dynamic merge technology, along with ingenious dimensional
mapping to mitigate potential impacts caused by the temporal aspect. During the
training process, DynST utilize iterative pruning and sparse training,
repeatedly identifying and dynamically removing sensor perception areas that
contribute the least to future predictions.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 12:31:24 GMT"
}
] | 1,709,683,200,000 | [
[
"Wu",
"Hao",
""
],
[
"Wen",
"Haomin",
""
],
[
"Zhang",
"Guibin",
""
],
[
"Xia",
"Yutong",
""
],
[
"Wang",
"Kai",
""
],
[
"Liang",
"Yuxuan",
""
],
[
"Zheng",
"Yu",
""
],
[
"Wang",
"Kun",
""
]
] |
2403.02962 | Zheng Li | Zheng Li and Xiang Chen and Xiaojun Wan | WikiTableEdit: A Benchmark for Table Editing by Natural Language
Instruction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Tabular data, as a crucial form of data representation, exists in diverse
formats on the Web. When confronted with complex and irregular tables, manual
modification becomes a laborious task. This paper investigates the performance
of Large Language Models (LLMs) in the context of table editing tasks. Existing
research mainly focuses on regular-shaped tables, wherein instructions are used
to generate code in SQL, Python, or Excel Office-script for manipulating the
tables. Nevertheless, editing tables with irregular structures, particularly
those containing merged cells spanning multiple rows, poses a challenge when
using code. To address this, we introduce the WikiTableEdit dataset. Leveraging
26,531 tables from the WikiSQL dataset, we automatically generate natural
language instructions for six distinct basic operations and the corresponding
outcomes, resulting in over 200,000 instances. Subsequently, we evaluate
several representative large language models on the WikiTableEdit dataset to
demonstrate the challenge of this task. The dataset will be released to the
community to promote related researches.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 13:33:12 GMT"
}
] | 1,709,683,200,000 | [
[
"Li",
"Zheng",
""
],
[
"Chen",
"Xiang",
""
],
[
"Wan",
"Xiaojun",
""
]
] |
2403.02993 | Wenyang Hu | Wenyang Hu, Yao Shu, Zongmin Yu, Zhaoxuan Wu, Xiangqiang Lin,
Zhongxiang Dai, See-Kiong Ng, Bryan Kian Hsiang Low | Localized Zeroth-Order Prompt Optimization | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The efficacy of large language models (LLMs) in understanding and generating
natural language has aroused a wide interest in developing prompt-based methods
to harness the power of black-box LLMs. Existing methodologies usually
prioritize a global optimization for finding the global optimum, which however
will perform poorly in certain tasks. This thus motivates us to re-think the
necessity of finding a global optimum in prompt optimization. To answer this,
we conduct a thorough empirical study on prompt optimization and draw two major
insights. Contrasting with the rarity of global optimum, local optima are
usually prevalent and well-performed, which can be more worthwhile for
efficient prompt optimization (Insight I). The choice of the input domain,
covering both the generation and the representation of prompts, affects the
identification of well-performing local optima (Insight II). Inspired by these
insights, we propose a novel algorithm, namely localized zeroth-order prompt
optimization (ZOPO), which incorporates a Neural Tangent Kernel-based derived
Gaussian process into standard zeroth-order optimization for an efficient
search of well-performing local optima in prompt optimization. Remarkably, ZOPO
outperforms existing baselines in terms of both the optimization performance
and the query efficiency, which we demonstrate through extensive experiments.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 14:18:15 GMT"
}
] | 1,709,683,200,000 | [
[
"Hu",
"Wenyang",
""
],
[
"Shu",
"Yao",
""
],
[
"Yu",
"Zongmin",
""
],
[
"Wu",
"Zhaoxuan",
""
],
[
"Lin",
"Xiangqiang",
""
],
[
"Dai",
"Zhongxiang",
""
],
[
"Ng",
"See-Kiong",
""
],
[
"Low",
"Bryan Kian Hsiang",
""
]
] |
2403.03008 | Hasan Abu-Rasheed | Hasan Abu-Rasheed, Christian Weber, Madjid Fathi | Knowledge Graphs as Context Sources for LLM-Based Explanations of
Learning Recommendations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the era of personalized education, the provision of comprehensible
explanations for learning recommendations is of a great value to enhance the
learner's understanding and engagement with the recommended learning content.
Large language models (LLMs) and generative AI in general have recently opened
new doors for generating human-like explanations, for and along learning
recommendations. However, their precision is still far away from acceptable in
a sensitive field like education. To harness the abilities of LLMs, while still
ensuring a high level of precision towards the intent of the learners, this
paper proposes an approach to utilize knowledge graphs (KG) as a source of
factual context, for LLM prompts, reducing the risk of model hallucinations,
and safeguarding against wrong or imprecise information, while maintaining an
application-intended learning context. We utilize the semantic relations in the
knowledge graph to offer curated knowledge about learning recommendations. With
domain-experts in the loop, we design the explanation as a textual template,
which is filled and completed by the LLM. Domain experts were integrated in the
prompt engineering phase as part of a study, to ensure that explanations
include information that is relevant to the learner. We evaluate our approach
quantitatively using Rouge-N and Rouge-L measures, as well as qualitatively
with experts and learners. Our results show an enhanced recall and precision of
the generated explanations compared to those generated solely by the GPT model,
with a greatly reduced risk of generating imprecise information in the final
learning explanation.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 14:41:12 GMT"
}
] | 1,709,683,200,000 | [
[
"Abu-Rasheed",
"Hasan",
""
],
[
"Weber",
"Christian",
""
],
[
"Fathi",
"Madjid",
""
]
] |
2403.03017 | Haochen Shi | Haochen Shi, Zhiyuan Sun, Xingdi Yuan, Marc-Alexandre C\^ot\'e, Bang
Liu | OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied
Instruction Following | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Embodied Instruction Following (EIF) is a crucial task in embodied learning,
requiring agents to interact with their environment through egocentric
observations to fulfill natural language instructions. Recent advancements have
seen a surge in employing large language models (LLMs) within a
framework-centric approach to enhance performance in embodied learning tasks,
including EIF. Despite these efforts, there exists a lack of a unified
understanding regarding the impact of various components-ranging from visual
perception to action execution-on task performance. To address this gap, we
introduce OPEx, a comprehensive framework that delineates the core components
essential for solving embodied learning tasks: Observer, Planner, and Executor.
Through extensive evaluations, we provide a deep analysis of how each component
influences EIF task performance. Furthermore, we innovate within this space by
deploying a multi-agent dialogue strategy on a TextWorld counterpart, further
enhancing task performance. Our findings reveal that LLM-centric design
markedly improves EIF outcomes, identify visual perception and low-level action
execution as critical bottlenecks, and demonstrate that augmenting LLMs with a
multi-agent framework further elevates performance.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 14:53:53 GMT"
}
] | 1,709,683,200,000 | [
[
"Shi",
"Haochen",
""
],
[
"Sun",
"Zhiyuan",
""
],
[
"Yuan",
"Xingdi",
""
],
[
"Côté",
"Marc-Alexandre",
""
],
[
"Liu",
"Bang",
""
]
] |
2403.03165 | Jingxiao Tian | Yaqian Qi, Yuan Feng, Xiangxiang Wang, Hanzhe Li, Jingxiao Tian | Leveraging Federated Learning and Edge Computing for Recommendation
Systems within Cloud Computing Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To enable large-scale and efficient deployment of artificial intelligence
(AI), the combination of AI and edge computing has spawned Edge Intelligence,
which leverages the computing and communication capabilities of end devices and
edge servers to process data closer to where it is generated. A key technology
for edge intelligence is the privacy-protecting machine learning paradigm known
as Federated Learning (FL), which enables data owners to train models without
having to transfer raw data to third-party servers. However, FL networks are
expected to involve thousands of heterogeneous distributed devices. As a
result, communication efficiency remains a key bottleneck. To reduce node
failures and device exits, a Hierarchical Federated Learning (HFL) framework is
proposed, where a designated cluster leader supports the data owner through
intermediate model aggregation. Therefore, based on the improvement of edge
server resource utilization, this paper can effectively make up for the
limitation of cache capacity. In order to mitigate the impact of soft clicks on
the quality of user experience (QoE), the authors model the user QoE as a
comprehensive system cost. To solve the formulaic problem, the authors propose
a decentralized caching algorithm with federated deep reinforcement learning
(DRL) and federated learning (FL), where multiple agents learn and make
decisions independently
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 17:58:26 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Mar 2024 05:46:39 GMT"
}
] | 1,710,374,400,000 | [
[
"Qi",
"Yaqian",
""
],
[
"Feng",
"Yuan",
""
],
[
"Wang",
"Xiangxiang",
""
],
[
"Li",
"Hanzhe",
""
],
[
"Tian",
"Jingxiao",
""
]
] |
2403.03176 | Michael Katz | Michael Katz, Junkyu Lee, Shirin Sohrabi | Unifying and Certifying Top-Quality Planning | To appear at ICAPS 2024 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The growing utilization of planning tools in practical scenarios has sparked
an interest in generating multiple high-quality plans. Consequently, a range of
computational problems under the general umbrella of top-quality planning were
introduced over a short time period, each with its own definition. In this
work, we show that the existing definitions can be unified into one, based on a
dominance relation. The different computational problems, therefore, simply
correspond to different dominance relations. Given the unified definition, we
can now certify the top-quality of the solutions, leveraging existing
certification of unsolvability and optimality. We show that task
transformations found in the existing literature can be employed for the
efficient certification of various top-quality planning problems and propose a
novel transformation to efficiently certify loopless top-quality planning.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 18:13:18 GMT"
}
] | 1,709,683,200,000 | [
[
"Katz",
"Michael",
""
],
[
"Lee",
"Junkyu",
""
],
[
"Sohrabi",
"Shirin",
""
]
] |
2403.03186 | Zongqing Lu | Weihao Tan, Ziluo Ding, Wentao Zhang, Boyu Li, Bohan Zhou, Junpeng
Yue, Haochong Xia, Jiechuan Jiang, Longtao Zheng, Xinrun Xu, Yifei Bi,
Pengjie Gu, Xinrun Wang, B\"orje F. Karlsson, Bo An, Zongqing Lu | Towards General Computer Control: A Multimodal Agent for Red Dead
Redemption II as a Case Study | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite the success in specific tasks and scenarios, existing foundation
agents, empowered by large models (LMs) and advanced tools, still cannot
generalize to different scenarios, mainly due to dramatic differences in the
observations and actions across scenarios. In this work, we propose the General
Computer Control (GCC) setting: building foundation agents that can master any
computer task by taking only screen images (and possibly audio) of the computer
as input, and producing keyboard and mouse operations as output, similar to
human-computer interaction. The main challenges of achieving GCC are: 1) the
multimodal observations for decision-making, 2) the requirements of accurate
control of keyboard and mouse, 3) the need for long-term memory and reasoning,
and 4) the abilities of efficient exploration and self-improvement. To target
GCC, we introduce Cradle, an agent framework with six main modules, including:
1) information gathering to extract multi-modality information, 2)
self-reflection to rethink past experiences, 3) task inference to choose the
best next task, 4) skill curation for generating and updating relevant skills
for given tasks, 5) action planning to generate specific operations for
keyboard and mouse control, and 6) memory for storage and retrieval of past
experiences and known skills. To demonstrate the capabilities of generalization
and self-improvement of Cradle, we deploy it in the complex AAA game Red Dead
Redemption II, serving as a preliminary attempt towards GCC with a challenging
target. To our best knowledge, our work is the first to enable LMM-based agents
to follow the main storyline and finish real missions in complex AAA games,
with minimal reliance on prior knowledge or resources. The project website is
at https://baai-agents.github.io/Cradle/.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 18:22:29 GMT"
},
{
"version": "v2",
"created": "Thu, 7 Mar 2024 14:41:56 GMT"
}
] | 1,709,856,000,000 | [
[
"Tan",
"Weihao",
""
],
[
"Ding",
"Ziluo",
""
],
[
"Zhang",
"Wentao",
""
],
[
"Li",
"Boyu",
""
],
[
"Zhou",
"Bohan",
""
],
[
"Yue",
"Junpeng",
""
],
[
"Xia",
"Haochong",
""
],
[
"Jiang",
"Jiechuan",
""
],
[
"Zheng",
"Longtao",
""
],
[
"Xu",
"Xinrun",
""
],
[
"Bi",
"Yifei",
""
],
[
"Gu",
"Pengjie",
""
],
[
"Wang",
"Xinrun",
""
],
[
"Karlsson",
"Börje F.",
""
],
[
"An",
"Bo",
""
],
[
"Lu",
"Zongqing",
""
]
] |
2403.03203 | Marjan Alirezaie | Savitha Sam Abraham and Marjan Alirezaie and Luc De Raedt | CLEVR-POC: Reasoning-Intensive Visual Question Answering in Partially
Observable Environments | 17 pages, 10 images, Accepted at LREC-COLING 2024 - The 2024 Joint
International Conference on Computational Linguistics, Language Resources and
Evaluation | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The integration of learning and reasoning is high on the research agenda in
AI. Nevertheless, there is only a little attention to use existing background
knowledge for reasoning about partially observed scenes to answer questions
about the scene. Yet, we as humans use such knowledge frequently to infer
plausible answers to visual questions (by eliminating all inconsistent ones).
Such knowledge often comes in the form of constraints about objects and it
tends to be highly domain or environment-specific. We contribute a novel
benchmark called CLEVR-POC for reasoning-intensive visual question answering
(VQA) in partially observable environments under constraints. In CLEVR-POC,
knowledge in the form of logical constraints needs to be leveraged to generate
plausible answers to questions about a hidden object in a given partial scene.
For instance, if one has the knowledge that all cups are colored either red,
green or blue and that there is only one green cup, it becomes possible to
deduce the color of an occluded cup as either red or blue, provided that all
other cups, including the green one, are observed. Through experiments, we
observe that the low performance of pre-trained vision language models like
CLIP (~ 22%) and a large language model (LLM) like GPT-4 (~ 46%) on CLEVR-POC
ascertains the necessity for frameworks that can handle reasoning-intensive
tasks where environment-specific background knowledge is available and crucial.
Furthermore, our demonstration illustrates that a neuro-symbolic model, which
integrates an LLM like GPT-4 with a visual perception network and a formal
logical reasoner, exhibits exceptional performance on CLEVR-POC.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 18:41:37 GMT"
}
] | 1,709,683,200,000 | [
[
"Abraham",
"Savitha Sam",
""
],
[
"Alirezaie",
"Marjan",
""
],
[
"De Raedt",
"Luc",
""
]
] |
2403.03288 | Jianqiu Zhang | Jianqiiu Zhang | Should We Fear Large Language Models? A Structural Analysis of the Human
Reasoning System for Elucidating LLM Capabilities and Risks Through the Lens
of Heidegger's Philosophy | 39 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the rapidly evolving field of Large Language Models (LLMs), there is a
critical need to thoroughly analyze their capabilities and risks. Central to
our investigation are two novel elements. Firstly, it is the innovative
parallels between the statistical patterns of word relationships within LLMs
and Martin Heidegger's concepts of "ready-to-hand" and "present-at-hand," which
encapsulate the utilitarian and scientific altitudes humans employ in
interacting with the world. This comparison lays the groundwork for positioning
LLMs as the digital counterpart to the Faculty of Verbal Knowledge, shedding
light on their capacity to emulate certain facets of human reasoning. Secondly,
a structural analysis of human reasoning, viewed through Heidegger's notion of
truth as "unconcealment" is conducted This foundational principle enables us to
map out the inputs and outputs of the reasoning system and divide reasoning
into four distinct categories. Respective cognitive faculties are delineated,
allowing us to place LLMs within the broader schema of human reasoning, thus
clarifying their strengths and inherent limitations. Our findings reveal that
while LLMs possess the capability for Direct Explicative Reasoning and Pseudo
Rational Reasoning, they fall short in authentic rational reasoning and have no
creative reasoning capabilities, due to the current lack of many analogous AI
models such as the Faculty of Judgement. The potential and risks of LLMs when
they are augmented with other AI technologies are also evaluated. The results
indicate that although LLMs have achieved proficiency in some reasoning
abilities, the aspiration to match or exceed human intellectual capabilities is
yet unattained. This research not only enriches our comprehension of LLMs but
also propels forward the discourse on AI's potential and its bounds, paving the
way for future explorations into AI's evolving landscape.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 19:40:53 GMT"
}
] | 1,709,769,600,000 | [
[
"Zhang",
"Jianqiiu",
""
]
] |
2403.03293 | Rrubaa Panchendrarajan | Anjalee De Silva, Janaka L. Wijekoon, Rashini Liyanarachchi, Rrubaa
Panchendrarajan, Weranga Rajapaksha | AI Insights: A Case Study on Utilizing ChatGPT Intelligence for Research
Paper Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper discusses the effectiveness of leveraging Chatbot: Generative
Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research
papers for effective writing of scientific literature surveys. The study
selected the \textit{Application of Artificial Intelligence in Breast Cancer
Treatment} as the research topic. Research papers related to this topic were
collected from three major publication databases Google Scholar, Pubmed, and
Scopus. ChatGPT models were used to identify the category, scope, and relevant
information from the research papers for automatic identification of relevant
papers related to Breast Cancer Treatment (BCT), organization of papers
according to scope, and identification of key information for survey paper
writing. Evaluations performed using ground truth data annotated using subject
experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research
paper categories and 50\% of the papers were correctly identified by GPT-4 for
their scopes. Further, the results demonstrate that GPT-4 can generate reasons
for its decisions with an average of 27\% new words, and 67\% of the reasons
given by the model were completely agreeable to the subject experts.
| [
{
"version": "v1",
"created": "Tue, 5 Mar 2024 19:47:57 GMT"
}
] | 1,709,769,600,000 | [
[
"De Silva",
"Anjalee",
""
],
[
"Wijekoon",
"Janaka L.",
""
],
[
"Liyanarachchi",
"Rashini",
""
],
[
"Panchendrarajan",
"Rrubaa",
""
],
[
"Rajapaksha",
"Weranga",
""
]
] |
2403.03382 | Guangyao Chen | Guangyao Chen, Peixi Peng, Yangru Huang, Mengyue Geng, Yonghong Tian | Adaptive Discovering and Merging for Incremental Novel Class Discovery | AAAI 2024. arXiv admin note: text overlap with arXiv:2207.08605 by
other authors | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | One important desideratum of lifelong learning aims to discover novel classes
from unlabelled data in a continuous manner. The central challenge is twofold:
discovering and learning novel classes while mitigating the issue of
catastrophic forgetting of established knowledge. To this end, we introduce a
new paradigm called Adaptive Discovering and Merging (ADM) to discover novel
categories adaptively in the incremental stage and integrate novel knowledge
into the model without affecting the original knowledge. To discover novel
classes adaptively, we decouple representation learning and novel class
discovery, and use Triple Comparison (TC) and Probability Regularization (PR)
to constrain the probability discrepancy and diversity for adaptive category
assignment. To merge the learned novel knowledge adaptively, we propose a
hybrid structure with base and novel branches named Adaptive Model Merging
(AMM), which reduces the interference of the novel branch on the old classes to
preserve the previous knowledge, and merges the novel branch to the base model
without performance loss and parameter growth. Extensive experiments on several
datasets show that ADM significantly outperforms existing class-incremental
Novel Class Discovery (class-iNCD) approaches. Moreover, our AMM also benefits
the class-incremental Learning (class-IL) task by alleviating the catastrophic
forgetting problem.
| [
{
"version": "v1",
"created": "Wed, 6 Mar 2024 00:17:03 GMT"
}
] | 1,709,769,600,000 | [
[
"Chen",
"Guangyao",
""
],
[
"Peng",
"Peixi",
""
],
[
"Huang",
"Yangru",
""
],
[
"Geng",
"Mengyue",
""
],
[
"Tian",
"Yonghong",
""
]
] |
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