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2307.06126 | Dimosthenis Tsouros | Dimos Tsouros, Senne Berden, Tias Guns | Guided Bottom-Up Interactive Constraint Acquisition | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Constraint Acquisition (CA) systems can be used to assist in the modeling of
constraint satisfaction problems. In (inter)active CA, the system is given a
set of candidate constraints and posts queries to the user with the goal of
finding the right constraints among the candidates. Current interactive CA
algorithms suffer from at least two major bottlenecks. First, in order to
converge, they require a large number of queries to be asked to the user.
Second, they cannot handle large sets of candidate constraints, since these
lead to large waiting times for the user. For this reason, the user must have
fairly precise knowledge about what constraints the system should consider. In
this paper, we alleviate these bottlenecks by presenting two novel methods that
improve the efficiency of CA. First, we introduce a bottom-up approach named
GrowAcq that reduces the maximum waiting time for the user and allows the
system to handle much larger sets of candidate constraints. It also reduces the
total number of queries for problems in which the target constraint network is
not sparse. Second, we propose a probability-based method to guide query
generation and show that it can significantly reduce the number of queries
required to converge. We also propose a new technique that allows the use of
openly accessible CP solvers in query generation, removing the dependency of
existing methods on less well-maintained custom solvers that are not publicly
available. Experimental results show that our proposed methods outperform
state-of-the-art CA methods, reducing the number of queries by up to 60%. Our
methods work well even in cases where the set of candidate constraints is 50
times larger than the ones commonly used in the literature.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2023 12:25:37 GMT"
}
] | 1,689,206,400,000 | [
[
"Tsouros",
"Dimos",
""
],
[
"Berden",
"Senne",
""
],
[
"Guns",
"Tias",
""
]
] |
2307.06399 | Aadesh Neupane | Aadesh Neupane, Eric G Mercer, Michael A. Goodrich | Designing Behavior Trees from Goal-Oriented LTLf Formulas | Accepted as "Most Visionary Paper" in Autonomous Robots and
Multirobot Systems (ARMS) 2023 workshop affiliated with the 22nd
International Conference on Autonomous Agents and Multiagent Systems (AAMAS
2023) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Temporal logic can be used to formally specify autonomous agent goals, but
synthesizing planners that guarantee goal satisfaction can be computationally
prohibitive. This paper shows how to turn goals specified using a subset of
finite trace Linear Temporal Logic (LTL) into a behavior tree (BT) that
guarantees that successful traces satisfy the LTL goal. Useful LTL formulas for
achievement goals can be derived using achievement-oriented task mission
grammars, leading to missions made up of tasks combined using LTL operators.
Constructing BTs from LTL formulas leads to a relaxed behavior synthesis
problem in which a wide range of planners can implement the action nodes in the
BT. Importantly, any successful trace induced by the planners satisfies the
corresponding LTL formula. The usefulness of the approach is demonstrated in
two ways: a) exploring the alignment between two planners and LTL goals, and b)
solving a sequential key-door problem for a Fetch robot.
| [
{
"version": "v1",
"created": "Wed, 12 Jul 2023 18:29:37 GMT"
},
{
"version": "v2",
"created": "Tue, 19 Dec 2023 16:11:05 GMT"
}
] | 1,703,030,400,000 | [
[
"Neupane",
"Aadesh",
""
],
[
"Mercer",
"Eric G",
""
],
[
"Goodrich",
"Michael A.",
""
]
] |
2307.07059 | Jundong Liu | Yuanhang Zhang and Jundong Liu | Vertex-based Networks to Accelerate Path Planning Algorithms | Accepted to IEEE Workshop on Machine Learning for Signal Processing
(MLSP'2023) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Path planning plays a crucial role in various autonomy applications, and RRT*
is one of the leading solutions in this field. In this paper, we propose the
utilization of vertex-based networks to enhance the sampling process of RRT*,
leading to more efficient path planning. Our approach focuses on critical
vertices along the optimal paths, which provide essential yet sparser
abstractions of the paths. We employ focal loss to address the associated data
imbalance issue, and explore different masking configurations to determine
practical tradeoffs in system performance. Through experiments conducted on
randomly generated floor maps, our solutions demonstrate significant speed
improvements, achieving over a 400% enhancement compared to the baseline model.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2023 20:56:46 GMT"
}
] | 1,689,552,000,000 | [
[
"Zhang",
"Yuanhang",
""
],
[
"Liu",
"Jundong",
""
]
] |
2307.07515 | Johannes Jaeger | Johannes Jaeger | Artificial intelligence is algorithmic mimicry: why artificial "agents"
are not (and won't be) proper agents | null | JNeurons, Behavior, Data Analysis, and Theory 1-21 (2024) | 10.51628/001c.94404 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | What is the prospect of developing artificial general intelligence (AGI)? I
investigate this question by systematically comparing living and algorithmic
systems, with a special focus on the notion of "agency." There are three
fundamental differences to consider: (1) Living systems are autopoietic, that
is, self-manufacturing, and therefore able to set their own intrinsic goals,
while algorithms exist in a computational environment with target functions
that are both provided by an external agent. (2) Living systems are embodied in
the sense that there is no separation between their symbolic and physical
aspects, while algorithms run on computational architectures that maximally
isolate software from hardware. (3) Living systems experience a large world, in
which most problems are ill-defined (and not all definable), while algorithms
exist in a small world, in which all problems are well-defined. These three
differences imply that living and algorithmic systems have very different
capabilities and limitations. In particular, it is extremely unlikely that true
AGI (beyond mere mimicry) can be developed in the current algorithmic framework
of AI research. Consequently, discussions about the proper development and
deployment of algorithmic tools should be shaped around the dangers and
opportunities of current narrow AI, not the extremely unlikely prospect of the
emergence of true agency in artificial systems.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 19:25:09 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Jul 2023 08:18:47 GMT"
},
{
"version": "v3",
"created": "Thu, 1 Feb 2024 11:28:23 GMT"
},
{
"version": "v4",
"created": "Thu, 22 Feb 2024 08:48:13 GMT"
}
] | 1,709,424,000,000 | [
[
"Jaeger",
"Johannes",
""
]
] |
2307.07517 | Riichiro Mizoguchi | Riichiro Mizoguchi | Causing is Achieving -- A solution to the problem of causation | 13 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | From the standpoint of applied ontology, the problem of understanding and
modeling causation has been recently challenged on the premise that causation
is real. As a consequence, the following three results were obtained: (1)
causation can be understood via the notion of systemic function; (2) any cause
can be decomposed using only four subfunctions, namely Achieves, Prevents,
Allows, and Disallows; and (3) the last three subfunctions can be defined in
terms of Achieves alone. It follows that the essence of causation lies in a
single function, namely Achieves. It remains to elucidate the nature of the
Achieves function, which has been elaborated only partially in the previous
work. In this paper, we first discuss a couple of underlying policies in the
above-mentioned causal theory since these are useful in the discussion, then
summarize the results obtained in the former paper, and finally reveal the
nature of Achieves giving a complete solution to the problem of what causation
is.
| [
{
"version": "v1",
"created": "Sat, 1 Jul 2023 09:01:49 GMT"
}
] | 1,689,638,400,000 | [
[
"Mizoguchi",
"Riichiro",
""
]
] |
2307.07524 | Tianyi Miao | Tianyi Miao | Reducing Causality to Functions with Structural Models | 47 pages, submitted to The British Journal for the Philosophy of
Science | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The precise definition of causality is currently an open problem in
philosophy and statistics. We believe causality should be defined as functions
(in mathematics) that map causes to effects. We propose a reductive definition
of causality based on Structural Functional Model (SFM). Using delta
compression and contrastive forward inference, SFM can produce causal
utterances like "X causes Y" and "X is the cause of Y" that match our
intuitions. We compile a dataset of causal scenarios and use SFM in all of
them. SFM is compatible with but not reducible to probability theory. We also
compare SFM with other theories of causation and apply SFM to downstream
problems like free will, causal explanation, and mental causation.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2023 02:47:33 GMT"
}
] | 1,689,638,400,000 | [
[
"Miao",
"Tianyi",
""
]
] |
2307.07636 | Judy Hanwen Shen | Omer Reingold, Judy Hanwen Shen, Aditi Talati | Dissenting Explanations: Leveraging Disagreement to Reduce Model
Overreliance | V2: AAAI 2024 V1: AI & HCI Workshop at ICML 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | While explainability is a desirable characteristic of increasingly complex
black-box models, modern explanation methods have been shown to be inconsistent
and contradictory. The semantics of explanations is not always fully understood
- to what extent do explanations "explain" a decision and to what extent do
they merely advocate for a decision? Can we help humans gain insights from
explanations accompanying correct predictions and not over-rely on incorrect
predictions advocated for by explanations? With this perspective in mind, we
introduce the notion of dissenting explanations: conflicting predictions with
accompanying explanations. We first explore the advantage of dissenting
explanations in the setting of model multiplicity, where multiple models with
similar performance may have different predictions. In such cases, providing
dissenting explanations could be done by invoking the explanations of
disagreeing models. Through a pilot study, we demonstrate that dissenting
explanations reduce overreliance on model predictions, without reducing overall
accuracy. Motivated by the utility of dissenting explanations we present both
global and local methods for their generation.
| [
{
"version": "v1",
"created": "Fri, 14 Jul 2023 21:27:00 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Feb 2024 17:47:23 GMT"
}
] | 1,708,646,400,000 | [
[
"Reingold",
"Omer",
""
],
[
"Shen",
"Judy Hanwen",
""
],
[
"Talati",
"Aditi",
""
]
] |
2307.07734 | Fan Shi | Fan Shi, Bin Li, Xiangyang Xue | Abstracting Concept-Changing Rules for Solving Raven's Progressive
Matrix Problems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The abstract visual reasoning ability in human intelligence benefits
discovering underlying rules in the novel environment. Raven's Progressive
Matrix (RPM) is a classic test to realize such ability in machine intelligence
by selecting from candidates. Recent studies suggest that solving RPM in an
answer-generation way boosts a more in-depth understanding of rules. However,
existing generative solvers cannot discover the global concept-changing rules
without auxiliary supervision (e.g., rule annotations and distractors in
candidate sets). To this end, we propose a deep latent variable model for
Concept-changing Rule ABstraction (CRAB) by learning interpretable concepts and
parsing concept-changing rules in the latent space. With the iterative learning
process, CRAB can automatically abstract global rules shared on the dataset on
each concept and form the learnable prior knowledge of global rules. CRAB
outperforms the baselines trained without auxiliary supervision in the
arbitrary-position answer generation task and achieves comparable and even
higher accuracy than the compared models trained with auxiliary supervision.
Finally, we conduct experiments to illustrate the interpretability of CRAB in
concept learning, answer selection, and global rule abstraction.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 07:16:38 GMT"
}
] | 1,689,638,400,000 | [
[
"Shi",
"Fan",
""
],
[
"Li",
"Bin",
""
],
[
"Xue",
"Xiangyang",
""
]
] |
2307.07764 | Bastian Pfeifer | Bastian Pfeifer, Mateusz Krzyzinski, Hubert Baniecki, Anna Saranti,
Andreas Holzinger, Przemyslaw Biecek | Explaining and visualizing black-box models through counterfactual paths | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainable AI (XAI) is an increasingly important area of machine learning
research, which aims to make black-box models transparent and interpretable. In
this paper, we propose a novel approach to XAI that uses the so-called
counterfactual paths generated by conditional permutations of features. The
algorithm measures feature importance by identifying sequential permutations of
features that most influence changes in model predictions. It is particularly
suitable for generating explanations based on counterfactual paths in knowledge
graphs incorporating domain knowledge. Counterfactual paths introduce an
additional graph dimension to current XAI methods in both explaining and
visualizing black-box models. Experiments with synthetic and medical data
demonstrate the practical applicability of our approach.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 10:16:51 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Jul 2023 13:00:52 GMT"
},
{
"version": "v3",
"created": "Tue, 1 Aug 2023 07:01:31 GMT"
}
] | 1,690,934,400,000 | [
[
"Pfeifer",
"Bastian",
""
],
[
"Krzyzinski",
"Mateusz",
""
],
[
"Baniecki",
"Hubert",
""
],
[
"Saranti",
"Anna",
""
],
[
"Holzinger",
"Andreas",
""
],
[
"Biecek",
"Przemyslaw",
""
]
] |
2307.07876 | Douglas Antunes Tesch | Douglas Tesch, Leonardo Rosa Amado, Felipe Meneguzzi | Online Goal Recognition in Discrete and Continuous Domains Using a
Vectorial Representation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | While recent work on online goal recognition efficiently infers goals under
low observability, comparatively less work focuses on online goal recognition
that works in both discrete and continuous domains. Online goal recognition
approaches often rely on repeated calls to the planner at each new observation,
incurring high computational costs. Recognizing goals online in continuous
space quickly and reliably is critical for any trajectory planning problem
since the real physical world is fast-moving, e.g. robot applications. We
develop an efficient method for goal recognition that relies either on a single
call to the planner for each possible goal in discrete domains or a simplified
motion model that reduces the computational burden in continuous ones. The
resulting approach performs the online component of recognition orders of
magnitude faster than the current state of the art, making it the first online
method effectively usable for robotics applications that require sub-second
recognition.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 19:27:38 GMT"
}
] | 1,689,638,400,000 | [
[
"Tesch",
"Douglas",
""
],
[
"Amado",
"Leonardo Rosa",
""
],
[
"Meneguzzi",
"Felipe",
""
]
] |
2307.07909 | Yao Wei | Yao Wei and Yanchao Sun and Ruijie Zheng and Sai Vemprala and Rogerio
Bonatti and Shuhang Chen and Ratnesh Madaan and Zhongjie Ba and Ashish Kapoor
and Shuang Ma | Is Imitation All You Need? Generalized Decision-Making with Dual-Phase
Training | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We introduce DualMind, a generalist agent designed to tackle various
decision-making tasks that addresses challenges posed by current methods, such
as overfitting behaviors and dependence on task-specific fine-tuning. DualMind
uses a novel "Dual-phase" training strategy that emulates how humans learn to
act in the world. The model first learns fundamental common knowledge through a
self-supervised objective tailored for control tasks and then learns how to
make decisions based on different contexts through imitating behaviors
conditioned on given prompts. DualMind can handle tasks across domains, scenes,
and embodiments using just a single set of model weights and can execute
zero-shot prompting without requiring task-specific fine-tuning. We evaluate
DualMind on MetaWorld and Habitat through extensive experiments and demonstrate
its superior generalizability compared to previous techniques, outperforming
other generalist agents by over 50$\%$ and 70$\%$ on Habitat and MetaWorld,
respectively. On the 45 tasks in MetaWorld, DualMind achieves over 30 tasks at
a 90$\%$ success rate.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2023 00:34:12 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 16:05:00 GMT"
},
{
"version": "v3",
"created": "Mon, 9 Oct 2023 08:07:00 GMT"
}
] | 1,696,896,000,000 | [
[
"Wei",
"Yao",
""
],
[
"Sun",
"Yanchao",
""
],
[
"Zheng",
"Ruijie",
""
],
[
"Vemprala",
"Sai",
""
],
[
"Bonatti",
"Rogerio",
""
],
[
"Chen",
"Shuhang",
""
],
[
"Madaan",
"Ratnesh",
""
],
[
"Ba",
"Zhongjie",
""
],
[
"Kapoor",
"Ashish",
""
],
[
"Ma",
"Shuang",
""
]
] |
2307.07919 | Xiaohuan Pei | Xiaohuan Pei, Yanxi Li, Minjing Dong, Chang Xu | Neural Architecture Retrieval | ICLR 2024 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | With the increasing number of new neural architecture designs and substantial
existing neural architectures, it becomes difficult for the researchers to
situate their contributions compared with existing neural architectures or
establish the connections between their designs and other relevant ones. To
discover similar neural architectures in an efficient and automatic manner, we
define a new problem Neural Architecture Retrieval which retrieves a set of
existing neural architectures which have similar designs to the query neural
architecture. Existing graph pre-training strategies cannot address the
computational graph in neural architectures due to the graph size and motifs.
To fulfill this potential, we propose to divide the graph into motifs which are
used to rebuild the macro graph to tackle these issues, and introduce
multi-level contrastive learning to achieve accurate graph representation
learning. Extensive evaluations on both human-designed and synthesized neural
architectures demonstrate the superiority of our algorithm. Such a dataset
which contains 12k real-world network architectures, as well as their
embedding, is built for neural architecture retrieval.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2023 01:56:41 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Mar 2024 01:13:32 GMT"
}
] | 1,710,806,400,000 | [
[
"Pei",
"Xiaohuan",
""
],
[
"Li",
"Yanxi",
""
],
[
"Dong",
"Minjing",
""
],
[
"Xu",
"Chang",
""
]
] |
2307.08024 | Chengmin Zhou | Chengmin Zhou, Chao Wang, Haseeb Hassan, Himat Shah, Bingding Huang,
Pasi Fr\"anti | Bayesian inference for data-efficient, explainable, and safe robotic
motion planning: A review | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Bayesian inference has many advantages in robotic motion planning over four
perspectives: The uncertainty quantification of the policy, safety (risk-aware)
and optimum guarantees of robot motions, data-efficiency in training of
reinforcement learning, and reducing the sim2real gap when the robot is applied
to real-world tasks. However, the application of Bayesian inference in robotic
motion planning is lagging behind the comprehensive theory of Bayesian
inference. Further, there are no comprehensive reviews to summarize the
progress of Bayesian inference to give researchers a systematic understanding
in robotic motion planning. This paper first provides the probabilistic
theories of Bayesian inference which are the preliminary of Bayesian inference
for complex cases. Second, the Bayesian estimation is given to estimate the
posterior of policies or unknown functions which are used to compute the
policy. Third, the classical model-based Bayesian RL and model-free Bayesian RL
algorithms for robotic motion planning are summarized, while these algorithms
in complex cases are also analyzed. Fourth, the analysis of Bayesian inference
in inverse RL is given to infer the reward functions in a data-efficient
manner. Fifth, we systematically present the hybridization of Bayesian
inference and RL which is a promising direction to improve the convergence of
RL for better motion planning. Sixth, given the Bayesian inference, we present
the interpretable and safe robotic motion plannings which are the hot research
topic recently. Finally, all algorithms reviewed in this paper are summarized
analytically as the knowledge graphs, and the future of Bayesian inference for
robotic motion planning is also discussed, to pave the way for data-efficient,
explainable, and safe robotic motion planning strategies for practical
applications.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2023 12:29:27 GMT"
}
] | 1,689,638,400,000 | [
[
"Zhou",
"Chengmin",
""
],
[
"Wang",
"Chao",
""
],
[
"Hassan",
"Haseeb",
""
],
[
"Shah",
"Himat",
""
],
[
"Huang",
"Bingding",
""
],
[
"Fränti",
"Pasi",
""
]
] |
2307.08087 | Jaime De Miguel Rodr\'iguez | Jaime de Miguel-Rodriguez, Fernando Sancho-Caparrini | A Recursive Bateson-Inspired Model for the Generation of Semantic Formal
Concepts from Spatial Sensory Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Neural-symbolic approaches to machine learning incorporate the advantages
from both connectionist and symbolic methods. Typically, these models employ a
first module based on a neural architecture to extract features from complex
data. Then, these features are processed as symbols by a symbolic engine that
provides reasoning, concept structures, composability, better generalization
and out-of-distribution learning among other possibilities. However, neural
approaches to the grounding of symbols in sensory data, albeit powerful, still
require heavy training and tedious labeling for the most part. This paper
presents a new symbolic-only method for the generation of hierarchical concept
structures from complex spatial sensory data. The approach is based on
Bateson's notion of difference as the key to the genesis of an idea or a
concept. Following his suggestion, the model extracts atomic features from raw
data by computing elemental sequential comparisons in a stream of multivariate
numerical values. Higher-level constructs are built from these features by
subjecting them to further comparisons in a recursive process. At any stage in
the recursion, a concept structure may be obtained from these constructs and
features by means of Formal Concept Analysis. Results show that the model is
able to produce fairly rich yet human-readable conceptual representations
without training. Additionally, the concept structures obtained through the
model (i) present high composability, which potentially enables the generation
of 'unseen' concepts, (ii) allow formal reasoning, and (iii) have inherent
abilities for generalization and out-of-distribution learning. Consequently,
this method may offer an interesting angle to current neural-symbolic research.
Future work is required to develop a training methodology so that the model can
be tested against a larger dataset.
| [
{
"version": "v1",
"created": "Sun, 16 Jul 2023 15:59:13 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 15:08:44 GMT"
}
] | 1,689,724,800,000 | [
[
"de Miguel-Rodriguez",
"Jaime",
""
],
[
"Sancho-Caparrini",
"Fernando",
""
]
] |
2307.08242 | Anubhav Singh | Anubhav Singh, Miquel Ramirez, Nir Lipovetzky, and Peter J. Stuckey | Lifted Sequential Planning with Lazy Constraint Generation Solvers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper studies the possibilities made open by the use of Lazy Clause
Generation (LCG) based approaches to Constraint Programming (CP) for tackling
sequential classical planning. We propose a novel CP model based on seminal
ideas on so-called lifted causal encodings for planning as satisfiability, that
does not require grounding, as choosing groundings for functions and action
schemas becomes an integral part of the problem of designing valid plans. This
encoding does not require encoding frame axioms, and does not explicitly
represent states as decision variables for every plan step. We also present a
propagator procedure that illustrates the possibilities of LCG to widen the
kind of inference methods considered to be feasible in planning as (iterated)
CSP solving. We test encodings and propagators over classic IPC and recently
proposed benchmarks for lifted planning, and report that for planning problem
instances requiring fewer plan steps our methods compare very well with the
state-of-the-art in optimal sequential planning.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 04:54:58 GMT"
}
] | 1,689,638,400,000 | [
[
"Singh",
"Anubhav",
""
],
[
"Ramirez",
"Miquel",
""
],
[
"Lipovetzky",
"Nir",
""
],
[
"Stuckey",
"Peter J.",
""
]
] |
2307.08262 | Minwoo Seong | Minwoo Seong, Jeongseok Oh, SeungJun Kim | MuLMINet: Multi-Layer Multi-Input Transformer Network with Weighted Loss | 4 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The increasing use of artificial intelligence (AI) technology in turn-based
sports, such as badminton, has sparked significant interest in evaluating
strategies through the analysis of match video data. Predicting future shots
based on past ones plays a vital role in coaching and strategic planning. In
this study, we present a Multi-Layer Multi-Input Transformer Network (MuLMINet)
that leverages professional badminton player match data to accurately predict
future shot types and area coordinates. Our approach resulted in achieving the
runner-up (2nd place) in the IJCAI CoachAI Badminton Challenge 2023, Track 2.
To facilitate further research, we have made our code publicly accessible
online, contributing to the broader research community's knowledge and
advancements in the field of AI-assisted sports analysis.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 06:10:03 GMT"
},
{
"version": "v2",
"created": "Sun, 7 Apr 2024 15:47:41 GMT"
}
] | 1,712,620,800,000 | [
[
"Seong",
"Minwoo",
""
],
[
"Oh",
"Jeongseok",
""
],
[
"Kim",
"SeungJun",
""
]
] |
2307.08304 | Alessandro Antonucci | Marco Zaffalon and Alessandro Antonucci and Rafael Caba\~nas and David
Huber and Dario Azzimonti | Efficient Computation of Counterfactual Bounds | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We assume to be given structural equations over discrete variables inducing a
directed acyclic graph, namely, a structural causal model, together with data
about its internal nodes. The question we want to answer is how we can compute
bounds for partially identifiable counterfactual queries from such an input. We
start by giving a map from structural casual models to credal networks. This
allows us to compute exact counterfactual bounds via algorithms for credal nets
on a subclass of structural causal models. Exact computation is going to be
inefficient in general given that, as we show, causal inference is NP-hard even
on polytrees. We target then approximate bounds via a causal EM scheme. We
evaluate their accuracy by providing credible intervals on the quality of the
approximation; we show through a synthetic benchmark that the EM scheme
delivers accurate results in a fair number of runs. In the course of the
discussion, we also point out what seems to be a neglected limitation to the
trending idea that counterfactual bounds can be computed without knowledge of
the structural equations. We also present a real case study on palliative care
to show how our algorithms can readily be used for practical purposes.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 07:59:47 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Nov 2023 14:06:27 GMT"
},
{
"version": "v3",
"created": "Mon, 4 Dec 2023 14:30:19 GMT"
}
] | 1,701,734,400,000 | [
[
"Zaffalon",
"Marco",
""
],
[
"Antonucci",
"Alessandro",
""
],
[
"Cabañas",
"Rafael",
""
],
[
"Huber",
"David",
""
],
[
"Azzimonti",
"Dario",
""
]
] |
2307.08401 | Stavros Orfanoudakis | Stavros Orfanoudakis, Georgios Chalkiadakis | A Novel Multiagent Flexibility Aggregation Framework | 12 pages, 9 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The increasing number of Distributed Energy Resources (DERs) in the emerging
Smart Grid, has created an imminent need for intelligent multiagent frameworks
able to utilize these assets efficiently. In this paper, we propose a novel DER
aggregation framework, encompassing a multiagent architecture and various types
of mechanisms for the effective management and efficient integration of DERs in
the Grid. One critical component of our architecture is the Local Flexibility
Estimators (LFEs) agents, which are key for offloading the Aggregator from
serious or resource-intensive responsibilities -- such as addressing privacy
concerns and predicting the accuracy of DER statements regarding their offered
demand response services. The proposed framework allows the formation of
efficient LFE cooperatives. To this end, we developed and deployed a variety of
cooperative member selection mechanisms, including (a) scoring rules, and (b)
(deep) reinforcement learning. We use data from the well-known PowerTAC
simulator to systematically evaluate our framework. Our experiments verify its
effectiveness for incorporating heterogeneous DERs into the Grid in an
efficient manner. In particular, when using the well-known probabilistic
prediction accuracy-incentivizing CRPS scoring rule as a selection mechanism,
our framework results in increased average payments for participants, when
compared with traditional commercial aggregators.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 11:36:15 GMT"
}
] | 1,689,638,400,000 | [
[
"Orfanoudakis",
"Stavros",
""
],
[
"Chalkiadakis",
"Georgios",
""
]
] |
2307.08424 | Rongke Liu | Rongke Liu, Dong Wang, Yizhi Ren, Zhen Wang, Kaitian Guo, Qianqian
Qin, Xiaolei Liu | Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion
Model | 16 pages, 9 figures, 8 tables | null | 10.1109/TIFS.2024.3372815 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model inversion attacks (MIAs) aim to recover private data from inaccessible
training sets of deep learning models, posing a privacy threat. MIAs primarily
focus on the white-box scenario where attackers have full access to the model's
structure and parameters. However, practical applications are usually in
black-box scenarios or label-only scenarios, i.e., the attackers can only
obtain the output confidence vectors or labels by accessing the model.
Therefore, the attack models in existing MIAs are difficult to effectively
train with the knowledge of the target model, resulting in sub-optimal attacks.
To the best of our knowledge, we pioneer the research of a powerful and
practical attack model in the label-only scenario.
In this paper, we develop a novel MIA method, leveraging a conditional
diffusion model (CDM) to recover representative samples under the target label
from the training set. Two techniques are introduced: selecting an auxiliary
dataset relevant to the target model task and using predicted labels as
conditions to guide training CDM; and inputting target label, pre-defined
guidance strength, and random noise into the trained attack model to generate
and correct multiple results for final selection. This method is evaluated
using Learned Perceptual Image Patch Similarity as a new metric and as a
judgment basis for deciding the values of hyper-parameters. Experimental
results show that this method can generate similar and accurate samples to the
target label, outperforming generators of previous approaches.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 12:14:24 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Jul 2023 01:21:15 GMT"
},
{
"version": "v3",
"created": "Wed, 6 Mar 2024 05:00:06 GMT"
}
] | 1,709,769,600,000 | [
[
"Liu",
"Rongke",
""
],
[
"Wang",
"Dong",
""
],
[
"Ren",
"Yizhi",
""
],
[
"Wang",
"Zhen",
""
],
[
"Guo",
"Kaitian",
""
],
[
"Qin",
"Qianqian",
""
],
[
"Liu",
"Xiaolei",
""
]
] |
2307.08430 | Chao Li | Chao Li, Zijie Guo, Qiuting He, Hao Xu and Kun He | Long-range Meta-path Search on Large-scale Heterogeneous Graphs | 17 pages, 6 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Utilizing long-range dependency, though extensively studied in homogeneous
graphs, has not been well investigated on heterogeneous graphs. Addressing this
research gap presents two major challenges. The first is to alleviate
computational costs while endeavoring to leverage as much effective information
as possible in the presence of heterogeneity. The second involves overcoming
the well-known over-smoothing issue occurring in various graph neural networks.
To this end, we investigate the importance of different meta-paths and
introduce an automatic framework for utilizing long-range dependency on
heterogeneous graphs, denoted as Long-range Meta-path Search through
Progressive Sampling (LMSPS). Specifically, we develop a search space with all
meta-paths related to the target node type. By employing a progressive sampling
algorithm, LMSPS dynamically shrinks the search space with hop-independent time
complexity. Utilizing a sampling evaluation strategy as the guidance, LMSPS
conducts a specialized and effective meta-path selection. Subsequently, only
effective meta-paths are employed for retraining to reduce costs and overcome
the over-smoothing issue. Extensive experiments on various heterogeneous
datasets demonstrate that LMSPS discovers effective long-range meta-paths and
outperforms the state-of-the-art. Besides, it ranks top-1 on the leaderboards
of \texttt{ogbn-mag} in Open Graph Benchmark. Our code is available at
https://github.com/JHL-HUST/LDMLP.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 12:20:07 GMT"
},
{
"version": "v2",
"created": "Fri, 29 Sep 2023 13:54:29 GMT"
},
{
"version": "v3",
"created": "Wed, 22 Nov 2023 07:53:36 GMT"
},
{
"version": "v4",
"created": "Sat, 3 Feb 2024 09:14:48 GMT"
}
] | 1,707,177,600,000 | [
[
"Li",
"Chao",
""
],
[
"Guo",
"Zijie",
""
],
[
"He",
"Qiuting",
""
],
[
"Xu",
"Hao",
""
],
[
"He",
"Kun",
""
]
] |
2307.08461 | Anahid Jalali | Anahid Jalali, Anita Graser, Clemens Heistracher | Towards eXplainable AI for Mobility Data Science | 4 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents our ongoing work towards XAI for Mobility Data Science
applications, focusing on explainable models that can learn from dense
trajectory data, such as GPS tracks of vehicles and vessels using temporal
graph neural networks (GNNs) and counterfactuals. We review the existing GeoXAI
studies, argue the need for comprehensible explanations with human-centered
approaches, and outline a research path toward XAI for Mobility Data Science.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 13:06:33 GMT"
},
{
"version": "v2",
"created": "Wed, 6 Sep 2023 15:13:10 GMT"
},
{
"version": "v3",
"created": "Thu, 7 Sep 2023 06:50:38 GMT"
}
] | 1,694,131,200,000 | [
[
"Jalali",
"Anahid",
""
],
[
"Graser",
"Anita",
""
],
[
"Heistracher",
"Clemens",
""
]
] |
2307.08484 | Stefan Buijsman | Stefan Buijsman | Navigating Fairness Measures and Trade-Offs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In order to monitor and prevent bias in AI systems we can use a wide range of
(statistical) fairness measures. However, it is mathematically impossible to
optimize for all of these measures at the same time. In addition, optimizing a
fairness measure often greatly reduces the accuracy of the system (Kozodoi et
al, 2022). As a result, we need a substantive theory that informs us how to
make these decisions and for what reasons. I show that by using Rawls' notion
of justice as fairness, we can create a basis for navigating fairness measures
and the accuracy trade-off. In particular, this leads to a principled choice
focusing on both the most vulnerable groups and the type of fairness measure
that has the biggest impact on that group. This also helps to close part of the
gap between philosophical accounts of distributive justice and the fairness
literature that has been observed (Kuppler et al, 2021) and to operationalise
the value of fairness.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 13:45:47 GMT"
}
] | 1,689,638,400,000 | [
[
"Buijsman",
"Stefan",
""
]
] |
2307.08721 | Ying Zhang | Kai Peng, Ying Zhang, Shuai Ling, Zhaoru Ke, Haipeng Zhang | Where Did the President Visit Last Week? Detecting Celebrity Trips from
News Articles | Accepted to ICWSM 2024, 12 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Celebrities' whereabouts are of pervasive importance. For instance, where
politicians go, how often they visit, and who they meet, come with profound
geopolitical and economic implications. Although news articles contain travel
information of celebrities, it is not possible to perform large-scale and
network-wise analysis due to the lack of automatic itinerary detection tools.
To design such tools, we have to overcome difficulties from the heterogeneity
among news articles: 1)One single article can be noisy, with irrelevant people
and locations, especially when the articles are long. 2)Though it may be
helpful if we consider multiple articles together to determine a particular
trip, the key semantics are still scattered across different articles
intertwined with various noises, making it hard to aggregate them effectively.
3)Over 20% of the articles refer to the celebrities' trips indirectly, instead
of using the exact celebrity names or location names, leading to large portions
of trips escaping regular detecting algorithms. We model text content across
articles related to each candidate location as a graph to better associate
essential information and cancel out the noises. Besides, we design a special
pooling layer based on attention mechanism and node similarity, reducing
irrelevant information from longer articles. To make up the missing information
resulted from indirect mentions, we construct knowledge sub-graphs for named
entities (person, organization, facility, etc.). Specifically, we dynamically
update embeddings of event entities like the G7 summit from news descriptions
since the properties (date and location) of the event change each time, which
is not captured by the pre-trained event representations. The proposed CeleTrip
jointly trains these modules, which outperforms all baseline models and
achieves 82.53% in the F1 metric.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 05:37:49 GMT"
},
{
"version": "v2",
"created": "Mon, 9 Oct 2023 04:40:10 GMT"
}
] | 1,696,896,000,000 | [
[
"Peng",
"Kai",
""
],
[
"Zhang",
"Ying",
""
],
[
"Ling",
"Shuai",
""
],
[
"Ke",
"Zhaoru",
""
],
[
"Zhang",
"Haipeng",
""
]
] |
2307.08774 | Lily Xu | Lily Xu, Esther Rolf, Sara Beery, Joseph R. Bennett, Tanya
Berger-Wolf, Tanya Birch, Elizabeth Bondi-Kelly, Justin Brashares, Melissa
Chapman, Anthony Corso, Andrew Davies, Nikhil Garg, Angela Gaylard, Robert
Heilmayr, Hannah Kerner, Konstantin Klemmer, Vipin Kumar, Lester Mackey,
Claire Monteleoni, Paul Moorcroft, Jonathan Palmer, Andrew Perrault, David
Thau, Milind Tambe | Reflections from the Workshop on AI-Assisted Decision Making for
Conservation | Co-authored by participants from the October 2022 workshop:
https://crcs.seas.harvard.edu/conservation-workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this white paper, we synthesize key points made during presentations and
discussions from the AI-Assisted Decision Making for Conservation workshop,
hosted by the Center for Research on Computation and Society at Harvard
University on October 20-21, 2022. We identify key open research questions in
resource allocation, planning, and interventions for biodiversity conservation,
highlighting conservation challenges that not only require AI solutions, but
also require novel methodological advances. In addition to providing a summary
of the workshop talks and discussions, we hope this document serves as a
call-to-action to orient the expansion of algorithmic decision-making
approaches to prioritize real-world conservation challenges, through
collaborative efforts of ecologists, conservation decision-makers, and AI
researchers.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 18:41:03 GMT"
}
] | 1,689,724,800,000 | [
[
"Xu",
"Lily",
""
],
[
"Rolf",
"Esther",
""
],
[
"Beery",
"Sara",
""
],
[
"Bennett",
"Joseph R.",
""
],
[
"Berger-Wolf",
"Tanya",
""
],
[
"Birch",
"Tanya",
""
],
[
"Bondi-Kelly",
"Elizabeth",
""
],
[
"Brashares",
"Justin",
""
],
[
"Chapman",
"Melissa",
""
],
[
"Corso",
"Anthony",
""
],
[
"Davies",
"Andrew",
""
],
[
"Garg",
"Nikhil",
""
],
[
"Gaylard",
"Angela",
""
],
[
"Heilmayr",
"Robert",
""
],
[
"Kerner",
"Hannah",
""
],
[
"Klemmer",
"Konstantin",
""
],
[
"Kumar",
"Vipin",
""
],
[
"Mackey",
"Lester",
""
],
[
"Monteleoni",
"Claire",
""
],
[
"Moorcroft",
"Paul",
""
],
[
"Palmer",
"Jonathan",
""
],
[
"Perrault",
"Andrew",
""
],
[
"Thau",
"David",
""
],
[
"Tambe",
"Milind",
""
]
] |
2307.08775 | Yining Lu | Yining Lu and Haoping Yu and Daniel Khashabi | GEAR: Augmenting Language Models with Generalizable and Efficient Tool
Resolution | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Augmenting large language models (LLM) to use external tools enhances their
performance across a variety of tasks. However, prior works over-rely on
task-specific demonstration of tool use that limits their generalizability and
computational cost due to making many calls to large-scale LLMs. We introduce
GEAR, a computationally efficient query-tool grounding algorithm that is
generalizable to various tasks that require tool use while not relying on
task-specific demonstrations. GEAR achieves better efficiency by delegating
tool grounding and execution to small language models (SLM) and LLM,
respectively; while leveraging semantic and pattern-based evaluation at both
question and answer levels for generalizable tool grounding. We evaluate GEAR
on 14 datasets across 6 downstream tasks, demonstrating its strong
generalizability to novel tasks, tools and different SLMs. Despite offering
more efficiency, GEAR achieves higher precision in tool grounding compared to
prior strategies using LLM prompting, thus improving downstream accuracy at a
reduced computational cost. For example, we demonstrate that GEAR-augmented
GPT-J and GPT-3 outperform counterpart tool-augmented baselines because of
better tool use.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 18:42:05 GMT"
},
{
"version": "v2",
"created": "Wed, 31 Jan 2024 04:11:42 GMT"
}
] | 1,706,745,600,000 | [
[
"Lu",
"Yining",
""
],
[
"Yu",
"Haoping",
""
],
[
"Khashabi",
"Daniel",
""
]
] |
2307.08876 | Ted Selker PhD | Ted Selker | AI for the Generation and Testing of Ideas Towards an AI Supported
Knowledge Development Environment | 8 pages, 21 references | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | New systems employ Machine Learning to sift through large knowledge sources,
creating flexible Large Language Models. These models discern context and
predict sequential information in various communication forms. Generative AI,
leveraging Transformers, generates textual or visual outputs mimicking human
responses. It proposes one or multiple contextually feasible solutions for a
user to contemplate. However, generative AI does not currently support
traceability of ideas, a useful feature provided by search engines indicating
origin of information. The narrative style of generative AI has gained positive
reception. People learn from stories. Yet, early ChatGPT efforts had difficulty
with truth, reference, calculations, and aspects like accurate maps. Current
capabilities of referencing locations and linking to apps seem to be better
catered by the link-centric search methods we've used for two decades.
Deploying truly believable solutions extends beyond simulating contextual
relevance as done by generative AI. Combining the creativity of generative AI
with the provenance of internet sources in hybrid scenarios could enhance
internet usage. Generative AI, viewed as drafts, stimulates thinking, offering
alternative ideas for final versions or actions. Scenarios for information
requests are considered. We discuss how generative AI can boost idea generation
by eliminating human bias. We also describe how search can verify facts, logic,
and context. The user evaluates these generated ideas for selection and usage.
This paper introduces a system for knowledge workers, Generate And Search Test,
enabling individuals to efficiently create solutions previously requiring top
collaborations of experts.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 22:17:40 GMT"
}
] | 1,689,724,800,000 | [
[
"Selker",
"Ted",
""
]
] |
2307.09042 | Xuena Wang | Xuena Wang, Xueting Li, Zi Yin, Yue Wu and Liu Jia | Emotional Intelligence of Large Language Models | 36 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) have demonstrated remarkable abilities across
numerous disciplines, primarily assessed through tasks in language generation,
knowledge utilization, and complex reasoning. However, their alignment with
human emotions and values, which is critical for real-world applications, has
not been systematically evaluated. Here, we assessed LLMs' Emotional
Intelligence (EI), encompassing emotion recognition, interpretation, and
understanding, which is necessary for effective communication and social
interactions. Specifically, we first developed a novel psychometric assessment
focusing on Emotion Understanding (EU), a core component of EI, suitable for
both humans and LLMs. This test requires evaluating complex emotions (e.g.,
surprised, joyful, puzzled, proud) in realistic scenarios (e.g., despite
feeling underperformed, John surprisingly achieved a top score). With a
reference frame constructed from over 500 adults, we tested a variety of
mainstream LLMs. Most achieved above-average EQ scores, with GPT-4 exceeding
89% of human participants with an EQ of 117. Interestingly, a multivariate
pattern analysis revealed that some LLMs apparently did not reply on the
human-like mechanism to achieve human-level performance, as their
representational patterns were qualitatively distinct from humans. In addition,
we discussed the impact of factors such as model size, training method, and
architecture on LLMs' EQ. In summary, our study presents one of the first
psychometric evaluations of the human-like characteristics of LLMs, which may
shed light on the future development of LLMs aiming for both high intellectual
and emotional intelligence. Project website:
https://emotional-intelligence.github.io/
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2023 07:49:38 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Jul 2023 06:29:07 GMT"
}
] | 1,690,761,600,000 | [
[
"Wang",
"Xuena",
""
],
[
"Li",
"Xueting",
""
],
[
"Yin",
"Zi",
""
],
[
"Wu",
"Yue",
""
],
[
"Jia",
"Liu",
""
]
] |
2307.09047 | Pierre Senellart | Shrey Mishra, Antoine Gauquier, Pierre Senellart | Multimodal Machine Learning for Extraction of Theorems and Proofs in the
Scientific Literature | 15 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Scholarly articles in mathematical fields feature mathematical statements
such as theorems, propositions, etc., as well as their proofs. Extracting them
from the PDF representation of the articles requires understanding of
scientific text along with visual and font-based indicators. We pose this
problem as a multimodal classification problem using text, font features, and
bitmap image rendering of the PDF as different modalities. In this paper we
propose a multimodal machine learning approach for extraction of theorem-like
environments and proofs, based on late fusion of features extracted by
individual unimodal classifiers, taking into account the sequential succession
of blocks in the document. For the text modality, we pretrain a new language
model on a 11 GB scientific corpus; experiments shows similar performance for
our task than a model (RoBERTa) pretrained on 160 GB, with faster convergence
while requiring much less fine-tuning data. Font-based information relies on
training a 128-cell LSTM on the sequence of font names and sizes within each
block. Bitmap renderings are dealt with using an EfficientNetv2 deep network
tuned to classify each image block. Finally, a simple CRF-based approach uses
the features of the multimodal model along with information on block sequences.
Experimental results show the benefits of using a multimodal approach vs any
single modality, as well as major performance improvements using the CRF
modeling of block sequences.
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2023 07:59:37 GMT"
}
] | 1,689,724,800,000 | [
[
"Mishra",
"Shrey",
""
],
[
"Gauquier",
"Antoine",
""
],
[
"Senellart",
"Pierre",
""
]
] |
2307.09051 | Xiufeng Huang | Xiufeng Huang, Sheng Zhou | QMNet: Importance-Aware Message Exchange for Decentralized Multi-Agent
Reinforcement Learning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To improve the performance of multi-agent reinforcement learning under the
constraint of wireless resources, we propose a message importance metric and
design an importance-aware scheduling policy to effectively exchange messages.
The key insight is spending the precious communication resources on important
messages. The message importance depends not only on the messages themselves,
but also on the needs of agents who receive them. Accordingly, we propose a
query-message-based architecture, called QMNet. Agents generate queries and
messages with the environment observation. Sharing queries can help calculate
message importance. Exchanging messages can help agents cooperate better.
Besides, we exploit the message importance to deal with random access
collisions in decentralized systems. Furthermore, a message prediction
mechanism is proposed to compensate for messages that are not transmitted.
Finally, we evaluate the proposed schemes in a traffic junction environment,
where only a fraction of agents can send messages due to limited wireless
resources. Results show that QMNet can extract valuable information to
guarantee the system performance even when only $30\%$ of agents can share
messages. By exploiting message prediction, the system can further save $40\%$
of wireless resources. The importance-aware decentralized multi-access
mechanism can effectively avoid collisions, achieving almost the same
performance as centralized scheduling.
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2023 08:04:27 GMT"
}
] | 1,689,724,800,000 | [
[
"Huang",
"Xiufeng",
""
],
[
"Zhou",
"Sheng",
""
]
] |
2307.09141 | Mikhail Shirokikh | Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Sergey Nikolenko | Machine Learning for SAT: Restricted Heuristics and New Graph
Representations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Boolean satisfiability (SAT) is a fundamental NP-complete problem with many
applications, including automated planning and scheduling. To solve large
instances, SAT solvers have to rely on heuristics, e.g., choosing a branching
variable in DPLL and CDCL solvers. Such heuristics can be improved with machine
learning (ML) models; they can reduce the number of steps but usually hinder
the running time because useful models are relatively large and slow. We
suggest the strategy of making a few initial steps with a trained ML model and
then releasing control to classical heuristics; this simplifies cold start for
SAT solving and can decrease both the number of steps and overall runtime, but
requires a separate decision of when to release control to the solver.
Moreover, we introduce a modification of Graph-Q-SAT tailored to SAT problems
converted from other domains, e.g., open shop scheduling problems. We validate
the feasibility of our approach with random and industrial SAT problems.
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2023 10:46:28 GMT"
}
] | 1,689,724,800,000 | [
[
"Shirokikh",
"Mikhail",
""
],
[
"Shenbin",
"Ilya",
""
],
[
"Alekseev",
"Anton",
""
],
[
"Nikolenko",
"Sergey",
""
]
] |
2307.09166 | Joohyung Lee | Joohyung Lee, Vladimir Lifschitz, Ravi Palla | Safe Formulas in the General Theory of Stable Models | 16 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Safe first-order formulas generalize the concept of a safe rule, which plays
an important role in the design of answer set solvers. We show that any safe
sentence is equivalent, in a certain sense, to the result of its grounding --
to the variable-free sentence obtained from it by replacing all quantifiers
with multiple conjunctions and disjunctions. It follows that a safe sentence
and the result of its grounding have the same stable models, and that the
stable models of a safe sentence can be characterized by a formula of a simple
syntactic form.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 08:09:28 GMT"
}
] | 1,689,724,800,000 | [
[
"Lee",
"Joohyung",
""
],
[
"Lifschitz",
"Vladimir",
""
],
[
"Palla",
"Ravi",
""
]
] |
2307.09168 | Joohyung Lee | Martin Gebser, Joohyung Lee, Yuliya Lierler | Elementary Sets for Logic Programs | 6 pages. AAAI 2006, 244-249. arXiv admin note: substantial text
overlap with arXiv:1012.5847 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | By introducing the concepts of a loop and a loop formula, Lin and Zhao showed
that the answer sets of a nondisjunctive logic program are exactly the models
of its Clark's completion that satisfy the loop formulas of all loops.
Recently, Gebser and Schaub showed that the Lin-Zhao theorem remains correct
even if we restrict loop formulas to a special class of loops called
``elementary loops.'' In this paper, we simplify and generalize the notion of
an elementary loop, and clarify its role. We propose the notion of an
elementary set, which is almost equivalent to the notion of an elementary loop
for nondisjunctive programs, but is simpler, and, unlike elementary loops, can
be extended to disjunctive programs without producing unintuitive results. We
show that the maximal unfounded elementary sets for the ``relevant'' part of a
program are exactly the minimal sets among the nonempty unfounded sets. We also
present a graph-theoretic characterization of elementary sets for
nondisjunctive programs, which is simpler than the one proposed in (Gebser &
Schaub 2005). Unlike the case of nondisjunctive programs, we show that the
problem of deciding an elementary set is coNP-complete for disjunctive
programs.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 08:00:46 GMT"
}
] | 1,689,724,800,000 | [
[
"Gebser",
"Martin",
""
],
[
"Lee",
"Joohyung",
""
],
[
"Lierler",
"Yuliya",
""
]
] |
2307.09296 | Kaiwei Zhang | Kaiwei Zhang, Junchi Yu, Haichao Shi, Jian Liang, Xiao-Yu Zhang | Rumor Detection with Diverse Counterfactual Evidence | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The growth in social media has exacerbated the threat of fake news to
individuals and communities. This draws increasing attention to developing
efficient and timely rumor detection methods. The prevailing approaches resort
to graph neural networks (GNNs) to exploit the post-propagation patterns of the
rumor-spreading process. However, these methods lack inherent interpretation of
rumor detection due to the black-box nature of GNNs. Moreover, these methods
suffer from less robust results as they employ all the propagation patterns for
rumor detection. In this paper, we address the above issues with the proposed
Diverse Counterfactual Evidence framework for Rumor Detection (DCE-RD). Our
intuition is to exploit the diverse counterfactual evidence of an event graph
to serve as multi-view interpretations, which are further aggregated for robust
rumor detection results. Specifically, our method first designs a subgraph
generation strategy to efficiently generate different subgraphs of the event
graph. We constrain the removal of these subgraphs to cause the change in rumor
detection results. Thus, these subgraphs naturally serve as counterfactual
evidence for rumor detection. To achieve multi-view interpretation, we design a
diversity loss inspired by Determinantal Point Processes (DPP) to encourage
diversity among the counterfactual evidence. A GNN-based rumor detection model
further aggregates the diverse counterfactual evidence discovered by the
proposed DCE-RD to achieve interpretable and robust rumor detection results.
Extensive experiments on two real-world datasets show the superior performance
of our method. Our code is available at https://github.com/Vicinity111/DCE-RD.
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2023 14:37:23 GMT"
}
] | 1,689,724,800,000 | [
[
"Zhang",
"Kaiwei",
""
],
[
"Yu",
"Junchi",
""
],
[
"Shi",
"Haichao",
""
],
[
"Liang",
"Jian",
""
],
[
"Zhang",
"Xiao-Yu",
""
]
] |
2307.09564 | Julian Parsert | Julian Parsert and Elizabeth Polgreen | Reinforcement Learning and Data-Generation for Syntax-Guided Synthesis | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Program synthesis is the task of automatically generating code based on a
specification. In Syntax-Guided Synthesis (SyGuS) this specification is a
combination of a syntactic template and a logical formula, and the result is
guaranteed to satisfy both.
We present a reinforcement-learning guided algorithm for SyGuS which uses
Monte-Carlo Tree Search (MCTS) to search the space of candidate solutions. Our
algorithm learns policy and value functions which, combined with the upper
confidence bound for trees, allow it to balance exploration and exploitation. A
common challenge in applying machine learning approaches to syntax-guided
synthesis is the scarcity of training data. To address this, we present a
method for automatically generating training data for SyGuS based on
anti-unification of existing first-order satisfiability problems, which we use
to train our MCTS policy. We implement and evaluate this setup and demonstrate
that learned policy and value improve the synthesis performance over a baseline
by over 26 percentage points in the training and testing sets. Our tool
outperforms state-of-the-art tool cvc5 on the training set and performs
comparably in terms of the total number of problems solved on the testing set
(solving 23% of the benchmarks on which cvc5 fails). We make our data set
publicly available, to enable further application of machine learning methods
to the SyGuS problem.
| [
{
"version": "v1",
"created": "Thu, 13 Jul 2023 11:30:50 GMT"
},
{
"version": "v2",
"created": "Fri, 5 Jan 2024 13:07:10 GMT"
}
] | 1,704,672,000,000 | [
[
"Parsert",
"Julian",
""
],
[
"Polgreen",
"Elizabeth",
""
]
] |
2307.09673 | Mallika Mainali | Mallika Mainali, Rosina O Weber | What's meant by explainable model: A Scoping Review | 8 pages, 2 figures. This paper was accepted at IJCAI 2023 workshop on
Explainable Artificial Intelligence (XAI) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We often see the term explainable in the titles of papers that describe
applications based on artificial intelligence (AI). However, the literature in
explainable artificial intelligence (XAI) indicates that explanations in XAI
are application- and domain-specific, hence requiring evaluation whenever they
are employed to explain a model that makes decisions for a specific application
problem. Additionally, the literature reveals that the performance of post-hoc
methods, particularly feature attribution methods, varies substantially hinting
that they do not represent a solution to AI explainability. Therefore, when
using XAI methods, the quality and suitability of their information outputs
should be evaluated within the specific application. For these reasons, we used
a scoping review methodology to investigate papers that apply AI models and
adopt methods to generate post-hoc explanations while referring to said models
as explainable. This paper investigates whether the term explainable model is
adopted by authors under the assumption that incorporating a post-hoc XAI
method suffices to characterize a model as explainable. To inspect this
problem, our review analyzes whether these papers conducted evaluations. We
found that 81% of the application papers that refer to their approaches as an
explainable model do not conduct any form of evaluation on the XAI method they
used.
| [
{
"version": "v1",
"created": "Tue, 18 Jul 2023 22:55:04 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2023 15:17:38 GMT"
},
{
"version": "v3",
"created": "Tue, 29 Aug 2023 13:30:12 GMT"
}
] | 1,693,353,600,000 | [
[
"Mainali",
"Mallika",
""
],
[
"Weber",
"Rosina O",
""
]
] |
2307.09711 | Chanyoung Park | Soohyun Park, Haemin Lee, Chanyoung Park, Soyi Jung, Minseok Choi,
Joongheon Kim | Two Tales of Platoon Intelligence for Autonomous Mobility Control:
Enabling Deep Learning Recipes | 8 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents the deep learning-based recent achievements to resolve
the problem of autonomous mobility control and efficient resource management of
autonomous vehicles and UAVs, i.e., (i) multi-agent reinforcement learning
(MARL), and (ii) neural Myerson auction. Representatively, communication
network (CommNet), which is one of the most popular MARL algorithms, is
introduced to enable multiple agents to take actions in a distributed manner
for their shared goals by training all agents' states and actions in a single
neural network. Moreover, the neural Myerson auction guarantees trustfulness
among multiple agents as well as achieves the optimal revenue of highly dynamic
systems. Therefore, we survey the recent studies on autonomous mobility control
based on MARL and neural Myerson auction. Furthermore, we emphasize that
integration of MARL and neural Myerson auction is expected to be critical for
efficient and trustful autonomous mobility services.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 01:46:38 GMT"
}
] | 1,689,811,200,000 | [
[
"Park",
"Soohyun",
""
],
[
"Lee",
"Haemin",
""
],
[
"Park",
"Chanyoung",
""
],
[
"Jung",
"Soyi",
""
],
[
"Choi",
"Minseok",
""
],
[
"Kim",
"Joongheon",
""
]
] |
2307.09777 | Shuo Huang | Shuo Huang, Chengpeng Hu, Julian Togelius, Jialin Liu | Generating Redstone Style Cities in Minecraft | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Procedurally generating cities in Minecraft provides players more diverse
scenarios and could help understand and improve the design of cities in other
digital worlds and the real world. This paper presents a city generator that
was submitted as an entry to the 2023 Edition of Minecraft Settlement
Generation Competition for Minecraft. The generation procedure is composed of
six main steps, namely vegetation clearing, terrain reshaping, building layout
generation, route planning, streetlight placement, and wall construction. Three
algorithms, including a heuristic-based algorithm, an evolving layout
algorithm, and a random one are applied to generate the building layout, thus
determining where to place different redstone style buildings, and tested by
generating cities on random maps in limited time. Experimental results show
that the heuristic-based algorithm is capable of finding an acceptable building
layout faster for flat maps, while the evolving layout algorithm performs
better in evolving layout for rugged maps. A user study is conducted to compare
our generator with outstanding entries of the competition's 2022 edition using
the competition's evaluation criteria and shows that our generator performs
well in the adaptation and functionality criteria
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 06:36:01 GMT"
}
] | 1,689,811,200,000 | [
[
"Huang",
"Shuo",
""
],
[
"Hu",
"Chengpeng",
""
],
[
"Togelius",
"Julian",
""
],
[
"Liu",
"Jialin",
""
]
] |
2307.09831 | Junhong Xiang | Junhong Xiang, Jingmin Zhang and Zhixiong Nan | A Fast and Map-Free Model for Trajectory Prediction in Traffics | 7 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To handle the two shortcomings of existing methods, (i)nearly all models rely
on high-definition (HD) maps, yet the map information is not always available
in real traffic scenes and HD map-building is expensive and time-consuming and
(ii) existing models usually focus on improving prediction accuracy at the
expense of reducing computing efficiency, yet the efficiency is crucial for
various real applications, this paper proposes an efficient trajectory
prediction model that is not dependent on traffic maps. The core idea of our
model is encoding single-agent's spatial-temporal information in the first
stage and exploring multi-agents' spatial-temporal interactions in the second
stage. By comprehensively utilizing attention mechanism, LSTM, graph
convolution network and temporal transformer in the two stages, our model is
able to learn rich dynamic and interaction information of all agents. Our model
achieves the highest performance when comparing with existing map-free methods
and also exceeds most map-based state-of-the-art methods on the Argoverse
dataset. In addition, our model also exhibits a faster inference speed than the
baseline methods.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 08:36:31 GMT"
}
] | 1,699,920,000,000 | [
[
"Xiang",
"Junhong",
""
],
[
"Zhang",
"Jingmin",
""
],
[
"Nan",
"Zhixiong",
""
]
] |
2307.09858 | Longfeng Wu | Longfeng Wu, Bowen Lei, Dongkuan Xu, Dawei Zhou | Towards Reliable Rare Category Analysis on Graphs via Individual
Calibration | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rare categories abound in a number of real-world networks and play a pivotal
role in a variety of high-stakes applications, including financial fraud
detection, network intrusion detection, and rare disease diagnosis. Rare
category analysis (RCA) refers to the task of detecting, characterizing, and
comprehending the behaviors of minority classes in a highly-imbalanced data
distribution. While the vast majority of existing work on RCA has focused on
improving the prediction performance, a few fundamental research questions
heretofore have received little attention and are less explored: How confident
or uncertain is a prediction model in rare category analysis? How can we
quantify the uncertainty in the learning process and enable reliable rare
category analysis?
To answer these questions, we start by investigating miscalibration in
existing RCA methods. Empirical results reveal that state-of-the-art RCA
methods are mainly over-confident in predicting minority classes and
under-confident in predicting majority classes. Motivated by the observation,
we propose a novel individual calibration framework, named CALIRARE, for
alleviating the unique challenges of RCA, thus enabling reliable rare category
analysis. In particular, to quantify the uncertainties in RCA, we develop a
node-level uncertainty quantification algorithm to model the overlapping
support regions with high uncertainty; to handle the rarity of minority classes
in miscalibration calculation, we generalize the distribution-based calibration
metric to the instance level and propose the first individual calibration
measurement on graphs named Expected Individual Calibration Error (EICE). We
perform extensive experimental evaluations on real-world datasets, including
rare category characterization and model calibration tasks, which demonstrate
the significance of our proposed framework.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 09:38:52 GMT"
}
] | 1,689,811,200,000 | [
[
"Wu",
"Longfeng",
""
],
[
"Lei",
"Bowen",
""
],
[
"Xu",
"Dongkuan",
""
],
[
"Zhou",
"Dawei",
""
]
] |
2307.09878 | Antti Keurulainen | Antti Keurulainen, Isak Westerlund, Oskar Keurulainen, Andrew Howes | Amortised Experimental Design and Parameter Estimation for User Models
of Pointing | null | Proceedings of the 2023 CHI Conference on Human Factors in
Computing Systems (CHI '23), April 23--28, 2023, Hamburg, Germany | 10.1145/3544548.3581483 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | User models play an important role in interaction design, supporting
automation of interaction design choices. In order to do so, model parameters
must be estimated from user data. While very large amounts of user data are
sometimes required, recent research has shown how experiments can be designed
so as to gather data and infer parameters as efficiently as possible, thereby
minimising the data requirement. In the current article, we investigate a
variant of these methods that amortises the computational cost of designing
experiments by training a policy for choosing experimental designs with
simulated participants. Our solution learns which experiments provide the most
useful data for parameter estimation by interacting with in-silico agents
sampled from the model space thereby using synthetic data rather than vast
amounts of human data. The approach is demonstrated for three progressively
complex models of pointing.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 10:17:35 GMT"
}
] | 1,689,811,200,000 | [
[
"Keurulainen",
"Antti",
""
],
[
"Westerlund",
"Isak",
""
],
[
"Keurulainen",
"Oskar",
""
],
[
"Howes",
"Andrew",
""
]
] |
2307.09891 | Antti Keurulainen | Antti Keurulainen, Isak Westerlund, Oskar Keurulainen, Andrew Howes | Amortised Design Optimization for Item Response Theory | Artificial Intelligence in Education. AIED 2023. Communications in
Computer and Information Science, vol 1831. Springer, Cham | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Item Response Theory (IRT) is a well known method for assessing responses
from humans in education and psychology. In education, IRT is used to infer
student abilities and characteristics of test items from student responses.
Interactions with students are expensive, calling for methods that efficiently
gather information for inferring student abilities. Methods based on Optimal
Experimental Design (OED) are computationally costly, making them inapplicable
for interactive applications. In response, we propose incorporating amortised
experimental design into IRT. Here, the computational cost is shifted to a
precomputing phase by training a Deep Reinforcement Learning (DRL) agent with
synthetic data. The agent is trained to select optimally informative test items
for the distribution of students, and to conduct amortised inference
conditioned on the experiment outcomes. During deployment the agent estimates
parameters from data, and suggests the next test item for the student, in close
to real-time, by taking into account the history of experiments and outcomes.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 10:42:56 GMT"
}
] | 1,689,811,200,000 | [
[
"Keurulainen",
"Antti",
""
],
[
"Westerlund",
"Isak",
""
],
[
"Keurulainen",
"Oskar",
""
],
[
"Howes",
"Andrew",
""
]
] |
2307.09905 | Martin Balla | Martin Balla, George E.M. Long, Dominik Jeurissen, James Goodman,
Raluca D. Gaina, Diego Perez-Liebana | PyTAG: Challenges and Opportunities for Reinforcement Learning in
Tabletop Games | Accepted for Publication in: IEEE Conference on Games (2023) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years, Game AI research has made important breakthroughs using
Reinforcement Learning (RL). Despite this, RL for modern tabletop games has
gained little to no attention, even when they offer a range of unique
challenges compared to video games. To bridge this gap, we introduce PyTAG, a
Python API for interacting with the Tabletop Games framework (TAG). TAG
contains a growing set of more than 20 modern tabletop games, with a common API
for AI agents. We present techniques for training RL agents in these games and
introduce baseline results after training Proximal Policy Optimisation
algorithms on a subset of games. Finally, we discuss the unique challenges
complex modern tabletop games provide, now open to RL research through PyTAG.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 11:08:59 GMT"
}
] | 1,689,811,200,000 | [
[
"Balla",
"Martin",
""
],
[
"Long",
"George E. M.",
""
],
[
"Jeurissen",
"Dominik",
""
],
[
"Goodman",
"James",
""
],
[
"Gaina",
"Raluca D.",
""
],
[
"Perez-Liebana",
"Diego",
""
]
] |
2307.09909 | Michal Sroka PhD | Urszula Jessen, Michal Sroka, Dirk Fahland | Chit-Chat or Deep Talk: Prompt Engineering for Process Mining | 11 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This research investigates the application of Large Language Models (LLMs) to
augment conversational agents in process mining, aiming to tackle its inherent
complexity and diverse skill requirements. While LLM advancements present novel
opportunities for conversational process mining, generating efficient outputs
is still a hurdle. We propose an innovative approach that amend many issues in
existing solutions, informed by prior research on Natural Language Processing
(NLP) for conversational agents. Leveraging LLMs, our framework improves both
accessibility and agent performance, as demonstrated by experiments on public
question and data sets. Our research sets the stage for future explorations
into LLMs' role in process mining and concludes with propositions for enhancing
LLM memory, implementing real-time user testing, and examining diverse data
sets.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 11:25:12 GMT"
}
] | 1,689,811,200,000 | [
[
"Jessen",
"Urszula",
""
],
[
"Sroka",
"Michal",
""
],
[
"Fahland",
"Dirk",
""
]
] |
2307.10004 | Xidong Wang | Ye Ouyang, Yaqin Zhang, Peng Wang, Yunxin Liu, Wen Qiao, Jun Zhu, Yang
Liu, Feng Zhang, Shuling Wang, Xidong Wang | 6G Network Business Support System | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | 6G is the next-generation intelligent and integrated digital information
infrastructure, characterized by ubiquitous interconnection, native
intelligence, multi-dimensional perception, global coverage, green and
low-carbon, native network security, etc. 6G will realize the transition from
serving people and people-things communication to supporting the efficient
connection of intelligent agents, and comprehensively leading the digital,
intelligent and green transformation of the economy and the society. As the
core support system for mobile communication network, 6 6G BSS need to
integrate with new business models brought about by the development of the
next-generation Internet and IT, upgrade from "network-centric" to "business
and service centric" and "customer-centric". 6G OSS and BSS systems need to
strengthen their integration to improve the operational efficiency and benefits
of customers by connecting the digital intelligence support capabilities on
both sides of supply and demand. This paper provides a detailed introduction to
the overall vision, potential key technologies, and functional architecture of
6G BSS systems. It also presents an evolutionary roadmap and technological
prospects for the BSS systems from 5G to 6G.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 14:38:30 GMT"
}
] | 1,689,811,200,000 | [
[
"Ouyang",
"Ye",
""
],
[
"Zhang",
"Yaqin",
""
],
[
"Wang",
"Peng",
""
],
[
"Liu",
"Yunxin",
""
],
[
"Qiao",
"Wen",
""
],
[
"Zhu",
"Jun",
""
],
[
"Liu",
"Yang",
""
],
[
"Zhang",
"Feng",
""
],
[
"Wang",
"Shuling",
""
],
[
"Wang",
"Xidong",
""
]
] |
2307.10085 | Haoyu Sun | Haoyu Sun, Yan Yan | A Decision Making Framework for Recommended Maintenance of Road Segments | 10 pages, 8 figures, 4 tables, and 2 algorithms | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Due to limited budgets allocated for road maintenance projects in various
countries, road management departments face difficulties in making scientific
maintenance decisions. This paper aims to provide road management departments
with more scientific decision tools and evidence. The framework proposed in
this paper mainly has the following four innovative points: 1) Predicting
pavement performance deterioration levels of road sections as decision basis
rather than accurately predicting specific indicator values; 2) Determining
maintenance route priorities based on multiple factors; 3) Making maintenance
plan decisions by establishing deep reinforcement learning models to formulate
predictive strategies based on past maintenance performance evaluations, while
considering both technical and management indicators; 4) Determining repair
section priorities according to actual and suggested repair effects. By
resolving these four issues, the framework can make intelligent decisions
regarding optimal maintenance plans and sections, taking into account limited
funds and historical maintenance management experiences.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 15:55:25 GMT"
},
{
"version": "v2",
"created": "Sat, 22 Jul 2023 02:33:35 GMT"
},
{
"version": "v3",
"created": "Sun, 1 Oct 2023 19:28:43 GMT"
}
] | 1,696,291,200,000 | [
[
"Sun",
"Haoyu",
""
],
[
"Yan",
"Yan",
""
]
] |
2307.10198 | Caroline S. Wagner | Chao Min, Yi Zhao, Yi Bu, Ying Ding, Caroline S. Wagner | Has China caught up to the US in AI research? An exploration of mimetic
isomorphism as a model for late industrializers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial Intelligence (AI), a cornerstone of 21st-century technology, has
seen remarkable growth in China. In this paper, we examine China's AI
development process, demonstrating that it is characterized by rapid learning
and differentiation, surpassing the export-oriented growth propelled by Foreign
Direct Investment seen in earlier Asian industrializers.
Our data indicates that China currently leads the USA in the volume of
AI-related research papers. However, when we delve into the quality of these
papers based on specific metrics, the USA retains a slight edge. Nevertheless,
the pace and scale of China's AI development remain noteworthy.
We attribute China's accelerated AI progress to several factors, including
global trends favoring open access to algorithms and research papers,
contributions from China's broad diaspora and returnees, and relatively lax
data protection policies.
In the vein of our research, we have developed a novel measure for gauging
China's imitation of US research. Our analysis shows that by 2018, the time lag
between China and the USA in addressing AI research topics had evaporated. This
finding suggests that China has effectively bridged a significant knowledge gap
and could potentially be setting out on an independent research trajectory.
While this study compares China and the USA exclusively, it's important to
note that research collaborations between these two nations have resulted in
more highly cited work than those produced by either country independently.
This underscores the power of international cooperation in driving scientific
progress in AI.
| [
{
"version": "v1",
"created": "Tue, 11 Jul 2023 19:59:54 GMT"
}
] | 1,689,897,600,000 | [
[
"Min",
"Chao",
""
],
[
"Zhao",
"Yi",
""
],
[
"Bu",
"Yi",
""
],
[
"Ding",
"Ying",
""
],
[
"Wagner",
"Caroline S.",
""
]
] |
2307.10224 | Zhecheng Yuan | Zhecheng Yuan, Sizhe Yang, Pu Hua, Can Chang, Kaizhe Hu, Huazhe Xu | RL-ViGen: A Reinforcement Learning Benchmark for Visual Generalization | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Visual Reinforcement Learning (Visual RL), coupled with high-dimensional
observations, has consistently confronted the long-standing challenge of
out-of-distribution generalization. Despite the focus on algorithms aimed at
resolving visual generalization problems, we argue that the devil is in the
existing benchmarks as they are restricted to isolated tasks and generalization
categories, undermining a comprehensive evaluation of agents' visual
generalization capabilities. To bridge this gap, we introduce RL-ViGen: a novel
Reinforcement Learning Benchmark for Visual Generalization, which contains
diverse tasks and a wide spectrum of generalization types, thereby facilitating
the derivation of more reliable conclusions. Furthermore, RL-ViGen incorporates
the latest generalization visual RL algorithms into a unified framework, under
which the experiment results indicate that no single existing algorithm has
prevailed universally across tasks. Our aspiration is that RL-ViGen will serve
as a catalyst in this area, and lay a foundation for the future creation of
universal visual generalization RL agents suitable for real-world scenarios.
Access to our code and implemented algorithms is provided at
https://gemcollector.github.io/RL-ViGen/.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 05:45:37 GMT"
},
{
"version": "v2",
"created": "Wed, 9 Aug 2023 11:49:56 GMT"
},
{
"version": "v3",
"created": "Tue, 26 Sep 2023 10:14:54 GMT"
}
] | 1,695,772,800,000 | [
[
"Yuan",
"Zhecheng",
""
],
[
"Yang",
"Sizhe",
""
],
[
"Hua",
"Pu",
""
],
[
"Chang",
"Can",
""
],
[
"Hu",
"Kaizhe",
""
],
[
"Xu",
"Huazhe",
""
]
] |
2307.10226 | Joohyung Lee | Joohyung Lee, Yunsong Meng | On Loop Formulas with Variables | 10 pages. In Proc. Eleventh International Conference on Principles of
Knowledge Representation and Reasoning (KR 2008), pages 444-453. arXiv admin
note: text overlap with arXiv:1401.3898 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recently Ferraris, Lee and Lifschitz proposed a new definition of stable
models that does not refer to grounding, which applies to the syntax of
arbitrary first-order sentences. We show its relation to the idea of loop
formulas with variables by Chen, Lin, Wang and Zhang, and generalize their loop
formulas to disjunctive programs and to arbitrary first-order sentences. We
also extend the syntax of logic programs to allow explicit quantifiers, and
define its semantics as a subclass of the new language of stable models by
Ferraris et al. Such programs inherit from the general language the ability to
handle nonmonotonic reasoning under the stable model semantics even in the
absence of the unique name and the domain closure assumptions, while yielding
more succinct loop formulas than the general language due to the restricted
syntax. We also show certain syntactic conditions under which query answering
for an extended program can be reduced to entailment checking in first-order
logic, providing a way to apply first-order theorem provers to reasoning about
non-Herbrand stable models.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 06:20:43 GMT"
}
] | 1,689,897,600,000 | [
[
"Lee",
"Joohyung",
""
],
[
"Meng",
"Yunsong",
""
]
] |
2307.10227 | Joohyung Lee | Enrico Giunchiglia, Joohyung Lee, Vladimir Lifschitz, Hudson Turner | Causal Laws and Multi-Valued Fluents | 7 pages, In Proceedings of Workshop on Nonmonotonic Reasoning, Action
and Change (NRAC 2001) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper continues the line of work on representing properties of actions
in nonmonotonic formalisms that stresses the distinction between being "true"
and being "caused", as in the system of causal logic introduced by McCain and
Turner and in the action language C proposed by Giunchiglia and Lifschitz. The
only fluents directly representable in language C+ are truth-valued fluents,
which is often inconvenient. We show that both causal logic and language C can
be extended to allow values from arbitrary nonempty sets. Our extension of
language C, called C+, also makes it possible to describe actions in terms of
their attributes, which is important from the perspective of elaboration
tolerance. We describe an embedding of C+ in causal theories with multi-valued
constants, relate C+ to Pednault's action language ADL, and show how
multi-valued constants can be eliminated in favor of Boolean constants.
| [
{
"version": "v1",
"created": "Sat, 15 Jul 2023 06:41:08 GMT"
}
] | 1,689,897,600,000 | [
[
"Giunchiglia",
"Enrico",
""
],
[
"Lee",
"Joohyung",
""
],
[
"Lifschitz",
"Vladimir",
""
],
[
"Turner",
"Hudson",
""
]
] |
2307.10250 | Remo Pareschi Prof. | Remo Pareschi | Abductive Reasoning with the GPT-4 Language Model: Case studies from
criminal investigation, medical practice, scientific research | The article is 12 pages long and has one figure. It also includes a
link to some ChatGPT dialogues that show the experiments that support the
article's findings. The article will be published in V. Bambini and C.
Barattieri di San Pietro (eds.), Sistemi Intelligenti, Special Section
"Multidisciplinary perspectives on ChatGPT and the family of Large Language
Models" | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This study evaluates the GPT-4 Large Language Model's abductive reasoning in
complex fields like medical diagnostics, criminology, and cosmology. Using an
interactive interview format, the AI assistant demonstrated reliability in
generating and selecting hypotheses. It inferred plausible medical diagnoses
based on patient data and provided potential causes and explanations in
criminology and cosmology. The results highlight the potential of LLMs in
complex problem-solving and the need for further research to maximize their
practical applications.
| [
{
"version": "v1",
"created": "Mon, 17 Jul 2023 07:48:31 GMT"
}
] | 1,689,897,600,000 | [
[
"Pareschi",
"Remo",
""
]
] |
2307.10315 | Mitchell Barrington | Mitchell Barrington | Absolutist AI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper argues that training AI systems with absolute constraints -- which
forbid certain acts irrespective of the amount of value they might produce --
may make considerable progress on many AI safety problems in principle. First,
it provides a guardrail for avoiding the very worst outcomes of misalignment.
Second, it could prevent AIs from causing catastrophes for the sake of very
valuable consequences, such as replacing humans with a much larger number of
beings living at a higher welfare level. Third, it makes systems more
corrigible, allowing creators to make corrective interventions in them, such as
altering their objective functions or shutting them down. And fourth, it helps
systems explore their environment more safely by prohibiting them from
exploring especially dangerous acts. I offer a decision-theoretic formalization
of an absolute constraints, improving on existing models in the literature, and
use this model to prove some results about the training and behavior of
absolutist AIs. I conclude by showing that, although absolutist AIs will not
maximize expected value, they will not be susceptible to behave irrationally,
and they will not (contra coherence arguments) face environmental pressure to
become expected-value maximizers.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 03:40:37 GMT"
}
] | 1,689,897,600,000 | [
[
"Barrington",
"Mitchell",
""
]
] |
2307.10420 | Rebwar Khalid Hamad | Rebwar Khalid Hamad, Tarik A. Rashid | GOOSE Algorithm: A Powerful Optimization Tool for Real-World Engineering
Challenges and Beyond | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This study proposes the GOOSE algorithm as a novel metaheuristic algorithm
based on the goose's behavior during rest and foraging. The goose stands on one
leg and keeps his balance to guard and protect other individuals in the flock.
The GOOSE algorithm is benchmarked on 19 well-known benchmark test functions,
and the results are verified by a comparative study with genetic algorithm
(GA), particle swarm optimization (PSO), dragonfly algorithm (DA), and fitness
dependent optimizer (FDO). In addition, the proposed algorithm is tested on 10
modern benchmark functions, and the gained results are compared with three
recent algorithms, such as the dragonfly algorithm, whale optimization
algorithm (WOA), and salp swarm algorithm (SSA). Moreover, the GOOSE algorithm
is tested on 5 classical benchmark functions, and the obtained results are
evaluated with six algorithms, such as fitness dependent optimizer (FDO), FOX
optimizer, butterfly optimization algorithm (BOA), whale optimization
algorithm, dragonfly algorithm, and chimp optimization algorithm (ChOA). The
achieved findings attest to the proposed algorithm's superior performance
compared to the other algorithms that were utilized in the current study. The
technique is then used to optimize Welded beam design and Economic Load
Dispatch Problem, three renowned real-world engineering challenges, and the
Pathological IgG Fraction in the Nervous System. The outcomes of the
engineering case studies illustrate how well the suggested approach can
optimize issues that arise in the real-world.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 19:14:25 GMT"
}
] | 1,689,897,600,000 | [
[
"Hamad",
"Rebwar Khalid",
""
],
[
"Rashid",
"Tarik A.",
""
]
] |
2307.10458 | Jacintha Walters | Jacintha Walters, Diptish Dey, Debarati Bhaumik, Sophie Horsman | Complying with the EU AI Act | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The EU AI Act is the proposed EU legislation concerning AI systems. This
paper identifies several categories of the AI Act. Based on this
categorization, a questionnaire is developed that serves as a tool to offer
insights by creating quantitative data. Analysis of the data shows various
challenges for organizations in different compliance categories. The influence
of organization characteristics, such as size and sector, is examined to
determine the impact on compliance. The paper will also share qualitative data
on which questions were prevalent among respondents, both on the content of the
AI Act as the application. The paper concludes by stating that there is still
room for improvement in terms of compliance with the AIA and refers to a
related project that examines a solution to help these organizations.
| [
{
"version": "v1",
"created": "Wed, 19 Jul 2023 21:04:46 GMT"
}
] | 1,689,897,600,000 | [
[
"Walters",
"Jacintha",
""
],
[
"Dey",
"Diptish",
""
],
[
"Bhaumik",
"Debarati",
""
],
[
"Horsman",
"Sophie",
""
]
] |
2307.10543 | Wendi Li | Wendi Li, Wei Wei, Xiaoye Qu, Xian-Ling Mao, Ye Yuan, Wenfeng Xie,
Dangyang Chen | TREA: Tree-Structure Reasoning Schema for Conversational Recommendation | Accepted by ACL2023 main conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Conversational recommender systems (CRS) aim to timely trace the dynamic
interests of users through dialogues and generate relevant responses for item
recommendations. Recently, various external knowledge bases (especially
knowledge graphs) are incorporated into CRS to enhance the understanding of
conversation contexts. However, recent reasoning-based models heavily rely on
simplified structures such as linear structures or fixed-hierarchical
structures for causality reasoning, hence they cannot fully figure out
sophisticated relationships among utterances with external knowledge. To
address this, we propose a novel Tree structure Reasoning schEmA named TREA.
TREA constructs a multi-hierarchical scalable tree as the reasoning structure
to clarify the causal relationships between mentioned entities, and fully
utilizes historical conversations to generate more reasonable and suitable
responses for recommended results. Extensive experiments on two public CRS
datasets have demonstrated the effectiveness of our approach.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 02:48:04 GMT"
}
] | 1,689,897,600,000 | [
[
"Li",
"Wendi",
""
],
[
"Wei",
"Wei",
""
],
[
"Qu",
"Xiaoye",
""
],
[
"Mao",
"Xian-Ling",
""
],
[
"Yuan",
"Ye",
""
],
[
"Xie",
"Wenfeng",
""
],
[
"Chen",
"Dangyang",
""
]
] |
2307.10551 | Kaiwen Wei | Kaiwen Wei, Jie Yao, Jingyuan Zhang, Yangyang Kang, Fubang Zhao,
Yating Zhang, Changlong Sun, Xin Jin, Xin Zhang | PPN: Parallel Pointer-based Network for Key Information Extraction with
Complex Layouts | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Key Information Extraction (KIE) is a challenging multimodal task that aims
to extract structured value semantic entities from visually rich documents.
Although significant progress has been made, there are still two major
challenges that need to be addressed. Firstly, the layout of existing datasets
is relatively fixed and limited in the number of semantic entity categories,
creating a significant gap between these datasets and the complex real-world
scenarios. Secondly, existing methods follow a two-stage pipeline strategy,
which may lead to the error propagation problem. Additionally, they are
difficult to apply in situations where unseen semantic entity categories
emerge. To address the first challenge, we propose a new large-scale
human-annotated dataset named Complex Layout form for key information
EXtraction (CLEX), which consists of 5,860 images with 1,162 semantic entity
categories. To solve the second challenge, we introduce Parallel Pointer-based
Network (PPN), an end-to-end model that can be applied in zero-shot and
few-shot scenarios. PPN leverages the implicit clues between semantic entities
to assist extracting, and its parallel extraction mechanism allows it to
extract multiple results simultaneously and efficiently. Experiments on the
CLEX dataset demonstrate that PPN outperforms existing state-of-the-art methods
while also offering a much faster inference speed.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 03:29:09 GMT"
}
] | 1,689,897,600,000 | [
[
"Wei",
"Kaiwen",
""
],
[
"Yao",
"Jie",
""
],
[
"Zhang",
"Jingyuan",
""
],
[
"Kang",
"Yangyang",
""
],
[
"Zhao",
"Fubang",
""
],
[
"Zhang",
"Yating",
""
],
[
"Sun",
"Changlong",
""
],
[
"Jin",
"Xin",
""
],
[
"Zhang",
"Xin",
""
]
] |
2307.10573 | Rylan Schaeffer | Rylan Schaeffer, Kateryna Pistunova, Samar Khanna, Sarthak Consul,
Sanmi Koyejo | Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in
Language Model Prompting | ICML 2023 Workshop: Knowledge and Logical Reasoning in the Era of
Data-driven Learning | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Language models can be prompted to reason through problems in a manner that
significantly improves performance. However, \textit{why} such prompting
improves performance is unclear. Recent work showed that using logically
\textit{invalid} Chain-of-Thought (CoT) prompting improves performance almost
as much as logically \textit{valid} CoT prompting, and that editing CoT prompts
to replace problem-specific information with abstract information or
out-of-distribution information typically doesn't harm performance. Critics
have responded that these findings are based on too few and too easily solved
tasks to draw meaningful conclusions. To resolve this dispute, we test whether
logically invalid CoT prompts offer the same level of performance gains as
logically valid prompts on the hardest tasks in the BIG-Bench benchmark, termed
BIG-Bench Hard (BBH). We find that the logically \textit{invalid} reasoning
prompts do indeed achieve similar performance gains on BBH tasks as logically
valid reasoning prompts. We also discover that some CoT prompts used by
previous works contain logical errors. This suggests that covariates beyond
logically valid reasoning are responsible for performance improvements.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 04:28:53 GMT"
},
{
"version": "v2",
"created": "Sun, 23 Jul 2023 02:58:50 GMT"
}
] | 1,690,243,200,000 | [
[
"Schaeffer",
"Rylan",
""
],
[
"Pistunova",
"Kateryna",
""
],
[
"Khanna",
"Samar",
""
],
[
"Consul",
"Sarthak",
""
],
[
"Koyejo",
"Sanmi",
""
]
] |
2307.10574 | Jia-Rui Lin | Can Jiang, Xin Li, Jia-Rui Lin, Ming Liu, Zhiliang Ma | Adaptive Control of Resource Flow to Optimize Construction Work and Cash
Flow via Online Deep Reinforcement Learning | null | Automation in Construction, 2023 | 10.1016/j.autcon.2023.104817 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Due to complexity and dynamics of construction work, resource, and cash
flows, poor management of them usually leads to time and cost overruns,
bankruptcy, even project failure. Existing approaches in construction failed to
achieve optimal control of resource flow in a dynamic environment with
uncertainty. Therefore, this paper introducess a model and method to adaptive
control the resource flows to optimize the work and cash flows of construction
projects. First, a mathematical model based on a partially observable Markov
decision process is established to formulate the complex interactions of
construction work, resource, and cash flows as well as uncertainty and
variability of diverse influence factors. Meanwhile, to efficiently find the
optimal solutions, a deep reinforcement learning (DRL) based method is
introduced to realize the continuous adaptive optimal control of labor and
material flows, thereby optimizing the work and cash flows. To assist the
training process of DRL, a simulator based on discrete event simulation is also
developed to mimic the dynamic features and external environments of a project.
Experiments in simulated scenarios illustrate that our method outperforms the
vanilla empirical method and genetic algorithm, possesses remarkable capability
in diverse projects and external environments, and a hybrid agent of DRL and
empirical method leads to the best result. This paper contributes to adaptive
control and optimization of coupled work, resource, and cash flows, and may
serve as a step stone for adopting DRL technology in construction project
management.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 04:31:39 GMT"
}
] | 1,692,230,400,000 | [
[
"Jiang",
"Can",
""
],
[
"Li",
"Xin",
""
],
[
"Lin",
"Jia-Rui",
""
],
[
"Liu",
"Ming",
""
],
[
"Ma",
"Zhiliang",
""
]
] |
2307.10600 | Rifat Ara Shams | Rifat Ara Shams, Didar Zowghi, Muneera Bano | Challenges and Solutions in AI for All | 39 pages, 10 figures, 10 tables | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial Intelligence (AI)'s pervasive presence and variety necessitate
diversity and inclusivity (D&I) principles in its design for fairness, trust,
and transparency. Yet, these considerations are often overlooked, leading to
issues of bias, discrimination, and perceived untrustworthiness. In response,
we conducted a Systematic Review to unearth challenges and solutions relating
to D&I in AI. Our rigorous search yielded 48 research articles published
between 2017 and 2022. Open coding of these papers revealed 55 unique
challenges and 33 solutions for D&I in AI, as well as 24 unique challenges and
23 solutions for enhancing such practices using AI. This study, by offering a
deeper understanding of these issues, will enlighten researchers and
practitioners seeking to integrate these principles into future AI systems.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 05:43:39 GMT"
}
] | 1,689,897,600,000 | [
[
"Shams",
"Rifat Ara",
""
],
[
"Zowghi",
"Didar",
""
],
[
"Bano",
"Muneera",
""
]
] |
2307.10688 | Mutsunori Banbara | Yuya Yamada, Mutsunori Banbara, Katsumi Inoue, Torsten Schaub | Bounded Combinatorial Reconfiguration with Answer Set Programming | 15 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We develop an approach called bounded combinatorial reconfiguration for
solving combinatorial reconfiguration problems based on Answer Set Programming
(ASP). The general task is to study the solution spaces of source combinatorial
problems and to decide whether or not there are sequences of feasible solutions
that have special properties. The resulting recongo solver covers all metrics
of the solver track in the most recent international competition on
combinatorial reconfiguration (CoRe Challenge 2022). recongo ranked first in
the shortest metric of the single-engine solvers track. In this paper, we
present the design and implementation of bounded combinatorial reconfiguration,
and present an ASP encoding of the independent set reconfiguration problem that
is one of the most studied combinatorial reconfiguration problems. Finally, we
present empirical analysis considering all instances of CoRe Challenge 2022.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 08:30:56 GMT"
}
] | 1,689,897,600,000 | [
[
"Yamada",
"Yuya",
""
],
[
"Banbara",
"Mutsunori",
""
],
[
"Inoue",
"Katsumi",
""
],
[
"Schaub",
"Torsten",
""
]
] |
2307.10832 | Kevin McAreavey | Kevin McAreavey, Weiru Liu | Modifications of the Miller definition of contrastive (counterfactual)
explanations | Accepted by ECSQARU'23 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Miller recently proposed a definition of contrastive (counterfactual)
explanations based on the well-known Halpern-Pearl (HP) definitions of causes
and (non-contrastive) explanations. Crucially, the Miller definition was based
on the original HP definition of explanations, but this has since been modified
by Halpern; presumably because the original yields counterintuitive results in
many standard examples. More recently Borner has proposed a third definition,
observing that this modified HP definition may also yield counterintuitive
results. In this paper we show that the Miller definition inherits issues found
in the original HP definition. We address these issues by proposing two
improved variants based on the more robust modified HP and Borner definitions.
We analyse our new definitions and show that they retain the spirit of the
Miller definition where all three variants satisfy an alternative unified
definition that is modular with respect to an underlying definition of
non-contrastive explanations. To the best of our knowledge this paper also
provides the first explicit comparison between the original and modified HP
definitions.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 12:52:30 GMT"
}
] | 1,689,897,600,000 | [
[
"McAreavey",
"Kevin",
""
],
[
"Liu",
"Weiru",
""
]
] |
2307.11206 | Walid Saba | Walid S. Saba | Towards Ontologically Grounded and Language-Agnostic Knowledge Graphs | 7 pages, conference paper | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs (KGs) have become the standard technology for the
representation of factual information in applications such as recommendation
engines, search, and question-answering systems. However, the continual
updating of KGs, as well as the integration of KGs from different domains and
KGs in different languages, remains to be a major challenge. What we suggest
here is that by a reification of abstract objects and by acknowledging the
ontological distinction between concepts and types, we arrive at an
ontologically grounded and language-agnostic representation that can alleviate
the difficulties in KG integration.
| [
{
"version": "v1",
"created": "Thu, 20 Jul 2023 19:48:55 GMT"
}
] | 1,690,156,800,000 | [
[
"Saba",
"Walid S.",
""
]
] |
2307.11286 | Spencer Killen | Spencer Killen, Jia-Huai You | Eliminating Unintended Stable Fixpoints for Hybrid Reasoning Systems | 24 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A wide variety of nonmonotonic semantics can be expressed as approximators
defined under AFT (Approximation Fixpoint Theory). Using traditional AFT
theory, it is not possible to define approximators that rely on information
computed in previous iterations of stable revision. However, this information
is rich for semantics that incorporate classical negation into nonmonotonic
reasoning. In this work, we introduce a methodology resembling AFT that can
utilize priorly computed upper bounds to more precisely capture semantics. We
demonstrate our framework's applicability to hybrid MKNF (minimal knowledge and
negation as failure) knowledge bases by extending the state-of-the-art
approximator.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 01:08:15 GMT"
}
] | 1,690,156,800,000 | [
[
"Killen",
"Spencer",
""
],
[
"You",
"Jia-Huai",
""
]
] |
2307.11343 | Xuetao Li | Fang Gao, XueTao Li, Jun Yu, Feng Shaung | A Two-stage Fine-tuning Strategy for Generalizable Manipulation Skill of
Embodied AI | 5 pages, 2 figures, 5 tables, accept by Robotics: Science and Systems
2023 - Workshop Interdisciplinary Exploration of Generalizable Manipulation
Policy Learning:Paradigms and Debates | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The advent of Chat-GPT has led to a surge of interest in Embodied AI.
However, many existing Embodied AI models heavily rely on massive interactions
with training environments, which may not be practical in real-world
situations. To this end, the Maniskill2 has introduced a full-physics
simulation benchmark for manipulating various 3D objects. This benchmark
enables agents to be trained using diverse datasets of demonstrations and
evaluates their ability to generalize to unseen scenarios in testing
environments. In this paper, we propose a novel two-stage fine-tuning strategy
that aims to further enhance the generalization capability of our model based
on the Maniskill2 benchmark. Through extensive experiments, we demonstrate the
effectiveness of our approach by achieving the 1st prize in all three tracks of
the ManiSkill2 Challenge. Our findings highlight the potential of our method to
improve the generalization abilities of Embodied AI models and pave the way for
their ractical applications in real-world scenarios. All codes and models of
our solution is available at https://github.com/xtli12/GXU-LIPE.git
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 04:15:36 GMT"
}
] | 1,704,240,000,000 | [
[
"Gao",
"Fang",
""
],
[
"Li",
"XueTao",
""
],
[
"Yu",
"Jun",
""
],
[
"Shaung",
"Feng",
""
]
] |
2307.11449 | Yong Song | Ye Ouyang, Yaqin Zhang, Xiaozhou Ye, Yunxin Liu, Yong Song, Yang Liu,
Sen Bian, Zhiyong Liu | AIGC Empowering Telecom Sector White Paper_chinese | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the global craze of GPT, people have deeply realized that AI, as a
transformative technology and key force in economic and social development,
will bring great leaps and breakthroughs to the global industry and profoundly
influence the future world competition pattern. As the builder and operator of
information and communication infrastructure, the telecom sector provides
infrastructure support for the development of AI, and even takes the lead in
the implementation of AI applications. How to enable the application of AIGC
(GPT) and implement AIGC in the telecom sector are questions that telecom
practitioners must ponder and answer. Through the study of GPT, a typical
representative of AIGC, the authors have analyzed how GPT empowers the telecom
sector in the form of scenarios, discussed the gap between the current GPT
general model and telecom services, proposed for the first time a Telco
Augmented Cognition capability system, provided answers to how to construct a
telecom service GPT in the telecom sector, and carried out various practices.
Our counterparts in the industry are expected to focus on collaborative
innovation around telecom and AI, build an open and shared innovation
ecosystem, promote the deep integration of AI and telecom sector, and
accelerate the construction of next-generation information infrastructure, in
an effort to facilitate the digital transformation of the economy and society.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 09:30:08 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Jul 2023 01:54:25 GMT"
}
] | 1,690,243,200,000 | [
[
"Ouyang",
"Ye",
""
],
[
"Zhang",
"Yaqin",
""
],
[
"Ye",
"Xiaozhou",
""
],
[
"Liu",
"Yunxin",
""
],
[
"Song",
"Yong",
""
],
[
"Liu",
"Yang",
""
],
[
"Bian",
"Sen",
""
],
[
"Liu",
"Zhiyong",
""
]
] |
2307.11525 | Danilo Brajovic | Danilo Brajovic, Niclas Renner, Vincent Philipp Goebels, Philipp
Wagner, Benjamin Fresz, Martin Biller, Mara Klaeb, Janika Kutz, Jens
Neuhuettler, Marco F. Huber | Model Reporting for Certifiable AI: A Proposal from Merging EU
Regulation into AI Development | 54 pages, 1 figure, to be submitted | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Despite large progress in Explainable and Safe AI, practitioners suffer from
a lack of regulation and standards for AI safety. In this work we merge recent
regulation efforts by the European Union and first proposals for AI guidelines
with recent trends in research: data and model cards. We propose the use of
standardized cards to document AI applications throughout the development
process. Our main contribution is the introduction of use-case and operation
cards, along with updates for data and model cards to cope with regulatory
requirements. We reference both recent research as well as the source of the
regulation in our cards and provide references to additional support material
and toolboxes whenever possible. The goal is to design cards that help
practitioners develop safe AI systems throughout the development process, while
enabling efficient third-party auditing of AI applications, being easy to
understand, and building trust in the system. Our work incorporates insights
from interviews with certification experts as well as developers and
individuals working with the developed AI applications.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 12:13:54 GMT"
}
] | 1,690,156,800,000 | [
[
"Brajovic",
"Danilo",
""
],
[
"Renner",
"Niclas",
""
],
[
"Goebels",
"Vincent Philipp",
""
],
[
"Wagner",
"Philipp",
""
],
[
"Fresz",
"Benjamin",
""
],
[
"Biller",
"Martin",
""
],
[
"Klaeb",
"Mara",
""
],
[
"Kutz",
"Janika",
""
],
[
"Neuhuettler",
"Jens",
""
],
[
"Huber",
"Marco F.",
""
]
] |
2307.11544 | Zsolt Csaba Johany\'ak | L\'aszl\'o G\"ocs, Zsolt Csaba Johany\'ak | Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the
Development of an Intrusion Detection System | 24 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Intrusion detection systems (IDSs) are essential elements of IT systems.
Their key component is a classification module that continuously evaluates some
features of the network traffic and identifies possible threats. Its efficiency
is greatly affected by the right selection of the features to be monitored.
Therefore, the identification of a minimal set of features that are necessary
to safely distinguish malicious traffic from benign traffic is indispensable in
the course of the development of an IDS. This paper presents the preprocessing
and feature selection workflow as well as its results in the case of the
CSE-CIC-IDS2018 on AWS dataset, focusing on five attack types. To identify the
relevant features, six feature selection methods were applied, and the final
ranking of the features was elaborated based on their average score. Next,
several subsets of the features were formed based on different ranking
threshold values, and each subset was tried with five classification algorithms
to determine the optimal feature set for each attack type. During the
evaluation, four widely used metrics were taken into consideration.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 12:45:03 GMT"
}
] | 1,690,156,800,000 | [
[
"Göcs",
"László",
""
],
[
"Johanyák",
"Zsolt Csaba",
""
]
] |
2307.11621 | Josep Argelich | Teresa Alsinet, Josep Argelich, Ram\'on B\'ejar and Santi Mart\'inez | On the Complexity of the Bipartite Polarization Problem: from Neutral to
Highly Polarized Discussions | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The Bipartite Polarization Problem is an optimization problem where the goal
is to find the highest polarized bipartition on a weighted and labelled graph
that represents a debate developed through some social network, where nodes
represent user's opinions and edges agreement or disagreement between users.
This problem can be seen as a generalization of the maxcut problem, and in
previous work approximate solutions and exact solutions have been obtained for
real instances obtained from Reddit discussions, showing that such real
instances seem to be very easy to solve. In this paper, we investigate further
the complexity of this problem, by introducing an instance generation model
where a single parameter controls the polarization of the instances in such a
way that this correlates with the average complexity to solve those instances.
The average complexity results we obtain are consistent with our hypothesis:
the higher the polarization of the instance, the easier is to find the
corresponding polarized bipartition.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 14:40:41 GMT"
}
] | 1,690,156,800,000 | [
[
"Alsinet",
"Teresa",
""
],
[
"Argelich",
"Josep",
""
],
[
"Béjar",
"Ramón",
""
],
[
"Martínez",
"Santi",
""
]
] |
2307.11637 | Milapji Singh Gill | Milapji Singh Gill, Tom Westermann, Marvin Schieseck, Alexander Fay | Integration of Domain Expert-Centric Ontology Design into the CRISP-DM
for Cyber-Physical Production Systems | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the age of Industry 4.0 and Cyber-Physical Production Systems (CPPSs) vast
amounts of potentially valuable data are being generated. Methods from Machine
Learning (ML) and Data Mining (DM) have proven to be promising in extracting
complex and hidden patterns from the data collected. The knowledge obtained can
in turn be used to improve tasks like diagnostics or maintenance planning.
However, such data-driven projects, usually performed with the Cross-Industry
Standard Process for Data Mining (CRISP-DM), often fail due to the
disproportionate amount of time needed for understanding and preparing the
data. The application of domain-specific ontologies has demonstrated its
advantageousness in a wide variety of Industry 4.0 application scenarios
regarding the aforementioned challenges. However, workflows and artifacts from
ontology design for CPPSs have not yet been systematically integrated into the
CRISP-DM. Accordingly, this contribution intends to present an integrated
approach so that data scientists are able to more quickly and reliably gain
insights into the CPPS. The result is exemplarily applied to an anomaly
detection use case.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 15:04:00 GMT"
}
] | 1,690,156,800,000 | [
[
"Gill",
"Milapji Singh",
""
],
[
"Westermann",
"Tom",
""
],
[
"Schieseck",
"Marvin",
""
],
[
"Fay",
"Alexander",
""
]
] |
2307.11709 | Aakash Bansal | Aakash Bansal, Siyuan Jiang, Sakib Haque, and Collin McMillan | Statement-based Memory for Neural Source Code Summarization | 10 pages 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Source code summarization is the task of writing natural language
descriptions of source code behavior. Code summarization underpins software
documentation for programmers. Short descriptions of code help programmers
understand the program quickly without having to read the code itself. Lately,
neural source code summarization has emerged as the frontier of research into
automated code summarization techniques. By far the most popular targets for
summarization are program subroutines. The idea, in a nutshell, is to train an
encoder-decoder neural architecture using large sets of examples of subroutines
extracted from code repositories. The encoder represents the code and the
decoder represents the summary. However, most current approaches attempt to
treat the subroutine as a single unit. For example, by taking the entire
subroutine as input to a Transformer or RNN-based encoder. But code behavior
tends to depend on the flow from statement to statement. Normally dynamic
analysis may shed light on this flow, but dynamic analysis on hundreds of
thousands of examples in large datasets is not practical. In this paper, we
present a statement-based memory encoder that learns the important elements of
flow during training, leading to a statement-based subroutine representation
without the need for dynamic analysis. We implement our encoder for code
summarization and demonstrate a significant improvement over the
state-of-the-art.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 17:04:39 GMT"
}
] | 1,690,156,800,000 | [
[
"Bansal",
"Aakash",
""
],
[
"Jiang",
"Siyuan",
""
],
[
"Haque",
"Sakib",
""
],
[
"McMillan",
"Collin",
""
]
] |
2307.11719 | Rita T. Sousa | Rita T. Sousa, Sara Silva, Catia Pesquita | Benchmark datasets for biomedical knowledge graphs with negative
statements | null | International Conference on Principles of Knowledge Representation
and Reasoning 2023 | 10.24963/kr.2023/62 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs represent facts about real-world entities. Most of these
facts are defined as positive statements. The negative statements are scarce
but highly relevant under the open-world assumption. Furthermore, they have
been demonstrated to improve the performance of several applications, namely in
the biomedical domain. However, no benchmark dataset supports the evaluation of
the methods that consider these negative statements.
We present a collection of datasets for three relation prediction tasks -
protein-protein interaction prediction, gene-disease association prediction and
disease prediction - that aim at circumventing the difficulties in building
benchmarks for knowledge graphs with negative statements. These datasets
include data from two successful biomedical ontologies, Gene Ontology and Human
Phenotype Ontology, enriched with negative statements.
We also generate knowledge graph embeddings for each dataset with two popular
path-based methods and evaluate the performance in each task. The results show
that the negative statements can improve the performance of knowledge graph
embeddings.
| [
{
"version": "v1",
"created": "Fri, 21 Jul 2023 17:25:21 GMT"
}
] | 1,700,092,800,000 | [
[
"Sousa",
"Rita T.",
""
],
[
"Silva",
"Sara",
""
],
[
"Pesquita",
"Catia",
""
]
] |
2307.12081 | Marco De Bortoli | Marco De Bortoli, Luk\'a\v{s} Chrpa, Martin Gebser and Gerald
Steinbauer-Wagner | Enhancing Temporal Planning Domains by Sequential Macro-actions
(Extended Version) | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal planning is an extension of classical planning involving concurrent
execution of actions and alignment with temporal constraints. Durative actions
along with invariants allow for modeling domains in which multiple agents
operate in parallel on shared resources. Hence, it is often important to avoid
resource conflicts, where temporal constraints establish the consistency of
concurrent actions and events. Unfortunately, the performance of temporal
planning engines tends to sharply deteriorate when the number of agents and
objects in a domain gets large. A possible remedy is to use macro-actions that
are well-studied in the context of classical planning. In temporal planning
settings, however, introducing macro-actions is significantly more challenging
when the concurrent execution of actions and shared use of resources, provided
the compliance to temporal constraints, should not be suppressed entirely. Our
work contributes a general concept of sequential temporal macro-actions that
guarantees the applicability of obtained plans, i.e., the sequence of original
actions encapsulated by a macro-action is always executable. We apply our
approach to several temporal planners and domains, stemming from the
International Planning Competition and RoboCup Logistics League. Our
experiments yield improvements in terms of obtained satisficing plans as well
as plan quality for the majority of tested planners and domains.
| [
{
"version": "v1",
"created": "Sat, 22 Jul 2023 13:50:34 GMT"
}
] | 1,690,243,200,000 | [
[
"De Bortoli",
"Marco",
""
],
[
"Chrpa",
"Lukáš",
""
],
[
"Gebser",
"Martin",
""
],
[
"Steinbauer-Wagner",
"Gerald",
""
]
] |
2307.12133 | Priyansh Saxena | Priyansh Saxena, Raahat Gupta, Akshat Maheshwari | Route Planning Using Nature-Inspired Algorithms | This work is part of 'High-Performance Vision Intelligence'; Part of
the Studies in Computational Intelligence book series (SCI,volume 913) and
can be accessed at:
https://link.springer.com/chapter/10.1007/978-981-15-6844-2_15 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There are many different heuristic algorithms for solving combinatorial
optimization problems that are commonly described as Nature-Inspired Algorithms
(NIAs). Generally, they are inspired by some natural phenomenon, and due to
their inherent converging and stochastic nature, they are known to give optimal
results when compared to classical approaches. There are a large number of
applications of NIAs, perhaps the most popular being route planning problems in
robotics - problems that require a sequence of translation and rotation steps
from the start to the goal in an optimized manner while avoiding obstacles in
the environment. In this chapter, we will first give an overview of
Nature-Inspired Algorithms, followed by their classification and common
examples. We will then discuss how the NIAs have applied to solve the route
planning problem.
| [
{
"version": "v1",
"created": "Sat, 22 Jul 2023 17:37:43 GMT"
}
] | 1,690,243,200,000 | [
[
"Saxena",
"Priyansh",
""
],
[
"Gupta",
"Raahat",
""
],
[
"Maheshwari",
"Akshat",
""
]
] |
2307.12184 | Shuwa Miura | Shuwa Miura | On the Expressivity of Multidimensional Markov Reward | Presented at RLDM Workshop on Reinforcement Learning as a Model of
Agency | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We consider the expressivity of Markov rewards in sequential decision making
under uncertainty. We view reward functions in Markov Decision Processes (MDPs)
as a means to characterize desired behaviors of agents. Assuming desired
behaviors are specified as a set of acceptable policies, we investigate if
there exists a scalar or multidimensional Markov reward function that makes the
policies in the set more desirable than the other policies. Our main result
states both necessary and sufficient conditions for the existence of such
reward functions. We also show that for every non-degenerate set of
deterministic policies, there exists a multidimensional Markov reward function
that characterizes it
| [
{
"version": "v1",
"created": "Sat, 22 Jul 2023 23:17:44 GMT"
}
] | 1,690,243,200,000 | [
[
"Miura",
"Shuwa",
""
]
] |
2307.12289 | Nicola Gigante | Renato Acampora and Luca Geatti and Nicola Gigante and Angelo
Montanari and Valentino Picotti | Controller Synthesis for Timeline-based Games | arXiv admin note: substantial text overlap with arXiv:2209.10319 This
is a submission to the LMCS journal of the journal version of 2209.10319 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | In the timeline-based approach to planning, the evolution over time of a set
of state variables (the timelines) is governed by a set of temporal
constraints. Traditional timeline-based planning systems excel at the
integration of planning with execution by handling temporal uncertainty. In
order to handle general nondeterminism as well, the concept of timeline-based
games has been recently introduced. It has been proved that finding whether a
winning strategy exists for such games is 2EXPTIME-complete. However, a
concrete approach to synthesize controllers implementing such strategies is
missing. This paper fills this gap, by providing an effective and
computationally optimal approach to controller synthesis for timeline-based
games.
| [
{
"version": "v1",
"created": "Sun, 23 Jul 2023 10:52:20 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Apr 2024 07:19:29 GMT"
}
] | 1,712,707,200,000 | [
[
"Acampora",
"Renato",
""
],
[
"Geatti",
"Luca",
""
],
[
"Gigante",
"Nicola",
""
],
[
"Montanari",
"Angelo",
""
],
[
"Picotti",
"Valentino",
""
]
] |
2307.12620 | Fran\c{c}ois Laferri\`ere | Pedro Cabalar, Mart\'in Di\'eguez, Fran\c{c}ois Laferri\`ere, Torsten
Schaub | Past-present temporal programs over finite traces | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extensions of Answer Set Programming with language constructs from temporal
logics, such as temporal equilibrium logic over finite traces (TELf), provide
an expressive computational framework for modeling dynamic applications. In
this paper, we study the so-called past-present syntactic subclass, which
consists of a set of logic programming rules whose body references to the past
and head to the present. Such restriction ensures that the past remains
independent of the future, which is the case in most dynamic domains. We extend
the definitions of completion and loop formulas to the case of past-present
formulas, which allows capturing the temporal stable models of a set of
past-present temporal programs by means of an LTLf expression.
| [
{
"version": "v1",
"created": "Mon, 24 Jul 2023 08:50:12 GMT"
},
{
"version": "v2",
"created": "Sat, 20 Jan 2024 14:14:12 GMT"
}
] | 1,705,968,000,000 | [
[
"Cabalar",
"Pedro",
""
],
[
"Diéguez",
"Martín",
""
],
[
"Laferrière",
"François",
""
],
[
"Schaub",
"Torsten",
""
]
] |
2307.12626 | Jingxuan Wei | Jingxuan Wei, Cheng Tan, Zhangyang Gao, Linzhuang Sun, Siyuan Li,
Bihui Yu, Ruifeng Guo, Stan Z. Li | Enhancing Human-like Multi-Modal Reasoning: A New Challenging Dataset
and Comprehensive Framework | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal reasoning is a critical component in the pursuit of artificial
intelligence systems that exhibit human-like intelligence, especially when
tackling complex tasks. While the chain-of-thought (CoT) technique has gained
considerable attention, the existing ScienceQA dataset, which focuses on
multimodal scientific questions and explanations from elementary and high
school textbooks, lacks a comprehensive evaluation of diverse approaches. To
address this gap, we present COCO Multi-Modal Reasoning(COCO-MMR) dataset, a
novel dataset that encompasses an extensive collection of open-ended questions,
rationales, and answers derived from the large object dataset COCO. Unlike
previous datasets that rely on multiple-choice questions, our dataset pioneers
the use of open-ended questions in the context of multimodal CoT, introducing a
more challenging problem that effectively assesses the reasoning capability of
CoT models. Through comprehensive evaluations and detailed analyses, we provide
valuable insights and propose innovative techniques, including multi-hop
cross-modal attention and sentence-level contrastive learning, to enhance the
image and text encoders. Extensive experiments demonstrate the efficacy of the
proposed dataset and techniques, offering novel perspectives for advancing
multimodal reasoning. The data and code are available at
\href{https://github.com/weijingxuan/COCO-MMR}{https://github.com/weijingxuan/COCO-MMR}.
| [
{
"version": "v1",
"created": "Mon, 24 Jul 2023 08:58:25 GMT"
},
{
"version": "v2",
"created": "Mon, 25 Sep 2023 15:57:35 GMT"
}
] | 1,695,686,400,000 | [
[
"Wei",
"Jingxuan",
""
],
[
"Tan",
"Cheng",
""
],
[
"Gao",
"Zhangyang",
""
],
[
"Sun",
"Linzhuang",
""
],
[
"Li",
"Siyuan",
""
],
[
"Yu",
"Bihui",
""
],
[
"Guo",
"Ruifeng",
""
],
[
"Li",
"Stan Z.",
""
]
] |
2307.12933 | Ruonan Jia | Chuming Li, Ruonan Jia, Jie Liu, Yinmin Zhang, Yazhe Niu, Yaodong
Yang, Yu Liu, Wanli Ouyang | Theoretically Guaranteed Policy Improvement Distilled from Model-Based
Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Model-based reinforcement learning (RL) has demonstrated remarkable successes
on a range of continuous control tasks due to its high sample efficiency. To
save the computation cost of conducting planning online, recent practices tend
to distill optimized action sequences into an RL policy during the training
phase. Although the distillation can incorporate both the foresight of planning
and the exploration ability of RL policies, the theoretical understanding of
these methods is yet unclear. In this paper, we extend the policy improvement
step of Soft Actor-Critic (SAC) by developing an approach to distill from
model-based planning to the policy. We then demonstrate that such an approach
of policy improvement has a theoretical guarantee of monotonic improvement and
convergence to the maximum value defined in SAC. We discuss effective design
choices and implement our theory as a practical algorithm -- Model-based
Planning Distilled to Policy (MPDP) -- that updates the policy jointly over
multiple future time steps. Extensive experiments show that MPDP achieves
better sample efficiency and asymptotic performance than both model-free and
model-based planning algorithms on six continuous control benchmark tasks in
MuJoCo.
| [
{
"version": "v1",
"created": "Mon, 24 Jul 2023 16:52:31 GMT"
}
] | 1,690,243,200,000 | [
[
"Li",
"Chuming",
""
],
[
"Jia",
"Ruonan",
""
],
[
"Liu",
"Jie",
""
],
[
"Zhang",
"Yinmin",
""
],
[
"Niu",
"Yazhe",
""
],
[
"Yang",
"Yaodong",
""
],
[
"Liu",
"Yu",
""
],
[
"Ouyang",
"Wanli",
""
]
] |
2307.13453 | Alexey Skrynnik | Yelisey Pitanov, Alexey Skrynnik, Anton Andreychuk, Konstantin
Yakovlev, Aleksandr Panov | Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Results | The paper is accepted to HAIS 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this work we study a well-known and challenging problem of Multi-agent
Pathfinding, when a set of agents is confined to a graph, each agent is
assigned a unique start and goal vertices and the task is to find a set of
collision-free paths (one for each agent) such that each agent reaches its
respective goal. We investigate how to utilize Monte-Carlo Tree Search (MCTS)
to solve the problem. Although MCTS was shown to demonstrate superior
performance in a wide range of problems like playing antagonistic games (e.g.
Go, Chess etc.), discovering faster matrix multiplication algorithms etc., its
application to the problem at hand was not well studied before. To this end we
introduce an original variant of MCTS, tailored to multi-agent pathfinding. The
crux of our approach is how the reward, that guides MCTS, is computed.
Specifically, we use individual paths to assist the agents with the the
goal-reaching behavior, while leaving them freedom to get off the track if it
is needed to avoid collisions. We also use a dedicated decomposition technique
to reduce the branching factor of the tree search procedure. Empirically we
show that the suggested method outperforms the baseline planning algorithm that
invokes heuristic search, e.g. A*, at each re-planning step.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2023 12:33:53 GMT"
}
] | 1,690,329,600,000 | [
[
"Pitanov",
"Yelisey",
""
],
[
"Skrynnik",
"Alexey",
""
],
[
"Andreychuk",
"Anton",
""
],
[
"Yakovlev",
"Konstantin",
""
],
[
"Panov",
"Aleksandr",
""
]
] |
2307.13549 | Bharath Muppasani | Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Raghava
Mutharaju, Michael N. Huhns, Vignesh Narayanan | A Planning Ontology to Represent and Exploit Planning Knowledge for
Performance Efficiency | Ontology, Automated Planning, Planner Improvement | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Ontologies are known for their ability to organize rich metadata, support the
identification of novel insights via semantic queries, and promote reuse. In
this paper, we consider the problem of automated planning, where the objective
is to find a sequence of actions that will move an agent from an initial state
of the world to a desired goal state. We hypothesize that given a large number
of available planners and diverse planning domains; they carry essential
information that can be leveraged to identify suitable planners and improve
their performance for a domain. We use data on planning domains and planners
from the International Planning Competition (IPC) to construct a planning
ontology and demonstrate via experiments in two use cases that the ontology can
lead to the selection of promising planners and improving their performance
using macros - a form of action ordering constraints extracted from planning
ontology. We also make the planning ontology and associated resources available
to the community to promote further research.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2023 14:51:07 GMT"
}
] | 1,690,329,600,000 | [
[
"Muppasani",
"Bharath",
""
],
[
"Pallagani",
"Vishal",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Mutharaju",
"Raghava",
""
],
[
"Huhns",
"Michael N.",
""
],
[
"Narayanan",
"Vignesh",
""
]
] |
2307.13552 | Bharath Muppasani | Bharath Muppasani, Vishal Pallagani, Biplav Srivastava, Forest
Agostinelli | On Solving the Rubik's Cube with Domain-Independent Planners Using
Standard Representations | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Rubik's Cube (RC) is a well-known and computationally challenging puzzle that
has motivated AI researchers to explore efficient alternative representations
and problem-solving methods. The ideal situation for planning here is that a
problem be solved optimally and efficiently represented in a standard notation
using a general-purpose solver and heuristics. The fastest solver today for RC
is DeepCubeA with a custom representation, and another approach is with
Scorpion planner with State-Action-Space+ (SAS+) representation. In this paper,
we present the first RC representation in the popular PDDL language so that the
domain becomes more accessible to PDDL planners, competitions, and knowledge
engineering tools, and is more human-readable. We then bridge across existing
approaches and compare performance. We find that in one comparable experiment,
DeepCubeA (trained with 12 RC actions) solves all problems with varying
complexities, albeit only 78.5% are optimal plans. For the same problem set,
Scorpion with SAS+ representation and pattern database heuristics solves 61.50%
problems optimally, while FastDownward with PDDL representation and FF
heuristic solves 56.50% problems, out of which 79.64% of the plans generated
were optimal. Our study provides valuable insights into the trade-offs between
representational choice and plan optimality that can help researchers design
future strategies for challenging domains combining general-purpose solving
methods (planning, reinforcement learning), heuristics, and representations
(standard or custom).
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2023 14:52:23 GMT"
},
{
"version": "v2",
"created": "Mon, 21 Aug 2023 12:35:36 GMT"
}
] | 1,692,662,400,000 | [
[
"Muppasani",
"Bharath",
""
],
[
"Pallagani",
"Vishal",
""
],
[
"Srivastava",
"Biplav",
""
],
[
"Agostinelli",
"Forest",
""
]
] |
2307.13582 | Xiang Yin | Xiang Yin, Nico Potyka, Francesca Toni | Argument Attribution Explanations in Quantitative Bipolar Argumentation
Frameworks (Technical Report) | Accepted at the European Conference on Artificial Intelligence (ECAI)
2023 Conference | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Argumentative explainable AI has been advocated by several in recent years,
with an increasing interest on explaining the reasoning outcomes of
Argumentation Frameworks (AFs). While there is a considerable body of research
on qualitatively explaining the reasoning outcomes of AFs with
debates/disputes/dialogues in the spirit of extension-based semantics,
explaining the quantitative reasoning outcomes of AFs under gradual semantics
has not received much attention, despite widespread use in applications. In
this paper, we contribute to filling this gap by proposing a novel theory of
Argument Attribution Explanations (AAEs) by incorporating the spirit of feature
attribution from machine learning in the context of Quantitative Bipolar
Argumentation Frameworks (QBAFs): whereas feature attribution is used to
determine the influence of features towards outputs of machine learning models,
AAEs are used to determine the influence of arguments towards topic arguments
of interest. We study desirable properties of AAEs, including some new ones and
some partially adapted from the literature to our setting. To demonstrate the
applicability of our AAEs in practice, we conclude by carrying out two case
studies in the scenarios of fake news detection and movie recommender systems.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2023 15:36:33 GMT"
},
{
"version": "v2",
"created": "Mon, 31 Jul 2023 09:47:11 GMT"
},
{
"version": "v3",
"created": "Fri, 4 Aug 2023 18:03:36 GMT"
}
] | 1,691,452,800,000 | [
[
"Yin",
"Xiang",
""
],
[
"Potyka",
"Nico",
""
],
[
"Toni",
"Francesca",
""
]
] |
2307.13815 | Jiajun Zhang | Jiajun Zhang, Georgina Cosma, Sarah Bugby, Jason Watkins | ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and
Classification Models | 6 pages, 5 figures, accepted in 2023 IEEE symposium series on
computational intelligence (SSCI) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial intelligence (AI) reasoning and explainable AI (XAI) tasks have
gained popularity recently, enabling users to explain the predictions or
decision processes of AI models. This paper introduces Forest Monkey (FM), a
toolkit designed to reason the outputs of any AI-based defect detection and/or
classification model with data explainability. Implemented as a Python package,
FM takes input in the form of dataset folder paths (including original images,
ground truth labels, and predicted labels) and provides a set of charts and a
text file to illustrate the reasoning results and suggest possible
improvements. The FM toolkit consists of processes such as feature extraction
from predictions to reasoning targets, feature extraction from images to defect
characteristics, and a decision tree-based AI-Reasoner. Additionally, this
paper investigates the time performance of the FM toolkit when applied to four
AI models with different datasets. Lastly, a tutorial is provided to guide
users in performing reasoning tasks using the FM toolkit.
| [
{
"version": "v1",
"created": "Tue, 25 Jul 2023 20:53:31 GMT"
},
{
"version": "v2",
"created": "Tue, 10 Oct 2023 10:22:31 GMT"
}
] | 1,696,982,400,000 | [
[
"Zhang",
"Jiajun",
""
],
[
"Cosma",
"Georgina",
""
],
[
"Bugby",
"Sarah",
""
],
[
"Watkins",
"Jason",
""
]
] |
2307.14355 | Astrid Rakow | Astrid Rakow | Framing Relevance for Safety-Critical Autonomous Systems | arXiv admin note: text overlap with arXiv:2209.14038 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We are in the process of building complex highly autonomous systems that have
build-in beliefs, perceive their environment and exchange information. These
systems construct their respective world view and based on it they plan their
future manoeuvres, i.e., they choose their actions in order to establish their
goals based on their prediction of the possible futures. Usually these systems
face an overwhelming flood of information provided by a variety of sources
where by far not everything is relevant. The goal of our work is to develop a
formal approach to determine what is relevant for a safety critical autonomous
system at its current mission, i.e., what information suffices to build an
appropriate world view to accomplish its mission goals.
| [
{
"version": "v1",
"created": "Sun, 23 Jul 2023 18:41:11 GMT"
}
] | 1,690,502,400,000 | [
[
"Rakow",
"Astrid",
""
]
] |
2307.14660 | Hayyan Helal | Hayyan Helal, Gerhard Lakemeyer | Multi-Valued Partial Order Plans in Numeric Planning | 10 pages, 3 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Many planning formalisms allow for mixing numeric with Boolean effects.
However, most of these formalisms are undecidable. In this paper, we will
analyze possible causes for this undecidability by studying the number of
different occurrences of actions, an approach that proved useful for metric
fluents before. We will start by reformulating a numeric planning problem known
as restricted tasks as a search problem. We will then show how an NP-complete
fragment of numeric planning can be found by using heuristics. To achieve this,
we will develop the idea of multi-valued partial order plans, a least
committing compact representation for (sequential and parallel) plans. Finally,
we will study optimization techniques for this representation to incorporate
soft preconditions.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2023 07:24:30 GMT"
}
] | 1,690,502,400,000 | [
[
"Helal",
"Hayyan",
""
],
[
"Lakemeyer",
"Gerhard",
""
]
] |
2307.14669 | Gian Carlo Milanese | Gian Carlo Milanese, Gabriella Pasi | Fuzzy order-sorted feature logic | Accepted for publication in Fuzzy Sets and Systems | null | 10.1016/j.fss.2023.108800 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Order-Sorted Feature (OSF) logic is a knowledge representation and reasoning
language based on function-denoting feature symbols and set-denoting sort
symbols ordered in a subsumption lattice. OSF logic allows the construction of
record-like terms that represent classes of entities and that are themselves
ordered in a subsumption relation. The unification algorithm for such
structures provides an efficient calculus of type subsumption, which has been
applied in computational linguistics and implemented in constraint logic
programming languages such as LOGIN and LIFE and automated reasoners such as
CEDAR. This work generalizes OSF logic to a fuzzy setting. We give a flexible
definition of a fuzzy subsumption relation which generalizes Zadeh's inclusion
between fuzzy sets. Based on this definition we define a fuzzy semantics of OSF
logic where sort symbols and OSF terms denote fuzzy sets. We extend the
subsumption relation to OSF terms and prove that it constitutes a fuzzy partial
order with the property that two OSF terms are subsumed by one another in the
crisp sense if and only if their subsumption degree is greater than 0. We show
how to find the greatest lower bound of two OSF terms by unifying them and how
to compute the subsumption degree between two OSF terms, and we provide the
complexity of these operations.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2023 07:47:54 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Nov 2023 08:32:09 GMT"
}
] | 1,701,129,600,000 | [
[
"Milanese",
"Gian Carlo",
""
],
[
"Pasi",
"Gabriella",
""
]
] |
2307.14893 | Tiago de Lima | Tiago de Lima, Emiliano Lorini and Fran\c{c}ois Schwarzentruber | Base-based Model Checking for Multi-Agent Only Believing (long version) | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present a novel semantics for the language of multi-agent only believing
exploiting belief bases, and show how to use it for automatically checking
formulas of this language and of its dynamic extension with private belief
expansion operators. We provide a PSPACE algorithm for model checking relying
on a reduction to QBF and alternative dedicated algorithm relying on the
exploration of the state space. We present an implementation of the QBF-based
algorithm and some experimental results on computation time in a concrete
example.
| [
{
"version": "v1",
"created": "Thu, 27 Jul 2023 14:35:42 GMT"
}
] | 1,690,502,400,000 | [
[
"de Lima",
"Tiago",
""
],
[
"Lorini",
"Emiliano",
""
],
[
"Schwarzentruber",
"François",
""
]
] |
2307.15451 | Alessandro Burigana | Alessandro Burigana, Paolo Felli and Marco Montali | DELPHIC: Practical DEL Planning via Possibilities (Extended Version) | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Dynamic Epistemic Logic (DEL) provides a framework for epistemic planning
that is capable of representing non-deterministic actions, partial
observability, higher-order knowledge and both factual and epistemic change.
The high expressivity of DEL challenges existing epistemic planners, which
typically can handle only restricted fragments of the whole framework. The goal
of this work is to push the envelop of practical DEL planning, ultimately
aiming for epistemic planners to be able to deal with the full range of
features offered by DEL. Towards this goal, we question the traditional
semantics of DEL, defined in terms on Kripke models. In particular, we propose
an equivalent semantics defined using, as main building block, so-called
possibilities: non well-founded objects representing both factual properties of
the world, and what agents consider to be possible. We call the resulting
framework DELPHIC. We argue that DELPHIC indeed provides a more compact
representation of epistemic states. To substantiate this claim, we implement
both approaches in ASP and we set up an experimental evaluation to compare
DELPHIC with the traditional, Kripke-based approach. The evaluation confirms
that DELPHIC outperforms the traditional approach in space and time.
| [
{
"version": "v1",
"created": "Fri, 28 Jul 2023 10:09:45 GMT"
}
] | 1,690,761,600,000 | [
[
"Burigana",
"Alessandro",
""
],
[
"Felli",
"Paolo",
""
],
[
"Montali",
"Marco",
""
]
] |
2307.15485 | Alessandro Burigana | Alessandro Burigana, Paolo Felli, Marco Montali and Nicolas Troquard | A Semantic Approach to Decidability in Epistemic Planning (Extended
Version) | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | The use of Dynamic Epistemic Logic (DEL) in multi-agent planning has led to a
widely adopted action formalism that can handle nondeterminism, partial
observability and arbitrary knowledge nesting. As such expressive power comes
at the cost of undecidability, several decidable fragments have been isolated,
mainly based on syntactic restrictions of the action formalism. In this paper,
we pursue a novel semantic approach to achieve decidability. Namely, rather
than imposing syntactical constraints, the semantic approach focuses on the
axioms of the logic for epistemic planning. Specifically, we augment the logic
of knowledge S5$_n$ and with an interaction axiom called (knowledge)
commutativity, which controls the ability of agents to unboundedly reason on
the knowledge of other agents. We then provide a threefold contribution. First,
we show that the resulting epistemic planning problem is decidable. In doing
so, we prove that our framework admits a finitary non-fixpoint characterization
of common knowledge, which is of independent interest. Second, we study
different generalizations of the commutativity axiom, with the goal of
obtaining decidability for more expressive fragments of DEL. Finally, we show
that two well-known epistemic planning systems based on action templates, when
interpreted under the setting of knowledge, conform to the commutativity axiom,
hence proving their decidability.
| [
{
"version": "v1",
"created": "Fri, 28 Jul 2023 11:26:26 GMT"
}
] | 1,690,761,600,000 | [
[
"Burigana",
"Alessandro",
""
],
[
"Felli",
"Paolo",
""
],
[
"Montali",
"Marco",
""
],
[
"Troquard",
"Nicolas",
""
]
] |
2307.16387 | Jia Li | Jia Li, Xiang Li | Relation-First Modeling Paradigm for Causal Representation Learning
toward the Development of AGI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The traditional i.i.d.-based learning paradigm faces inherent challenges in
addressing causal relationships, which has become increasingly evident with the
rise of applications in causal representation learning.
Our understanding of causality naturally requires a perspective as the
creator rather than observer, as the ``what...if'' questions only hold within
the possible world we conceive. The traditional perspective limits capturing
dynamic causal outcomes and leads to compensatory efforts such as the reliance
on hidden confounders. This paper lays the groundwork for the new perspective,
which enables the \emph{relation-first} modeling paradigm for causality. Also,
it introduces the Relation-Indexed Representation Learning (RIRL) as a
practical implementation, supported by experiments that validate its efficacy.
| [
{
"version": "v1",
"created": "Mon, 31 Jul 2023 03:32:59 GMT"
},
{
"version": "v10",
"created": "Mon, 20 Nov 2023 02:06:57 GMT"
},
{
"version": "v11",
"created": "Mon, 27 Nov 2023 02:10:23 GMT"
},
{
"version": "v12",
"created": "Thu, 7 Dec 2023 03:30:24 GMT"
},
{
"version": "v13",
"created": "Thu, 14 Dec 2023 04:55:05 GMT"
},
{
"version": "v14",
"created": "Fri, 16 Feb 2024 19:16:00 GMT"
},
{
"version": "v15",
"created": "Mon, 26 Feb 2024 21:33:21 GMT"
},
{
"version": "v16",
"created": "Thu, 29 Feb 2024 05:56:44 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Aug 2023 01:58:44 GMT"
},
{
"version": "v3",
"created": "Sat, 5 Aug 2023 01:58:47 GMT"
},
{
"version": "v4",
"created": "Sat, 12 Aug 2023 01:32:48 GMT"
},
{
"version": "v5",
"created": "Fri, 15 Sep 2023 03:15:29 GMT"
},
{
"version": "v6",
"created": "Sat, 23 Sep 2023 00:03:15 GMT"
},
{
"version": "v7",
"created": "Sun, 1 Oct 2023 18:01:16 GMT"
},
{
"version": "v8",
"created": "Sun, 15 Oct 2023 19:47:37 GMT"
},
{
"version": "v9",
"created": "Mon, 23 Oct 2023 23:09:20 GMT"
}
] | 1,709,596,800,000 | [
[
"Li",
"Jia",
""
],
[
"Li",
"Xiang",
""
]
] |
2307.16780 | Jesse Heyninck | Jesse Heyninck and Badran Raddaoui and Christian Stra{\ss}er | Ranking-based Argumentation Semantics Applied to Logical Argumentation
(full version) | Accepted for the 32nd International Joint Conference on Artificial
Intelligence (IJCAI 2023). Full version including proofs | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In formal argumentation, a distinction can be made between extension-based
semantics, where sets of arguments are either (jointly) accepted or not, and
ranking-based semantics, where grades of acceptability are assigned to
arguments. Another important distinction is that between abstract approaches,
that abstract away from the content of arguments, and structured approaches,
that specify a method of constructing argument graphs on the basis of a
knowledge base. While ranking-based semantics have been extensively applied to
abstract argumentation, few work has been done on ranking-based semantics for
structured argumentation. In this paper, we make a systematic investigation
into the behaviour of ranking-based semantics applied to existing formalisms
for structured argumentation. We show that a wide class of ranking-based
semantics gives rise to so-called culpability measures, and are relatively
robust to specific choices in argument construction methods.
| [
{
"version": "v1",
"created": "Mon, 31 Jul 2023 15:44:33 GMT"
}
] | 1,690,848,000,000 | [
[
"Heyninck",
"Jesse",
""
],
[
"Raddaoui",
"Badran",
""
],
[
"Straßer",
"Christian",
""
]
] |
2308.00560 | Yubin Xiao | Yubin Xiao, Di Wang, Boyang Li, Huanhuan Chen, Wei Pang, Xuan Wu, Hao
Li, Dong Xu, Yanchun Liang, and You Zhou | Reinforcement Learning-based Non-Autoregressive Solver for Traveling
Salesman Problems | 14 pages, 5 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Traveling Salesman Problem (TSP) is a well-known combinatorial
optimization problem with broad real-world applications. Recently, neural
networks have gained popularity in this research area because they provide
strong heuristic solutions to TSPs. Compared to autoregressive neural
approaches, non-autoregressive (NAR) networks exploit the inference parallelism
to elevate inference speed but suffer from comparatively low solution quality.
In this paper, we propose a novel NAR model named NAR4TSP, which incorporates a
specially designed architecture and an enhanced reinforcement learning
strategy. To the best of our knowledge, NAR4TSP is the first TSP solver that
successfully combines RL and NAR networks. The key lies in the incorporation of
NAR network output decoding into the training process. NAR4TSP efficiently
represents TSP encoded information as rewards and seamlessly integrates it into
reinforcement learning strategies, while maintaining consistent TSP sequence
constraints during both training and testing phases. Experimental results on
both synthetic and real-world TSP instances demonstrate that NAR4TSP
outperforms four state-of-the-art models in terms of solution quality,
inference speed, and generalization to unseen scenarios.
| [
{
"version": "v1",
"created": "Tue, 1 Aug 2023 14:00:31 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Oct 2023 01:47:29 GMT"
}
] | 1,697,673,600,000 | [
[
"Xiao",
"Yubin",
""
],
[
"Wang",
"Di",
""
],
[
"Li",
"Boyang",
""
],
[
"Chen",
"Huanhuan",
""
],
[
"Pang",
"Wei",
""
],
[
"Wu",
"Xuan",
""
],
[
"Li",
"Hao",
""
],
[
"Xu",
"Dong",
""
],
[
"Liang",
"Yanchun",
""
],
[
"Zhou",
"You",
""
]
] |
2308.01105 | Baifan Zhou | Zhipeng Tan, Baifan Zhou, Zhuoxun Zheng, Ognjen Savkovic, Ziqi Huang,
Irlan-Grangel Gonzalez, Ahmet Soylu, Evgeny Kharlamov | Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring:
A Bosch Case | Paper accepted at ISWC2023 In-Use track | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recently there has been a series of studies in knowledge graph embedding
(KGE), which attempts to learn the embeddings of the entities and relations as
numerical vectors and mathematical mappings via machine learning (ML). However,
there has been limited research that applies KGE for industrial problems in
manufacturing. This paper investigates whether and to what extent KGE can be
used for an important problem: quality monitoring for welding in manufacturing
industry, which is an impactful process accounting for production of millions
of cars annually. The work is in line with Bosch research of data-driven
solutions that intends to replace the traditional way of destroying cars, which
is extremely costly and produces waste. The paper tackles two very challenging
questions simultaneously: how large the welding spot diameter is; and to which
car body the welded spot belongs to. The problem setting is difficult for
traditional ML because there exist a high number of car bodies that should be
assigned as class labels. We formulate the problem as link prediction, and
experimented popular KGE methods on real industry data, with consideration of
literals. Our results reveal both limitations and promising aspects of adapted
KGE methods.
| [
{
"version": "v1",
"created": "Wed, 2 Aug 2023 12:22:35 GMT"
}
] | 1,691,020,800,000 | [
[
"Tan",
"Zhipeng",
""
],
[
"Zhou",
"Baifan",
""
],
[
"Zheng",
"Zhuoxun",
""
],
[
"Savkovic",
"Ognjen",
""
],
[
"Huang",
"Ziqi",
""
],
[
"Gonzalez",
"Irlan-Grangel",
""
],
[
"Soylu",
"Ahmet",
""
],
[
"Kharlamov",
"Evgeny",
""
]
] |
2308.01375 | Robert Maier | Robert Maier, Andreas Schlattl, Thomas Guess, J\"urgen Mottok | CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic
Graphical Models | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal probabilistic graph-based models have gained widespread utility,
enabling the modeling of cause-and-effect relationships across diverse domains.
With their rising adoption in new areas, such as automotive system safety and
machine learning, the need for an integrated lifecycle framework akin to DevOps
and MLOps has emerged. Currently, a process reference for organizations
interested in employing causal engineering is missing. To address this gap and
foster widespread industrial adoption, we propose CausalOps, a novel lifecycle
framework for causal model development and application. By defining key
entities, dependencies, and intermediate artifacts generated during causal
engineering, we establish a consistent vocabulary and workflow model. This work
contextualizes causal model usage across different stages and stakeholders,
outlining a holistic view of creating and maintaining them. CausalOps' aim is
to drive the adoption of causal methods in practical applications within
interested organizations and the causality community.
| [
{
"version": "v1",
"created": "Wed, 2 Aug 2023 18:26:43 GMT"
},
{
"version": "v2",
"created": "Tue, 5 Sep 2023 13:47:26 GMT"
}
] | 1,693,958,400,000 | [
[
"Maier",
"Robert",
""
],
[
"Schlattl",
"Andreas",
""
],
[
"Guess",
"Thomas",
""
],
[
"Mottok",
"Jürgen",
""
]
] |
2308.01556 | Zhengyang Zhang | Zhang Zhengyang and Dong Wei and Liu jun and Sun Xinya and Ji Yindong | A Global Transport Capacity Risk Prediction Method for Rail Transit
Based on Gaussian Bayesian Network | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Aiming at the prediction problem of transport capacity risk caused by the
mismatch between the carrying capacity of rail transit network and passenger
flow demand, this paper proposes an explainable prediction method of rail
transit network transport capacity risk based on linear Gaussian Bayesian
network. This method obtains the training data of the prediction model based on
the simulation model of the rail transit system with a three-layer structure
including rail transit network, train flow and passenger flow. A Bayesian
network structure construction method based on the topology of the rail transit
network is proposed, and the MLE (Maximum Likelihood Estimation) method is used
to realize the parameter learning of the Bayesian network. Finally, the
effectiveness of the proposed method is verified by simulation examples.
| [
{
"version": "v1",
"created": "Thu, 3 Aug 2023 06:36:13 GMT"
}
] | 1,691,107,200,000 | [
[
"Zhengyang",
"Zhang",
""
],
[
"Wei",
"Dong",
""
],
[
"jun",
"Liu",
""
],
[
"Xinya",
"Sun",
""
],
[
"Yindong",
"Ji",
""
]
] |
2308.01597 | Stefano Borgo | Stefano Borgo, Roberta Ferrario, Aldo Gangemi, Nicola Guarino, Claudio
Masolo, Daniele Porello, Emilio M. Sanfilippo, Laure Vieu | DOLCE: A Descriptive Ontology for Linguistic and Cognitive Engineering | 25 pages, 7 figures | Applied Ontology 17 (2022):45-69 | 10.3233/AO-210259 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | DOLCE, the first top-level (foundational) ontology to be axiomatized, has
remained stable for twenty years and today is broadly used in a variety of
domains. DOLCE is inspired by cognitive and linguistic considerations and aims
to model a commonsense view of reality, like the one human beings exploit in
everyday life in areas as diverse as socio-technical systems, manufacturing,
financial transactions and cultural heritage. DOLCE clearly lists the
ontological choices it is based upon, relies on philosophical principles, is
richly formalized, and is built according to well-established ontological
methodologies, e.g. OntoClean. Because of these features, it has inspired most
of the existing top-level ontologies and has been used to develop or improve
standards and public domain resources (e.g. CIDOC CRM, DBpedia and WordNet).
Being a foundational ontology, DOLCE is not directly concerned with domain
knowledge. Its purpose is to provide the general categories and relations
needed to give a coherent view of reality, to integrate domain knowledge, and
to mediate across domains. In these 20 years DOLCE has shown that applied
ontologies can be stable and that interoperability across reference and domain
ontologies is a reality. This paper briefly introduces the ontology and shows
how to use it on a few modeling cases.
| [
{
"version": "v1",
"created": "Thu, 3 Aug 2023 08:03:19 GMT"
}
] | 1,691,107,200,000 | [
[
"Borgo",
"Stefano",
""
],
[
"Ferrario",
"Roberta",
""
],
[
"Gangemi",
"Aldo",
""
],
[
"Guarino",
"Nicola",
""
],
[
"Masolo",
"Claudio",
""
],
[
"Porello",
"Daniele",
""
],
[
"Sanfilippo",
"Emilio M.",
""
],
[
"Vieu",
"Laure",
""
]
] |
2308.01732 | Christian Jilek | Christian Jilek, Markus Schr\"oder, Heiko Maus, Sven Schwarz, Andreas
Dengel | Towards Self-organizing Personal Knowledge Assistants in Evolving
Corporate Memories | 73 pages, 22 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper presents a retrospective overview of a decade of research in our
department towards self-organizing personal knowledge assistants in evolving
corporate memories. Our research is typically inspired by real-world problems
and often conducted in interdisciplinary collaborations with research and
industry partners. We summarize past experiments and results comprising topics
like various ways of knowledge graph construction in corporate and personal
settings, Managed Forgetting and (Self-organizing) Context Spaces as a novel
approach to Personal Information Management (PIM) and knowledge work support.
Past results are complemented by an overview of related work and some of our
latest findings not published so far. Last, we give an overview of our related
industry use cases including a detailed look into CoMem, a Corporate Memory
based on our presented research already in productive use and providing
challenges for further research. Many contributions are only first steps in new
directions with still a lot of untapped potential, especially with regard to
further increasing the automation in PIM and knowledge work support.
| [
{
"version": "v1",
"created": "Thu, 3 Aug 2023 12:48:32 GMT"
}
] | 1,691,107,200,000 | [
[
"Jilek",
"Christian",
""
],
[
"Schröder",
"Markus",
""
],
[
"Maus",
"Heiko",
""
],
[
"Schwarz",
"Sven",
""
],
[
"Dengel",
"Andreas",
""
]
] |
2308.02317 | Rohan Agarwal | Rohan Agarwal, Zhiyu Lin, Mark Riedl | A Controllable Co-Creative Agent for Game System Design | Thesis | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Many advancements have been made in procedural content generation for games,
and with mixed-initiative co-creativity, have the potential for great benefits
to human designers. However, co-creative systems for game generation are
typically limited to specific genres, rules, or games, limiting the creativity
of the designer. We seek to model games abstractly enough to apply to any
genre, focusing on designing game systems and mechanics, and create a
controllable, co-creative agent that can collaborate on these designs. We
present a model of games using state-machine-like components and resource
flows, a set of controllable metrics, a design evaluator simulating
playthroughs with these metrics, and an evolutionary design balancer and
generator. We find this system to be both able to express a wide range of games
and able to be human-controllable for future co-creative applications.
| [
{
"version": "v1",
"created": "Fri, 4 Aug 2023 13:34:51 GMT"
}
] | 1,691,366,400,000 | [
[
"Agarwal",
"Rohan",
""
],
[
"Lin",
"Zhiyu",
""
],
[
"Riedl",
"Mark",
""
]
] |
2308.02457 | Jiapu Wang | Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu,
Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, Wen Gao | A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and
Prospects | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Temporal characteristics are prominently evident in a substantial volume of
knowledge, which underscores the pivotal role of Temporal Knowledge Graphs
(TKGs) in both academia and industry. However, TKGs often suffer from
incompleteness for three main reasons: the continuous emergence of new
knowledge, the weakness of the algorithm for extracting structured information
from unstructured data, and the lack of information in the source dataset.
Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted
increasing attention, aiming to predict missing items based on the available
information. In this paper, we provide a comprehensive review of TKGC methods
and their details. Specifically, this paper mainly consists of three
components, namely, 1)Background, which covers the preliminaries of TKGC
methods, loss functions required for training, as well as the dataset and
evaluation protocol; 2)Interpolation, that estimates and predicts the missing
elements or set of elements through the relevant available information. It
further categorizes related TKGC methods based on how to process temporal
information; 3)Extrapolation, which typically focuses on continuous TKGs and
predicts future events, and then classifies all extrapolation methods based on
the algorithms they utilize. We further pinpoint the challenges and discuss
future research directions of TKGC.
| [
{
"version": "v1",
"created": "Fri, 4 Aug 2023 16:49:54 GMT"
}
] | 1,691,366,400,000 | [
[
"Wang",
"Jiapu",
""
],
[
"Wang",
"Boyue",
""
],
[
"Qiu",
"Meikang",
""
],
[
"Pan",
"Shirui",
""
],
[
"Xiong",
"Bo",
""
],
[
"Liu",
"Heng",
""
],
[
"Luo",
"Linhao",
""
],
[
"Liu",
"Tengfei",
""
],
[
"Hu",
"Yongli",
""
],
[
"Yin",
"Baocai",
""
],
[
"Gao",
"Wen",
""
]
] |
2308.02558 | Vasant Dhar | Vasant Dhar | The Paradigm Shifts in Artificial Intelligence | 14 pages, 1 figure, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Kuhn's framework of scientific progress (Kuhn, 1962) provides a useful
framing of the paradigm shifts that have occurred in Artificial Intelligence
over the last 60 years. The framework is also useful in understanding what is
arguably a new paradigm shift in AI, signaled by the emergence of large
pre-trained systems such as GPT-3, on which conversational agents such as
ChatGPT are based. Such systems make intelligence a commoditized general
purpose technology that is configurable to applications. In this paper, I
summarize the forces that led to the rise and fall of each paradigm, and
discuss the pressing issues and risks associated with the current paradigm
shift in AI.
| [
{
"version": "v1",
"created": "Wed, 2 Aug 2023 19:38:24 GMT"
}
] | 1,691,452,800,000 | [
[
"Dhar",
"Vasant",
""
]
] |
2308.02561 | Qi Wang | Qi Wang, Yanghe Feng, Jincai Huang, Yiqin Lv, Zheng Xie, Xiaoshan Gao | Large-scale Generative Simulation Artificial Intelligence: the Next
Hotspot in Generative AI | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The concept of GenAI has been developed for decades. Until recently, it has
impressed us with substantial breakthroughs in natural language processing and
computer vision, actively engaging in industrial scenarios. Noticing the
practical challenges, e.g., limited learning resources, and overly dependencies
on scientific discovery empiricism, we nominate large-scale generative
simulation artificial intelligence (LS-GenAI) as the next hotspot for GenAI to
connect.
| [
{
"version": "v1",
"created": "Thu, 3 Aug 2023 02:04:04 GMT"
}
] | 1,691,452,800,000 | [
[
"Wang",
"Qi",
""
],
[
"Feng",
"Yanghe",
""
],
[
"Huang",
"Jincai",
""
],
[
"Lv",
"Yiqin",
""
],
[
"Xie",
"Zheng",
""
],
[
"Gao",
"Xiaoshan",
""
]
] |
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