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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2306.03606 | Daniel Daza | Daniel Daza, Dimitrios Alivanistos, Payal Mitra, Thom Pijnenburg,
Michael Cochez, Paul Groth | BioBLP: A Modular Framework for Learning on Multimodal Biomedical
Knowledge Graphs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge graphs (KGs) are an important tool for representing complex
relationships between entities in the biomedical domain. Several methods have
been proposed for learning embeddings that can be used to predict new links in
such graphs. Some methods ignore valuable attribute data associated with
entities in biomedical KGs, such as protein sequences, or molecular graphs.
Other works incorporate such data, but assume that entities can be represented
with the same data modality. This is not always the case for biomedical KGs,
where entities exhibit heterogeneous modalities that are central to their
representation in the subject domain.
We propose a modular framework for learning embeddings in KGs with entity
attributes, that allows encoding attribute data of different modalities while
also supporting entities with missing attributes. We additionally propose an
efficient pretraining strategy for reducing the required training runtime. We
train models using a biomedical KG containing approximately 2 million triples,
and evaluate the performance of the resulting entity embeddings on the tasks of
link prediction, and drug-protein interaction prediction, comparing against
methods that do not take attribute data into account. In the standard link
prediction evaluation, the proposed method results in competitive, yet lower
performance than baselines that do not use attribute data. When evaluated in
the task of drug-protein interaction prediction, the method compares favorably
with the baselines. We find settings involving low degree entities, which make
up for a substantial amount of the set of entities in the KG, where our method
outperforms the baselines. Our proposed pretraining strategy yields
significantly higher performance while reducing the required training runtime.
Our implementation is available at https://github.com/elsevier-AI-Lab/BioBLP .
| [
{
"version": "v1",
"created": "Tue, 6 Jun 2023 11:49:38 GMT"
}
]
| 1,686,096,000,000 | [
[
"Daza",
"Daniel",
""
],
[
"Alivanistos",
"Dimitrios",
""
],
[
"Mitra",
"Payal",
""
],
[
"Pijnenburg",
"Thom",
""
],
[
"Cochez",
"Michael",
""
],
[
"Groth",
"Paul",
""
]
]
|
2306.03980 | Omar Costilla Reyes | Juan Sebastian Canas, Francisco Gomez, Omar Costilla-Reyes | Counterfactual Explanations and Predictive Models to Enhance Clinical
Decision-Making in Schizophrenia using Digital Phenotyping | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Clinical practice in psychiatry is burdened with the increased demand for
healthcare services and the scarce resources available. New paradigms of health
data powered with machine learning techniques could open the possibility to
improve clinical workflow in critical stages of clinical assessment and
treatment in psychiatry. In this work, we propose a machine learning system
capable of predicting, detecting, and explaining individual changes in symptoms
of patients with Schizophrenia by using behavioral digital phenotyping data. We
forecast symptoms of patients with an error rate below 10%. The system detects
decreases in symptoms using changepoint algorithms and uses counterfactual
explanations as a recourse in a simulated continuous monitoring scenario in
healthcare. Overall, this study offers valuable insights into the performance
and potential of counterfactual explanations, predictive models, and
change-point detection within a simulated clinical workflow. These findings lay
the foundation for further research to explore additional facets of the
workflow, aiming to enhance its effectiveness and applicability in real-world
healthcare settings. By leveraging these components, the goal is to develop an
actionable, interpretable, and trustworthy integrative decision support system
that combines real-time clinical assessments with sensor-based inputs.
| [
{
"version": "v1",
"created": "Tue, 6 Jun 2023 19:33:03 GMT"
}
]
| 1,686,182,400,000 | [
[
"Canas",
"Juan Sebastian",
""
],
[
"Gomez",
"Francisco",
""
],
[
"Costilla-Reyes",
"Omar",
""
]
]
|
2306.04019 | Alex Fukunaga | Yu Liu and Ryo Kuroiwa and Alex Fukunaga | Learning Search-Space Specific Heuristics Using Neural Networks | Proceedings of ICAPS Workshop on Heuristics and Search for
Domain-independent Planning (HSDIP) 2020, pp.1-8 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose and evaluate a system which learns a neuralnetwork heuristic
function for forward search-based, satisficing classical planning. Our system
learns distance-to-goal estimators from scratch, given a single PDDL training
instance. Training data is generated by backward regression search or by
backward search from given or guessed goal states. In domains such as the
24-puzzle where all instances share the same search space, such heuristics can
also be reused across all instances in the domain. We show that this relatively
simple system can perform surprisingly well, sometimes competitive with
well-known domain-independent heuristics.
| [
{
"version": "v1",
"created": "Tue, 6 Jun 2023 21:22:32 GMT"
}
]
| 1,686,182,400,000 | [
[
"Liu",
"Yu",
""
],
[
"Kuroiwa",
"Ryo",
""
],
[
"Fukunaga",
"Alex",
""
]
]
|
2306.04025 | Mahault Albarracin Mx | Mahault Albarracin, In\^es Hip\'olito, Safae Essafi Tremblay, Jason G.
Fox, Gabriel Ren\'e, Karl Friston, Maxwell J. D. Ramstead | Designing explainable artificial intelligence with active inference: A
framework for transparent introspection and decision-making | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper investigates the prospect of developing human-interpretable,
explainable artificial intelligence (AI) systems based on active inference and
the free energy principle. We first provide a brief overview of active
inference, and in particular, of how it applies to the modeling of
decision-making, introspection, as well as the generation of overt and covert
actions. We then discuss how active inference can be leveraged to design
explainable AI systems, namely, by allowing us to model core features of
``introspective'' processes and by generating useful, human-interpretable
models of the processes involved in decision-making. We propose an architecture
for explainable AI systems using active inference. This architecture
foregrounds the role of an explicit hierarchical generative model, the
operation of which enables the AI system to track and explain the factors that
contribute to its own decisions, and whose structure is designed to be
interpretable and auditable by human users. We outline how this architecture
can integrate diverse sources of information to make informed decisions in an
auditable manner, mimicking or reproducing aspects of human-like consciousness
and introspection. Finally, we discuss the implications of our findings for
future research in AI, and the potential ethical considerations of developing
AI systems with (the appearance of) introspective capabilities.
| [
{
"version": "v1",
"created": "Tue, 6 Jun 2023 21:38:09 GMT"
}
]
| 1,686,182,400,000 | [
[
"Albarracin",
"Mahault",
""
],
[
"Hipólito",
"Inês",
""
],
[
"Tremblay",
"Safae Essafi",
""
],
[
"Fox",
"Jason G.",
""
],
[
"René",
"Gabriel",
""
],
[
"Friston",
"Karl",
""
],
[
"Ramstead",
"Maxwell J. D.",
""
]
]
|
2306.04031 | Gabriel Poesia | Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, Noah D. Goodman | Certified Deductive Reasoning with Language Models | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Language models often achieve higher accuracy when reasoning step-by-step in
complex tasks. However, even when arriving at a correct final answer, their
rationales are often logically unsound or inconsistent. This is a major issue
when reliable reasoning traces are needed, such when fine-tuning on
model-generated reasoning for self-improvement. To tackle these issues, we
introduce a class of tools for language models called \emph{guides}, that use
state and incremental constraints to guide generation. A guide can be invoked
by the model to constrain its own generation to a set of valid statements given
by the tool. In turn, the model's choices can change the guide's state. We show
how a general system for logical reasoning can be used as a guide, which we
call \textsc{LogicGuide}. Given a reasoning problem in natural language, a
model can formalize its assumptions for \textsc{LogicGuide} and guarantee that
its step-by-step reasoning is sound. In experiments on PrOntoQA, ProofWriter
and Syllogism Validity datasets, \textsc{LogicGuide} significantly improves the
performance of GPT-3, GPT-3.5 Turbo and LLaMA (accuracy gains up to 35\%),
while drastically reducing \emph{content effects} -- the interference between
unwanted prior assumptions and reasoning, which humans and language models
suffer from. We then explore bootstrapping GPT-3.5 Turbo and LLaMA using their
own reasoning traces. We find that LogicGuide is critical: by training only on
certified self-generated reasoning, models can self-improve, avoiding learning
from their own hallucinations. Moreover, bootstrapped models enjoy significant
boosts on ReClor, a challenging real-world reasoning dataset, even when not
relying on formalization at inference time.
| [
{
"version": "v1",
"created": "Tue, 6 Jun 2023 21:49:00 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Nov 2023 01:53:31 GMT"
}
]
| 1,699,488,000,000 | [
[
"Poesia",
"Gabriel",
""
],
[
"Gandhi",
"Kanishk",
""
],
[
"Zelikman",
"Eric",
""
],
[
"Goodman",
"Noah D.",
""
]
]
|
2306.04141 | Ziv Epstein | Ziv Epstein, Aaron Hertzmann, Laura Herman, Robert Mahari, Morgan R.
Frank, Matthew Groh, Hope Schroeder, Amy Smith, Memo Akten, Jessica Fjeld,
Hany Farid, Neil Leach, Alex Pentland, and Olga Russakovsky | Art and the science of generative AI: A deeper dive | This white paper is an expanded version of Epstein et al 2023
published in Science Perspectives on July 16, 2023 which you can find at the
following DOI: 10.1126/science.adh4451 | null | 10.1126/science.adh4451 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | A new class of tools, colloquially called generative AI, can produce
high-quality artistic media for visual arts, concept art, music, fiction,
literature, video, and animation. The generative capabilities of these tools
are likely to fundamentally alter the creative processes by which creators
formulate ideas and put them into production. As creativity is reimagined, so
too may be many sectors of society. Understanding the impact of generative AI -
and making policy decisions around it - requires new interdisciplinary
scientific inquiry into culture, economics, law, algorithms, and the
interaction of technology and creativity. We argue that generative AI is not
the harbinger of art's demise, but rather is a new medium with its own distinct
affordances. In this vein, we consider the impacts of this new medium on
creators across four themes: aesthetics and culture, legal questions of
ownership and credit, the future of creative work, and impacts on the
contemporary media ecosystem. Across these themes, we highlight key research
questions and directions to inform policy and beneficial uses of the
technology.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 04:27:51 GMT"
}
]
| 1,689,724,800,000 | [
[
"Epstein",
"Ziv",
""
],
[
"Hertzmann",
"Aaron",
""
],
[
"Herman",
"Laura",
""
],
[
"Mahari",
"Robert",
""
],
[
"Frank",
"Morgan R.",
""
],
[
"Groh",
"Matthew",
""
],
[
"Schroeder",
"Hope",
""
],
[
"Smith",
"Amy",
""
],
[
"Akten",
"Memo",
""
],
[
"Fjeld",
"Jessica",
""
],
[
"Farid",
"Hany",
""
],
[
"Leach",
"Neil",
""
],
[
"Pentland",
"Alex",
""
],
[
"Russakovsky",
"Olga",
""
]
]
|
2306.04152 | Haiqin Yang | Junxian Zhou, Haiqin Yang, Yuxuan He, Hao Mou, Junbo Yang | A Unified One-Step Solution for Aspect Sentiment Quad Prediction | 15 pages, 12 tables, 3 figures, ACL Findings | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Aspect sentiment quad prediction (ASQP) is a challenging yet significant
subtask in aspect-based sentiment analysis as it provides a complete
aspect-level sentiment structure. However, existing ASQP datasets are usually
small and low-density, hindering technical advancement. To expand the capacity,
in this paper, we release two new datasets for ASQP, which contain the
following characteristics: larger size, more words per sample, and higher
density. With such datasets, we unveil the shortcomings of existing strong ASQP
baselines and therefore propose a unified one-step solution for ASQP, namely
One-ASQP, to detect the aspect categories and to identify the
aspect-opinion-sentiment (AOS) triplets simultaneously. Our One-ASQP holds
several unique advantages: (1) by separating ASQP into two subtasks and solving
them independently and simultaneously, we can avoid error propagation in
pipeline-based methods and overcome slow training and inference in
generation-based methods; (2) by introducing sentiment-specific horns tagging
schema in a token-pair-based two-dimensional matrix, we can exploit deeper
interactions between sentiment elements and efficiently decode the AOS
triplets; (3) we design ``[NULL]'' token can help us effectively identify the
implicit aspects or opinions. Experiments on two benchmark datasets and our
released two datasets demonstrate the advantages of our One-ASQP. The two new
datasets are publicly released at
\url{https://www.github.com/Datastory-CN/ASQP-Datasets}.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 05:00:01 GMT"
}
]
| 1,686,182,400,000 | [
[
"Zhou",
"Junxian",
""
],
[
"Yang",
"Haiqin",
""
],
[
"He",
"Yuxuan",
""
],
[
"Mou",
"Hao",
""
],
[
"Yang",
"Junbo",
""
]
]
|
2306.04274 | Richard Blythman | Richard Blythman, Mohamed Arshath, Salvatore Vivona, Jakub Sm\'ekal,
Hithesh Shaji | Decentralized Technologies for AI Hubs | arXiv admin note: substantial text overlap with arXiv:2210.16651 | 2022 Conference on Neural Information Processing Systems Workshops | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | AI requires heavy amounts of storage and compute with assets that are
commonly stored in AI Hubs. AI Hubs have contributed significantly to the
democratization of AI. However, existing implementations are associated with
certain benefits and limitations that stem from the underlying infrastructure
and governance systems with which they are built. These limitations include
high costs, lack of monetization and reward, lack of control and difficulty of
reproducibility. In the current work, we explore the potential of decentralized
technologies - such as Web3 wallets, peer-to-peer marketplaces, storage and
compute, and DAOs - to address some of these issues. We suggest that these
infrastructural components can be used in combination in the design and
construction of decentralized AI Hubs.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 09:18:56 GMT"
}
]
| 1,686,182,400,000 | [
[
"Blythman",
"Richard",
""
],
[
"Arshath",
"Mohamed",
""
],
[
"Vivona",
"Salvatore",
""
],
[
"Smékal",
"Jakub",
""
],
[
"Shaji",
"Hithesh",
""
]
]
|
2306.04287 | Jeremy Straub | Jonathan Rivard, Jeremy Straub | Extension of the Blackboard Architecture with Common Properties and
Generic Rules | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The Blackboard Architecture provides a mechanism for embodying data, decision
making and actuation. Its versatility has been demonstrated across a wide
number of application areas. However, it lacks the capability to directly model
organizational, spatial and other relationships which may be useful in
decision-making, in addition to the propositional logic embodied in the
rule-fact-action network. Previous work has proposed the use of container
objects and links as a mechanism to simultaneously model these organizational
and other relationships, while leaving the operational logic modeled in the
rules, facts and actions. While containers facilitate this modeling, their
utility is limited by the need to manually define them. For systems which may
have multiple instances of a particular type of object and which may build
their network autonomously, based on sensing, the reuse of logical structures
facilitates operations and reduces storage and processing needs. This paper,
thus, presents and assesses two additional concepts to add to the Blackboard
Architecture: common properties and generic rules. Common properties are facts
associated with containers which are defined as representing the same
information across the various objects that they are associated with. Generic
rules provide logical propositions that use these generic rules across links
and apply to any objects matching their definition. The potential uses of these
two new concepts are discussed herein and their impact on system performance is
characterized.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 09:40:13 GMT"
}
]
| 1,686,182,400,000 | [
[
"Rivard",
"Jonathan",
""
],
[
"Straub",
"Jeremy",
""
]
]
|
2306.04289 | Jeremy Straub | Jordan Milbrath, Jeremy Straub | Introduction and Assessment of the Addition of Links and Containers to
the Blackboard Architecture | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The Blackboard Architecture provides a mechanism for storing data and logic
and using it to make decisions that impact the application environment that the
Blackboard Architecture network models. While rule-fact-action networks can
represent numerous types of data, the relationships that can be easily modeled
are limited by the propositional logic nature of the rule-fact network
structure. This paper proposes and evaluates the inclusion of containers and
links in the Blackboard Architecture. These objects are designed to allow them
to model organizational, physical, spatial and other relationships that cannot
be readily or efficiently implemented as Boolean logic rules. Containers group
related facts together and can be nested to implement complex relationships.
Links interconnect containers that have a relationship that is relevant to
their organizational purpose. Both objects, together, facilitate new ways of
using the Blackboard Architecture and enable or simply its use for complex
tasks that have multiple types of relationships that need to be considered
during operations.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 09:41:46 GMT"
}
]
| 1,686,182,400,000 | [
[
"Milbrath",
"Jordan",
""
],
[
"Straub",
"Jeremy",
""
]
]
|
2306.04324 | Vadim Porvatov | Vladimir Mashurov, Vaagn Chopurian, Vadim Porvatov, Arseny Ivanov,
Natalia Semenova | GCT-TTE: Graph Convolutional Transformer for Travel Time Estimation | 17 pages, 7 figures, 4 tables; supplementary included; accepted in
Journal of Big Data | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper introduces a new transformer-based model for the problem of travel
time estimation. The key feature of the proposed GCT-TTE architecture is the
utilization of different data modalities capturing different properties of an
input path. Along with the extensive study regarding the model configuration,
we implemented and evaluated a sufficient number of actual baselines for
path-aware and path-blind settings. The conducted computational experiments
have confirmed the viability of our pipeline, which outperformed
state-of-the-art models on both considered datasets. Additionally, GCT-TTE was
deployed as a web service accessible for further experiments with user-defined
routes.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 10:44:13 GMT"
},
{
"version": "v2",
"created": "Sun, 15 Oct 2023 08:30:00 GMT"
}
]
| 1,697,500,800,000 | [
[
"Mashurov",
"Vladimir",
""
],
[
"Chopurian",
"Vaagn",
""
],
[
"Porvatov",
"Vadim",
""
],
[
"Ivanov",
"Arseny",
""
],
[
"Semenova",
"Natalia",
""
]
]
|
2306.04335 | Mbithe Nzomo | Mbithe Nzomo and Deshendran Moodley | Semantic Technologies in Sensor-Based Personal Health Monitoring
Systems: A Systematic Mapping Study | 40 pages, 6 figures. Under review in the Semantic Web Journal (SWJ).
https://www.semantic-web-journal.net/content/semantic-technologies-sensor-based-personal-health-monitoring-systems-systematic-mapping | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In recent years, there has been an increased focus on early detection,
prevention, and prediction of diseases. This, together with advances in sensor
technology and the Internet of Things, has led to accelerated efforts in the
development of personal health monitoring systems. Semantic technologies have
emerged as an effective way to not only deal with the issue of interoperability
associated with heterogeneous health sensor data, but also to represent expert
health knowledge to support complex reasoning required for decision-making.
This study evaluates the state of the art in the use of semantic technologies
in sensor-based personal health monitoring systems. Using a systematic
approach, a total of 40 systems representing the state of the art in the field
are analysed. Through this analysis, six key challenges that such systems must
overcome for optimal and effective health monitoring are identified:
interoperability, context awareness, situation detection, situation prediction,
decision support, and uncertainty handling. The study critically evaluates the
extent to which these systems incorporate semantic technologies to deal with
these challenges and identifies the prominent architectures, system development
and evaluation methodologies that are used. The study provides a comprehensive
mapping of the field, identifies inadequacies in the state of the art, and
provides recommendations for future research directions.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 11:02:35 GMT"
}
]
| 1,686,182,400,000 | [
[
"Nzomo",
"Mbithe",
""
],
[
"Moodley",
"Deshendran",
""
]
]
|
2306.04410 | Arsham Gholamzadeh Khoee | Arsham Gholamzadeh Khoee, Alireza Javaheri, Saeed Reza Kheradpisheh
and Mohammad Ganjtabesh | Meta-Learning in Spiking Neural Networks with Reward-Modulated STDP | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The human brain constantly learns and rapidly adapts to new situations by
integrating acquired knowledge and experiences into memory. Developing this
capability in machine learning models is considered an important goal of AI
research since deep neural networks perform poorly when there is limited data
or when they need to adapt quickly to new unseen tasks. Meta-learning models
are proposed to facilitate quick learning in low-data regimes by employing
absorbed information from the past. Although some models have recently been
introduced that reached high-performance levels, they are not biologically
plausible. We have proposed a bio-plausible meta-learning model inspired by the
hippocampus and the prefrontal cortex using spiking neural networks with a
reward-based learning system. Our proposed model includes a memory designed to
prevent catastrophic forgetting, a phenomenon that occurs when meta-learning
models forget what they have learned as soon as the new task begins. Also, our
new model can easily be applied to spike-based neuromorphic devices and enables
fast learning in neuromorphic hardware. The final analysis will discuss the
implications and predictions of the model for solving few-shot classification
tasks. In solving these tasks, our model has demonstrated the ability to
compete with the existing state-of-the-art meta-learning techniques.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 13:08:46 GMT"
}
]
| 1,686,182,400,000 | [
[
"Khoee",
"Arsham Gholamzadeh",
""
],
[
"Javaheri",
"Alireza",
""
],
[
"Kheradpisheh",
"Saeed Reza",
""
],
[
"Ganjtabesh",
"Mohammad",
""
]
]
|
2306.04541 | Vincent Derkinderen | Vincent Derkinderen, Pedro Zuidberg Dos Martires, Samuel Kolb, Paolo
Morettin | Top-Down Knowledge Compilation for Counting Modulo Theories | 9 pages; submitted to Workshop on Counting and Sampling 2023 at
SAT2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Propositional model counting (#SAT) can be solved efficiently when the input
formula is in deterministic decomposable negation normal form (d-DNNF).
Translating an arbitrary formula into a representation that allows inference
tasks, such as counting, to be performed efficiently, is called knowledge
compilation. Top-down knowledge compilation is a state-of-the-art technique for
solving #SAT problems that leverages the traces of exhaustive DPLL search to
obtain d-DNNF representations. While knowledge compilation is well studied for
propositional approaches, knowledge compilation for the (quantifier free)
counting modulo theory setting (#SMT) has been studied to a much lesser degree.
In this paper, we discuss compilation strategies for #SMT. We specifically
advocate for a top-down compiler based on the traces of exhaustive DPLL(T)
search.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 15:46:28 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Nov 2023 16:21:18 GMT"
}
]
| 1,701,388,800,000 | [
[
"Derkinderen",
"Vincent",
""
],
[
"Martires",
"Pedro Zuidberg Dos",
""
],
[
"Kolb",
"Samuel",
""
],
[
"Morettin",
"Paolo",
""
]
]
|
2306.04750 | MD Abdullah Al Nasim | Tasmia Tahmida Jidney, Angona Biswas, MD Abdullah Al Nasim, Ismail
Hossain, Md Jahangir Alam, Sajedul Talukder, Mofazzal Hossain, Dr. Md Azim
Ullah | AutoML Systems For Medical Imaging | 11 pages, 4 figures; Acceptance of the chapter for the Springer book
"Data-driven approaches to medical imaging" | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | The integration of machine learning in medical image analysis can greatly
enhance the quality of healthcare provided by physicians. The combination of
human expertise and computerized systems can result in improved diagnostic
accuracy. An automated machine learning approach simplifies the creation of
custom image recognition models by utilizing neural architecture search and
transfer learning techniques. Medical imaging techniques are used to
non-invasively create images of internal organs and body parts for diagnostic
and procedural purposes. This article aims to highlight the potential
applications, strategies, and techniques of AutoML in medical imaging through
theoretical and empirical evidence.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 19:57:07 GMT"
},
{
"version": "v2",
"created": "Sat, 17 Jun 2023 17:24:05 GMT"
}
]
| 1,687,305,600,000 | [
[
"Jidney",
"Tasmia Tahmida",
""
],
[
"Biswas",
"Angona",
""
],
[
"Nasim",
"MD Abdullah Al",
""
],
[
"Hossain",
"Ismail",
""
],
[
"Alam",
"Md Jahangir",
""
],
[
"Talukder",
"Sajedul",
""
],
[
"Hossain",
"Mofazzal",
""
],
[
"Ullah",
"Dr. Md Azim",
""
]
]
|
2306.04792 | Nimrod Megiddo | Nimrod Megiddo | On the Use of Generative Models in Observational Causal Analysis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of a hypothetical generative model was been suggested for causal
analysis of observational data. The very assumption of a particular model is a
commitment to a certain set of variables and therefore to a certain set of
possible causes. Estimating the joint probability distribution of can be useful
for predicting values of variables in view of the observed values of others,
but it is not sufficient for inferring causal relationships. The model
describes a single observable distribution and cannot a chain of effects of
intervention that deviate from the observed distribution.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 21:29:49 GMT"
}
]
| 1,686,268,800,000 | [
[
"Megiddo",
"Nimrod",
""
]
]
|
2306.04806 | Pulkit Verma | Pulkit Verma, Rushang Karia, Siddharth Srivastava | Autonomous Capability Assessment of Sequential Decision-Making Systems
in Stochastic Settings (Extended Version) | NeurIPS 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is essential for users to understand what their AI systems can and can't
do in order to use them safely. However, the problem of enabling users to
assess AI systems with sequential decision-making (SDM) capabilities is
relatively understudied. This paper presents a new approach for modeling the
capabilities of black-box AI systems that can plan and act, along with the
possible effects and requirements for executing those capabilities in
stochastic settings. We present an active-learning approach that can
effectively interact with a black-box SDM system and learn an interpretable
probabilistic model describing its capabilities. Theoretical analysis of the
approach identifies the conditions under which the learning process is
guaranteed to converge to the correct model of the agent; empirical evaluations
on different agents and simulated scenarios show that this approach is few-shot
generalizable and can effectively describe the capabilities of arbitrary
black-box SDM agents in a sample-efficient manner.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 22:05:48 GMT"
},
{
"version": "v2",
"created": "Sat, 28 Oct 2023 19:43:36 GMT"
}
]
| 1,698,710,400,000 | [
[
"Verma",
"Pulkit",
""
],
[
"Karia",
"Rushang",
""
],
[
"Srivastava",
"Siddharth",
""
]
]
|
2306.04813 | Mark Roland Bercasio | Mark Bercasio, Allison Wong, Dustin Dannenhauer | Human in the Loop Novelty Generation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Developing artificial intelligence approaches to overcome novel, unexpected
circumstances is a difficult, unsolved problem. One challenge to advancing the
state of the art in novelty accommodation is the availability of testing
frameworks for evaluating performance against novel situations. Recent novelty
generation approaches in domains such as Science Birds and Monopoly leverage
human domain expertise during the search to discover new novelties. Such
approaches introduce human guidance before novelty generation occurs and yield
novelties that can be directly loaded into a simulated environment. We
introduce a new approach to novelty generation that uses abstract models of
environments (including simulation domains) that do not require
domain-dependent human guidance to generate novelties. A key result is a
larger, often infinite space of novelties capable of being generated, with the
trade-off being a requirement to involve human guidance to select and filter
novelties post generation. We describe our Human-in-the-Loop novelty generation
process using our open-source novelty generation library to test baseline
agents in two domains: Monopoly and VizDoom. Our results shows the
Human-in-the-Loop method enables users to develop, implement, test, and revise
novelties within 4 hours for both Monopoly and VizDoom domains.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 22:30:27 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Jun 2023 23:09:30 GMT"
}
]
| 1,686,700,800,000 | [
[
"Bercasio",
"Mark",
""
],
[
"Wong",
"Allison",
""
],
[
"Dannenhauer",
"Dustin",
""
]
]
|
2306.04814 | Shuwen Liu | Shuwen Liu, Bernardo Cuenca Grau, Ian Horrocks, Egor V. Kostylev | Revisiting Inferential Benchmarks for Knowledge Graph Completion | Accepted by the 20th International Conference on Principles of
Knowledge Representation and Reasoning (KR 2023) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Knowledge Graph (KG) completion is the problem of extending an incomplete KG
with missing facts. A key feature of Machine Learning approaches for KG
completion is their ability to learn inference patterns, so that the predicted
facts are the results of applying these patterns to the KG. Standard completion
benchmarks, however, are not well-suited for evaluating models' abilities to
learn patterns, because the training and test sets of these benchmarks are a
random split of a given KG and hence do not capture the causality of inference
patterns. We propose a novel approach for designing KG completion benchmarks
based on the following principles: there is a set of logical rules so that the
missing facts are the results of the rules' application; the training set
includes both premises matching rule antecedents and the corresponding
conclusions; the test set consists of the results of applying the rules to the
training set; the negative examples are designed to discourage the models from
learning rules not entailed by the rule set. We use our methodology to generate
several benchmarks and evaluate a wide range of existing KG completion systems.
Our results provide novel insights on the ability of existing models to induce
inference patterns from incomplete KGs.
| [
{
"version": "v1",
"created": "Wed, 7 Jun 2023 22:35:39 GMT"
}
]
| 1,686,268,800,000 | [
[
"Liu",
"Shuwen",
""
],
[
"Grau",
"Bernardo Cuenca",
""
],
[
"Horrocks",
"Ian",
""
],
[
"Kostylev",
"Egor V.",
""
]
]
|
2306.05003 | Domenico Gigante | Vita Santa Barletta, Danilo Caivano, Domenico Gigante and Azzurra
Ragone | A Rapid Review of Responsible AI frameworks: How to guide the
development of ethical AI | null | Proceedings of the International Conference on Evaluation and
Assessment in Software Engineering (EASE '23), June 14--16, 2023, Oulu,
Finland | 10.1145/3593434.3593478 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In the last years, the raise of Artificial Intelligence (AI), and its
pervasiveness in our lives, has sparked a flourishing debate about the ethical
principles that should lead its implementation and use in society. Driven by
these concerns, we conduct a rapid review of several frameworks providing
principles, guidelines, and/or tools to help practitioners in the development
and deployment of Responsible AI (RAI) applications. We map each framework
w.r.t. the different Software Development Life Cycle (SDLC) phases discovering
that most of these frameworks fall just in the Requirements Elicitation phase,
leaving the other phases uncovered. Very few of these frameworks offer
supporting tools for practitioners, and they are mainly provided by private
companies. Our results reveal that there is not a "catching-all" framework
supporting both technical and non-technical stakeholders in the implementation
of real-world projects. Our findings highlight the lack of a comprehensive
framework encompassing all RAI principles and all (SDLC) phases that could be
navigated by users with different skill sets and with different goals.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 07:47:18 GMT"
}
]
| 1,686,268,800,000 | [
[
"Barletta",
"Vita Santa",
""
],
[
"Caivano",
"Danilo",
""
],
[
"Gigante",
"Domenico",
""
],
[
"Ragone",
"Azzurra",
""
]
]
|
2306.05016 | Xinhang Li | Xinhang Li, Yiying Yang, Zheng Yuan, Zhe Wang, Qinwen Wang, Chen Xu,
Lei Li, Jianhua He and Lin Zhang | Progression Cognition Reinforcement Learning with Prioritized Experience
for Multi-Vehicle Pursuit | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing
suspects is important but very challenging due to its mission and safety
critical nature. While multi-agent reinforcement learning (MARL) algorithms
have been proposed for MVP problem in structured grid-pattern roads, the
existing algorithms use randomly training samples in centralized learning,
which leads to homogeneous agents showing low collaboration performance. For
the more challenging problem of pursuing multiple evading vehicles, these
algorithms typically select a fixed target evading vehicle for pursuing
vehicles without considering dynamic traffic situation, which significantly
reduces pursuing success rate. To address the above problems, this paper
proposes a Progression Cognition Reinforcement Learning with Prioritized
Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic
scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in
the global experience replay buffer according to the parameters of each MARL
agent. With the personalized and prioritized experience set selected via the
prioritization network, diversity is introduced to the learning process of
MARL, which can improve collaboration and task related performance.
Furthermore, PEPCRL-MVP employs an attention module to extract critical
features from complex urban traffic environments. These features are used to
develop progression cognition method to adaptively group pursuing vehicles.
Each group efficiently target one evading vehicle in dynamic driving
environments. Extensive experiments conducted with a simulator over
unstructured roads of an urban area show that PEPCRL-MVP is superior to other
state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency
by 3.95% over TD3-DMAP and its success rate is 34.78% higher than that of
MADDPG. Codes are open sourced.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 08:10:46 GMT"
}
]
| 1,686,268,800,000 | [
[
"Li",
"Xinhang",
""
],
[
"Yang",
"Yiying",
""
],
[
"Yuan",
"Zheng",
""
],
[
"Wang",
"Zhe",
""
],
[
"Wang",
"Qinwen",
""
],
[
"Xu",
"Chen",
""
],
[
"Li",
"Lei",
""
],
[
"He",
"Jianhua",
""
],
[
"Zhang",
"Lin",
""
]
]
|
2306.05069 | Masood Feyzbakhsh Rankooh | Masood Feyzbakhsh Rankooh and Tomi Janhunen | Capturing (Optimal) Relaxed Plans with Stable and Supported Models of
Logic Programs | Paper presented at the 39th International Conference on Logic
Programming (ICLP 2023), 14 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | We establish a novel relation between delete-free planning, an important task
for the AI Planning community also known as relaxed planning, and logic
programming. We show that given a planning problem, all subsets of actions that
could be ordered to produce relaxed plans for the problem can be bijectively
captured with stable models of a logic program describing the corresponding
relaxed planning problem. We also consider the supported model semantics of
logic programs, and introduce one causal and one diagnostic encoding of the
relaxed planning problem as logic programs, both capturing relaxed plans with
their supported models. Our experimental results show that these new encodings
can provide major performance gain when computing optimal relaxed plans, with
our diagnostic encoding outperforming state-of-the-art approaches to relaxed
planning regardless of the given time limit when measured on a wide collection
of STRIPS planning benchmarks.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 09:34:38 GMT"
}
]
| 1,686,268,800,000 | [
[
"Rankooh",
"Masood Feyzbakhsh",
""
],
[
"Janhunen",
"Tomi",
""
]
]
|
2306.05120 | Sepideh Pashami | Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan, Jakub Jakubowski,
Nuno Paiva, Narjes Davari, Szymon Bobek, Samaneh Jamshidi, Hamid Sarmadi,
Abdallah Alabdallah, Rita P. Ribeiro, Bruno Veloso, Moamar Sayed-Mouchaweh,
Lala Rajaoarisoa, Grzegorz J. Nalepa, Jo\~ao Gama | Explainable Predictive Maintenance | 51 pages, 9 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Explainable Artificial Intelligence (XAI) fills the role of a critical
interface fostering interactions between sophisticated intelligent systems and
diverse individuals, including data scientists, domain experts, end-users, and
more. It aids in deciphering the intricate internal mechanisms of ``black box''
Machine Learning (ML), rendering the reasons behind their decisions more
understandable. However, current research in XAI primarily focuses on two
aspects; ways to facilitate user trust, or to debug and refine the ML model.
The majority of it falls short of recognising the diverse types of explanations
needed in broader contexts, as different users and varied application areas
necessitate solutions tailored to their specific needs.
One such domain is Predictive Maintenance (PdM), an exploding area of
research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights
the gap between existing XAI methodologies and the specific requirements for
explanations within industrial applications, particularly the Predictive
Maintenance field. Despite explainability's crucial role, this subject remains
a relatively under-explored area, making this paper a pioneering attempt to
bring relevant challenges to the research community's attention. We provide an
overview of predictive maintenance tasks and accentuate the need and varying
purposes for corresponding explanations. We then list and describe XAI
techniques commonly employed in the literature, discussing their suitability
for PdM tasks. Finally, to make the ideas and claims more concrete, we
demonstrate XAI applied in four specific industrial use cases: commercial
vehicles, metro trains, steel plants, and wind farms, spotlighting areas
requiring further research.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 11:42:47 GMT"
}
]
| 1,686,268,800,000 | [
[
"Pashami",
"Sepideh",
""
],
[
"Nowaczyk",
"Slawomir",
""
],
[
"Fan",
"Yuantao",
""
],
[
"Jakubowski",
"Jakub",
""
],
[
"Paiva",
"Nuno",
""
],
[
"Davari",
"Narjes",
""
],
[
"Bobek",
"Szymon",
""
],
[
"Jamshidi",
"Samaneh",
""
],
[
"Sarmadi",
"Hamid",
""
],
[
"Alabdallah",
"Abdallah",
""
],
[
"Ribeiro",
"Rita P.",
""
],
[
"Veloso",
"Bruno",
""
],
[
"Sayed-Mouchaweh",
"Moamar",
""
],
[
"Rajaoarisoa",
"Lala",
""
],
[
"Nalepa",
"Grzegorz J.",
""
],
[
"Gama",
"João",
""
]
]
|
2306.05138 | Raphael Boige | Raphael Boige, Guillaume Richard, J\'er\'emie Dona, Thomas Pierrot,
Antoine Cully | Gradient-Informed Quality Diversity for the Illumination of Discrete
Spaces | null | GECCO 2023 Proceedings of the Genetic and Evolutionary Computation
Conference; Pages 119-128 | 10.1145/3583131.3590407 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Quality Diversity (QD) algorithms have been proposed to search for a large
collection of both diverse and high-performing solutions instead of a single
set of local optima. While early QD algorithms view the objective and
descriptor functions as black-box functions, novel tools have been introduced
to use gradient information to accelerate the search and improve overall
performance of those algorithms over continuous input spaces. However a broad
range of applications involve discrete spaces, such as drug discovery or image
generation. Exploring those spaces is challenging as they are combinatorially
large and gradients cannot be used in the same manner as in continuous spaces.
We introduce map-elites with a Gradient-Informed Discrete Emitter (ME-GIDE),
which extends QD optimisation with differentiable functions over discrete
search spaces. ME-GIDE leverages the gradient information of the objective and
descriptor functions with respect to its discrete inputs to propose
gradient-informed updates that guide the search towards a diverse set of high
quality solutions. We evaluate our method on challenging benchmarks including
protein design and discrete latent space illumination and find that our method
outperforms state-of-the-art QD algorithms in all benchmarks.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 12:04:52 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Sep 2023 08:28:46 GMT"
}
]
| 1,694,649,600,000 | [
[
"Boige",
"Raphael",
""
],
[
"Richard",
"Guillaume",
""
],
[
"Dona",
"Jérémie",
""
],
[
"Pierrot",
"Thomas",
""
],
[
"Cully",
"Antoine",
""
]
]
|
2306.05298 | No\'emi \'Eltet\H{o} | No\'emi \'Eltet\H{o} and Peter Dayan | Habits of Mind: Reusing Action Sequences for Efficient Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | When we exercise sequences of actions, their execution becomes more fluent
and precise. Here, we consider the possibility that exercised action sequences
can also be used to make planning faster and more accurate by focusing
expansion of the search tree on paths that have been frequently used in the
past, and by reducing deep planning problems to shallow ones via multi-step
jumps in the tree. To capture such sequences, we use a flexible Bayesian action
chunking mechanism which finds and exploits statistically reliable structure at
different scales. This gives rise to shorter or longer routines that can be
embedded into a Monte-Carlo tree search planner. We show the benefits of this
scheme using a physical construction task patterned after tangrams.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 15:42:56 GMT"
}
]
| 1,686,268,800,000 | [
[
"Éltető",
"Noémi",
""
],
[
"Dayan",
"Peter",
""
]
]
|
2306.05480 | Xiang Li | Xiang Li, Lu Zhang, Zihao Wu, Zhengliang Liu, Lin Zhao, Yixuan Yuan,
Jun Liu, Gang Li, Dajiang Zhu, Pingkun Yan, Quanzheng Li, Wei Liu, Tianming
Liu, and Dinggang Shen | Artificial General Intelligence for Medical Imaging | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this review, we explore the potential applications of Artificial General
Intelligence (AGI) models in healthcare, focusing on foundational Large
Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We
emphasize the importance of integrating clinical expertise, domain knowledge,
and multimodal capabilities into AGI models. In addition, we lay out key
roadmaps that guide the development and deployment of healthcare AGI models.
Throughout the review, we provide critical perspectives on the potential
challenges and pitfalls associated with deploying large-scale AGI models in the
medical field. This comprehensive review aims to offer insights into the future
implications of AGI in medical imaging, healthcare and beyond.
| [
{
"version": "v1",
"created": "Thu, 8 Jun 2023 18:04:13 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Jul 2023 01:52:58 GMT"
}
]
| 1,688,428,800,000 | [
[
"Li",
"Xiang",
""
],
[
"Zhang",
"Lu",
""
],
[
"Wu",
"Zihao",
""
],
[
"Liu",
"Zhengliang",
""
],
[
"Zhao",
"Lin",
""
],
[
"Yuan",
"Yixuan",
""
],
[
"Liu",
"Jun",
""
],
[
"Li",
"Gang",
""
],
[
"Zhu",
"Dajiang",
""
],
[
"Yan",
"Pingkun",
""
],
[
"Li",
"Quanzheng",
""
],
[
"Liu",
"Wei",
""
],
[
"Liu",
"Tianming",
""
],
[
"Shen",
"Dinggang",
""
]
]
|
2306.05731 | Nikolaos Rodis | Nikolaos Rodis, Christos Sardianos, Georgios Th. Papadopoulos,
Panagiotis Radoglou-Grammatikis, Panagiotis Sarigiannidis and Iraklis
Varlamis | Multimodal Explainable Artificial Intelligence: A Comprehensive Review
of Methodological Advances and Future Research Directions | 26 pages, 11 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The current study focuses on systematically analyzing the recent advances in
the field of Multimodal eXplainable Artificial Intelligence (MXAI). In
particular, the relevant primary prediction tasks and publicly available
datasets are initially described. Subsequently, a structured presentation of
the MXAI methods of the literature is provided, taking into account the
following criteria: a) The number of the involved modalities, b) The stage at
which explanations are produced, and c) The type of the adopted methodology
(i.e. mathematical formalism). Then, the metrics used for MXAI evaluation are
discussed. Finally, a comprehensive analysis of current challenges and future
research directions is provided.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2023 07:51:50 GMT"
}
]
| 1,686,528,000,000 | [
[
"Rodis",
"Nikolaos",
""
],
[
"Sardianos",
"Christos",
""
],
[
"Papadopoulos",
"Georgios Th.",
""
],
[
"Radoglou-Grammatikis",
"Panagiotis",
""
],
[
"Sarigiannidis",
"Panagiotis",
""
],
[
"Varlamis",
"Iraklis",
""
]
]
|
2306.05801 | Andrea Apicella | Andrea Apicella, Luca Di Lorenzo, Francesco Isgr\`o, Andrea Pollastro,
Roberto Prevete | Strategies to exploit XAI to improve classification systems | This work has been accepted to be presented to The 1st World
Conference on eXplainable Artificial Intelligence (xAI 2023), July 26-28,
2023 - Lisboa, Portugal | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explainable Artificial Intelligence (XAI) aims to provide insights into the
decision-making process of AI models, allowing users to understand their
results beyond their decisions. A significant goal of XAI is to improve the
performance of AI models by providing explanations for their decision-making
processes. However, most XAI literature focuses on how to explain an AI system,
while less attention has been given to how XAI methods can be exploited to
improve an AI system. In this work, a set of well-known XAI methods typically
used with Machine Learning (ML) classification tasks are investigated to verify
if they can be exploited, not just to provide explanations but also to improve
the performance of the model itself. To this aim, two strategies to use the
explanation to improve a classification system are reported and empirically
evaluated on three datasets: Fashion-MNIST, CIFAR10, and STL10. Results suggest
that explanations built by Integrated Gradients highlight input features that
can be effectively used to improve classification performance.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2023 10:38:26 GMT"
}
]
| 1,686,528,000,000 | [
[
"Apicella",
"Andrea",
""
],
[
"Di Lorenzo",
"Luca",
""
],
[
"Isgrò",
"Francesco",
""
],
[
"Pollastro",
"Andrea",
""
],
[
"Prevete",
"Roberto",
""
]
]
|
2306.06036 | Silvan Ferreira da Silva Junior | Silvan Ferreira, Allan Martins, Ivanovitch Silva | SNeL: A Structured Neuro-Symbolic Language for Entity-Based Multimodal
Scene Understanding | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the evolving landscape of artificial intelligence, multimodal and
Neuro-Symbolic paradigms stand at the forefront, with a particular emphasis on
the identification and interaction with entities and their relations across
diverse modalities. Addressing the need for complex querying and interaction in
this context, we introduce SNeL (Structured Neuro-symbolic Language), a
versatile query language designed to facilitate nuanced interactions with
neural networks processing multimodal data. SNeL's expressive interface enables
the construction of intricate queries, supporting logical and arithmetic
operators, comparators, nesting, and more. This allows users to target specific
entities, specify their properties, and limit results, thereby efficiently
extracting information from a scene. By aligning high-level symbolic reasoning
with low-level neural processing, SNeL effectively bridges the Neuro-Symbolic
divide. The language's versatility extends to a variety of data types,
including images, audio, and text, making it a powerful tool for multimodal
scene understanding. Our evaluations demonstrate SNeL's potential to reshape
the way we interact with complex neural networks, underscoring its efficacy in
driving targeted information extraction and facilitating a deeper understanding
of the rich semantics encapsulated in multimodal AI models.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2023 17:01:51 GMT"
}
]
| 1,686,528,000,000 | [
[
"Ferreira",
"Silvan",
""
],
[
"Martins",
"Allan",
""
],
[
"Silva",
"Ivanovitch",
""
]
]
|
2306.06272 | Shiwali Mohan | Shiwali Mohan, Wiktor Piotrowski, Roni Stern, Sachin Grover, Sookyung
Kim, Jacob Le, Johan De Kleer | A Domain-Independent Agent Architecture for Adaptive Operation in
Evolving Open Worlds | Under review in Artificial Intelligence Journal - Open World Learning
track | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Model-based reasoning agents are ill-equipped to act in novel situations in
which their model of the environment no longer sufficiently represents the
world. We propose HYDRA - a framework for designing model-based agents
operating in mixed discrete-continuous worlds, that can autonomously detect
when the environment has evolved from its canonical setup, understand how it
has evolved, and adapt the agents' models to perform effectively. HYDRA is
based upon PDDL+, a rich modeling language for planning in mixed,
discrete-continuous environments. It augments the planning module with visual
reasoning, task selection, and action execution modules for closed-loop
interaction with complex environments. HYDRA implements a novel meta-reasoning
process that enables the agent to monitor its own behavior from a variety of
aspects. The process employs a diverse set of computational methods to maintain
expectations about the agent's own behavior in an environment. Divergences from
those expectations are useful in detecting when the environment has evolved and
identifying opportunities to adapt the underlying models. HYDRA builds upon
ideas from diagnosis and repair and uses a heuristics-guided search over model
changes such that they become competent in novel conditions. The HYDRA
framework has been used to implement novelty-aware agents for three diverse
domains - CartPole++ (a higher dimension variant of a classic control problem),
Science Birds (an IJCAI competition problem), and PogoStick (a specific problem
domain in Minecraft). We report empirical observations from these domains to
demonstrate the efficacy of various components in the novelty meta-reasoning
process.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2023 21:54:13 GMT"
}
]
| 1,686,614,400,000 | [
[
"Mohan",
"Shiwali",
""
],
[
"Piotrowski",
"Wiktor",
""
],
[
"Stern",
"Roni",
""
],
[
"Grover",
"Sachin",
""
],
[
"Kim",
"Sookyung",
""
],
[
"Le",
"Jacob",
""
],
[
"De Kleer",
"Johan",
""
]
]
|
2306.06294 | Jiong Yang | Jiong Yang, Arijit Shaw, Teodora Baluta, Mate Soos, and Kuldeep S.
Meel | Explaining SAT Solving Using Causal Reasoning | 17 pages, 3 figures, to be published in SAT23 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The past three decades have witnessed notable success in designing efficient
SAT solvers, with modern solvers capable of solving industrial benchmarks
containing millions of variables in just a few seconds. The success of modern
SAT solvers owes to the widely-used CDCL algorithm, which lacks comprehensive
theoretical investigation. Furthermore, it has been observed that CDCL solvers
still struggle to deal with specific classes of benchmarks comprising only
hundreds of variables, which contrasts with their widespread use in real-world
applications. Consequently, there is an urgent need to uncover the inner
workings of these seemingly weak yet powerful black boxes.
In this paper, we present a first step towards this goal by introducing an
approach called CausalSAT, which employs causal reasoning to gain insights into
the functioning of modern SAT solvers. CausalSAT initially generates
observational data from the execution of SAT solvers and learns a structured
graph representing the causal relationships between the components of a SAT
solver. Subsequently, given a query such as whether a clause with low literals
blocks distance (LBD) has a higher clause utility, CausalSAT calculates the
causal effect of LBD on clause utility and provides an answer to the question.
We use CausalSAT to quantitatively verify hypotheses previously regarded as
"rules of thumb" or empirical findings such as the query above. Moreover,
CausalSAT can address previously unexplored questions, like which branching
heuristic leads to greater clause utility in order to study the relationship
between branching and clause management. Experimental evaluations using
practical benchmarks demonstrate that CausalSAT effectively fits the data,
verifies four "rules of thumb", and provides answers to three questions closely
related to implementing modern solvers.
| [
{
"version": "v1",
"created": "Fri, 9 Jun 2023 22:53:16 GMT"
}
]
| 1,686,614,400,000 | [
[
"Yang",
"Jiong",
""
],
[
"Shaw",
"Arijit",
""
],
[
"Baluta",
"Teodora",
""
],
[
"Soos",
"Mate",
""
],
[
"Meel",
"Kuldeep S.",
""
]
]
|
2306.06808 | Jiangwei Wang | Jiangwei Wang, Shuo Yang, Ziyan An, Songyang Han, Zhili Zhang, Rahul
Mangharam, Meiyi Ma, Fei Miao | Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic
Specifications | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Reward design is a key component of deep reinforcement learning, yet some
tasks and designer's objectives may be unnatural to define as a scalar cost
function. Among the various techniques, formal methods integrated with DRL have
garnered considerable attention due to their expressiveness and flexibility to
define the reward and requirements for different states and actions of the
agent. However, how to leverage Signal Temporal Logic (STL) to guide
multi-agent reinforcement learning reward design remains unexplored. Complex
interactions, heterogeneous goals and critical safety requirements in
multi-agent systems make this problem even more challenging. In this paper, we
propose a novel STL-guided multi-agent reinforcement learning framework. The
STL requirements are designed to include both task specifications according to
the objective of each agent and safety specifications, and the robustness
values of the STL specifications are leveraged to generate rewards. We validate
the advantages of our method through empirical studies. The experimental
results demonstrate significant reward performance improvements compared to
MARL without STL guidance, along with a remarkable increase in the overall
safety rate of the multi-agent systems.
| [
{
"version": "v1",
"created": "Sun, 11 Jun 2023 23:53:29 GMT"
},
{
"version": "v2",
"created": "Sun, 22 Oct 2023 20:37:40 GMT"
}
]
| 1,698,105,600,000 | [
[
"Wang",
"Jiangwei",
""
],
[
"Yang",
"Shuo",
""
],
[
"An",
"Ziyan",
""
],
[
"Han",
"Songyang",
""
],
[
"Zhang",
"Zhili",
""
],
[
"Mangharam",
"Rahul",
""
],
[
"Ma",
"Meiyi",
""
],
[
"Miao",
"Fei",
""
]
]
|
2306.06821 | Taisuke Sato | Taisuke Sato, Akihiro Takemura, Katsumi Inoue | Towards end-to-end ASP computation | 29 pages, 9 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We propose an end-to-end approach for answer set programming (ASP) and linear
algebraically compute stable models satisfying given constraints. The idea is
to implement Lin-Zhao's theorem \cite{Lin04} together with constraints directly
in vector spaces as numerical minimization of a cost function constructed from
a matricized normal logic program, loop formulas in Lin-Zhao's theorem and
constraints, thereby no use of symbolic ASP or SAT solvers involved in our
approach. We also propose precomputation that shrinks the program size and
heuristics for loop formulas to reduce computational difficulty. We empirically
test our approach with programming examples including the 3-coloring and
Hamiltonian cycle problems. As our approach is purely numerical and only
contains vector/matrix operations, acceleration by parallel technologies such
as many-cores and GPUs is expected.
| [
{
"version": "v1",
"created": "Mon, 12 Jun 2023 02:00:22 GMT"
},
{
"version": "v2",
"created": "Tue, 13 Jun 2023 08:11:20 GMT"
}
]
| 1,686,700,800,000 | [
[
"Sato",
"Taisuke",
""
],
[
"Takemura",
"Akihiro",
""
],
[
"Inoue",
"Katsumi",
""
]
]
|
2306.06841 | Jinwoo Nam | Hyeondey Kim, Jinwoo Nam, Minjae Lee, Yun Jegal, Kyungwoo Song | Leveraging Skill-to-Skill Supervision for Knowledge Tracing | AAAI2023 Artificial Intelligence for Education | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Knowledge tracing plays a pivotal role in intelligent tutoring systems. This
task aims to predict the probability of students answering correctly to
specific questions. To do so, knowledge tracing systems should trace the
knowledge state of the students by utilizing their problem-solving history and
knowledge about the problems. Recent advances in knowledge tracing models have
enabled better exploitation of problem solving history. However, knowledge
about problems has not been studied, as well compared to students' answering
histories. Knowledge tracing algorithms that incorporate knowledge directly are
important to settings with limited data or cold starts. Therefore, we consider
the problem of utilizing skill-to-skill relation to knowledge tracing. In this
work, we introduce expert labeled skill-to-skill relationships. Moreover, we
also provide novel methods to construct a knowledge-tracing model to leverage
human experts' insight regarding relationships between skills. The results of
an extensive experimental analysis show that our method outperformed a baseline
Transformer model. Furthermore, we found that the extent of our model's
superiority was greater in situations with limited data, which allows a smooth
cold start of our model.
| [
{
"version": "v1",
"created": "Mon, 12 Jun 2023 03:23:22 GMT"
}
]
| 1,686,614,400,000 | [
[
"Kim",
"Hyeondey",
""
],
[
"Nam",
"Jinwoo",
""
],
[
"Lee",
"Minjae",
""
],
[
"Jegal",
"Yun",
""
],
[
"Song",
"Kyungwoo",
""
]
]
|
2306.07126 | Jesse Heyninck | Jesse Heyninck and Ofer Arieli | Argumentative Characterizations of (Extended) Disjunctive Logic Programs | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This paper continues an established line of research about the relations
between argumentation theory, particularly assumption-based argumentation, and
different kinds of logic programs. In particular, we extend known result of
Caminada, Schultz and Toni by showing that assumption-based argumentation can
represent not only normal logic programs, but also disjunctive logic programs
and their extensions. For this, we consider some inference rules for
disjunction that the core logic of the argumentation frameworks should respect,
and show the correspondence to the handling of disjunctions in the heads of the
logic programs' rules.
| [
{
"version": "v1",
"created": "Mon, 12 Jun 2023 14:01:38 GMT"
}
]
| 1,686,614,400,000 | [
[
"Heyninck",
"Jesse",
""
],
[
"Arieli",
"Ofer",
""
]
]
|
2306.07353 | Damien Pellier | Damien Pellier, Alexandre Albore, Humbert Fiorino, Rafael Bailon-Ruiz | HDDL 2.1: Towards Defining a Formalism and a Semantics for Temporal HTN
Planning | 5 pages, International Workshop of Hierarchical Planning (ICAPS),
2023 | International Workshop of Hierarchical Planning (ICAPS), 2023 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Real world applications as in industry and robotics need modelling rich and
diverse automated planning problems. Their resolution usually requires
coordinated and concurrent action execution. In several cases, these problems
are naturally decomposed in a hierarchical way and expressed by a Hierarchical
Task Network (HTN) formalism.
HDDL, a hierarchical extension of the Planning Domain Definition Language
(PDDL), unlike PDDL 2.1 does not allow to represent planning problems with
numerical and temporal constraints, which are essential for real world
applications. We propose to fill the gap between HDDL and these operational
needs and to extend HDDL by taking inspiration from PDDL 2.1 in order to
express numerical and temporal expressions. This paper opens discussions on the
semantics and the syntax needed for a future HDDL 2.1 extension.
| [
{
"version": "v1",
"created": "Mon, 12 Jun 2023 18:21:23 GMT"
}
]
| 1,686,700,800,000 | [
[
"Pellier",
"Damien",
""
],
[
"Albore",
"Alexandre",
""
],
[
"Fiorino",
"Humbert",
""
],
[
"Bailon-Ruiz",
"Rafael",
""
]
]
|
2306.07542 | Xianliang Yang | Xianliang Yang, Zhihao Liu, Wei Jiang, Chuheng Zhang, Li Zhao, Lei
Song, Jiang Bian | A Versatile Multi-Agent Reinforcement Learning Benchmark for Inventory
Management | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multi-agent reinforcement learning (MARL) models multiple agents that
interact and learn within a shared environment. This paradigm is applicable to
various industrial scenarios such as autonomous driving, quantitative trading,
and inventory management. However, applying MARL to these real-world scenarios
is impeded by many challenges such as scaling up, complex agent interactions,
and non-stationary dynamics. To incentivize the research of MARL on these
challenges, we develop MABIM (Multi-Agent Benchmark for Inventory Management)
which is a multi-echelon, multi-commodity inventory management simulator that
can generate versatile tasks with these different challenging properties. Based
on MABIM, we evaluate the performance of classic operations research (OR)
methods and popular MARL algorithms on these challenging tasks to highlight
their weaknesses and potential.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 05:22:30 GMT"
}
]
| 1,686,700,800,000 | [
[
"Yang",
"Xianliang",
""
],
[
"Liu",
"Zhihao",
""
],
[
"Jiang",
"Wei",
""
],
[
"Zhang",
"Chuheng",
""
],
[
"Zhao",
"Li",
""
],
[
"Song",
"Lei",
""
],
[
"Bian",
"Jiang",
""
]
]
|
2306.07635 | Josep Al\`os | Josep Al\`os, Carlos Ans\'otegui, Josep M. Salvia, Eduard Torres | Exploiting Configurations of MaxSAT Solvers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In this paper, we describe how we can effectively exploit alternative
parameter configurations to a MaxSAT solver. We describe how these
configurations can be computed in the context of MaxSAT. In particular, we
experimentally show how to easily combine configurations of a non-competitive
solver to obtain a better solving approach.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 09:11:17 GMT"
}
]
| 1,686,700,800,000 | [
[
"Alòs",
"Josep",
""
],
[
"Ansótegui",
"Carlos",
""
],
[
"Salvia",
"Josep M.",
""
],
[
"Torres",
"Eduard",
""
]
]
|
2306.07638 | Damien Pellier | Nicolas Cavrel, Damien Pellier, Humbert Fiorino | On Guiding Search in HTN Temporal Planning with non Temporal Heuristics | null | ICAPS Hierarchical Planning Workshop, 2023 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The Hierarchical Task Network (HTN) formalism is used to express a wide
variety of planning problems as task decompositions, and many techniques have
been proposed to solve them. However, few works have been done on temporal HTN.
This is partly due to the lack of a formal and consensual definition of what a
temporal hierarchical planning problem is as well as the difficulty to develop
heuristics in this context. In response to these inconveniences, we propose in
this paper a new general POCL (Partial Order Causal Link) approach to represent
and solve a temporal HTN problem by using existing heuristics developed to
solve non temporal problems. We show experimentally that this approach is
performant and can outperform the existing ones.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 09:17:12 GMT"
}
]
| 1,686,700,800,000 | [
[
"Cavrel",
"Nicolas",
""
],
[
"Pellier",
"Damien",
""
],
[
"Fiorino",
"Humbert",
""
]
]
|
2306.07675 | Carlo Taticchi | Stefano Bistarelli, Maria Chiara Meo, Carlo Taticchi | An Interleaving Semantics of the Timed Concurrent Language for
Argumentation to Model Debates and Dialogue Games | Under consideration in Theory and Practice of Logic Programming
(TPLP) | null | 10.1017/S1471068423000194 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Time is a crucial factor in modelling dynamic behaviours of intelligent
agents: activities have a determined temporal duration in a real-world
environment, and previous actions influence agents' behaviour. In this paper,
we propose a language for modelling concurrent interaction between agents that
also allows the specification of temporal intervals in which particular actions
occur. Such a language exploits a timed version of Abstract Argumentation
Frameworks to realise a shared memory used by the agents to communicate and
reason on the acceptability of their beliefs with respect to a given time
interval. An interleaving model on a single processor is used for basic
computation steps, with maximum parallelism for time elapsing. Following this
approach, only one of the enabled agents is executed at each moment. To
demonstrate the capabilities of language, we also show how it can be used to
model interactions such as debates and dialogue games taking place between
intelligent agents. Lastly, we present an implementation of the language that
can be accessed via a web interface. Under consideration in Theory and Practice
of Logic Programming (TPLP).
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 10:41:28 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Jul 2023 07:37:54 GMT"
}
]
| 1,689,033,600,000 | [
[
"Bistarelli",
"Stefano",
""
],
[
"Meo",
"Maria Chiara",
""
],
[
"Taticchi",
"Carlo",
""
]
]
|
2306.07706 | Dariusz Brzezinski | Robert Susmaga, Izabela Szczech, Dariusz Brzezinski | Towards Explainable TOPSIS: Visual Insights into the Effects of Weights
and Aggregations on Rankings | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Multi-Criteria Decision Analysis (MCDA) is extensively used across diverse
industries to assess and rank alternatives. Among numerous MCDA methods
developed to solve real-world ranking problems, TOPSIS remains one of the most
popular choices in many application areas. TOPSIS calculates distances between
the considered alternatives and two predefined ones, namely the ideal and the
anti-ideal, and creates a ranking of the alternatives according to a chosen
aggregation of these distances. However, the interpretation of the inner
workings of TOPSIS is difficult, especially when the number of criteria is
large. To this end, recent research has shown that TOPSIS aggregations can be
expressed using the means (M) and standard deviations (SD) of alternatives,
creating MSD-space, a tool for visualizing and explaining aggregations. Even
though MSD-space is highly useful, it assumes equally important criteria,
making it less applicable to real-world ranking problems. In this paper, we
generalize the concept of MSD-space to weighted criteria by introducing the
concept of WMSD-space defined by what is referred to as weight-scaled means and
standard deviations. We demonstrate that TOPSIS and similar distance-based
aggregation methods can be successfully illustrated in a plane and interpreted
even when the criteria are weighted, regardless of their number. The proposed
WMSD-space offers a practical method for explaining TOPSIS rankings in
real-world decision problems.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 11:49:44 GMT"
}
]
| 1,686,700,800,000 | [
[
"Susmaga",
"Robert",
""
],
[
"Szczech",
"Izabela",
""
],
[
"Brzezinski",
"Dariusz",
""
]
]
|
2306.07719 | Jining Wang | Jining Wang, Delai Qiu, YouMing Liu, Yining Wang, Chuan Chen, Zibin
Zheng, Yuren Zhou | Contextual Dictionary Lookup for Knowledge Graph Completion | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge graph completion (KGC) aims to solve the incompleteness of
knowledge graphs (KGs) by predicting missing links from known triples, numbers
of knowledge graph embedding (KGE) models have been proposed to perform KGC by
learning embeddings. Nevertheless, most existing embedding models map each
relation into a unique vector, overlooking the specific fine-grained semantics
of them under different entities. Additionally, the few available fine-grained
semantic models rely on clustering algorithms, resulting in limited performance
and applicability due to the cumbersome two-stage training process. In this
paper, we present a novel method utilizing contextual dictionary lookup,
enabling conventional embedding models to learn fine-grained semantics of
relations in an end-to-end manner. More specifically, we represent each
relation using a dictionary that contains multiple latent semantics. The
composition of a given entity and the dictionary's central semantics serves as
the context for generating a lookup, thus determining the fine-grained
semantics of the relation adaptively. The proposed loss function optimizes both
the central and fine-grained semantics simultaneously to ensure their semantic
consistency. Besides, we introduce two metrics to assess the validity and
accuracy of the dictionary lookup operation. We extend several KGE models with
the method, resulting in substantial performance improvements on widely-used
benchmark datasets.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 12:13:41 GMT"
}
]
| 1,686,700,800,000 | [
[
"Wang",
"Jining",
""
],
[
"Qiu",
"Delai",
""
],
[
"Liu",
"YouMing",
""
],
[
"Wang",
"Yining",
""
],
[
"Chen",
"Chuan",
""
],
[
"Zheng",
"Zibin",
""
],
[
"Zhou",
"Yuren",
""
]
]
|
2306.07863 | Longtao Zheng | Longtao Zheng, Rundong Wang, Xinrun Wang, Bo An | Synapse: Trajectory-as-Exemplar Prompting with Memory for Computer
Control | ICLR 2024. Project page: https://ltzheng.github.io/Synapse | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Building agents with large language models (LLMs) for computer control is a
burgeoning research area, where the agent receives computer states and performs
actions to complete complex tasks. Previous computer agents have demonstrated
the benefits of in-context learning (ICL); however, their performance is
hindered by several issues. First, the limited context length of LLMs and
complex computer states restrict the number of exemplars, as a single webpage
can consume the entire context. Second, the exemplars in current methods, such
as high-level plans and multi-choice questions, cannot represent complete
trajectories, leading to suboptimal performance in long-horizon tasks. Third,
existing computer agents rely on task-specific exemplars and overlook the
similarity among tasks, resulting in poor generalization to novel tasks. To
address these challenges, we introduce Synapse, a computer agent featuring
three key components: i) state abstraction, which filters out task-irrelevant
information from raw states, allowing more exemplars within the limited
context, ii) trajectory-as-exemplar prompting, which prompts the LLM with
complete trajectories of the abstracted states and actions to improve
multi-step decision-making, and iii) exemplar memory, which stores the
embeddings of exemplars and retrieves them via similarity search for
generalization to novel tasks. We evaluate Synapse on MiniWoB++, a standard
task suite, and Mind2Web, a real-world website benchmark. In MiniWoB++, Synapse
achieves a 99.2% average success rate (a 10% relative improvement) across 64
tasks using demonstrations from only 48 tasks. Notably, Synapse is the first
ICL method to solve the book-flight task in MiniWoB++. Synapse also exhibits a
56% relative improvement in average step success rate over the previous
state-of-the-art prompting scheme in Mind2Web.
| [
{
"version": "v1",
"created": "Tue, 13 Jun 2023 15:49:41 GMT"
},
{
"version": "v2",
"created": "Fri, 6 Oct 2023 17:28:38 GMT"
},
{
"version": "v3",
"created": "Fri, 19 Jan 2024 06:59:26 GMT"
}
]
| 1,705,881,600,000 | [
[
"Zheng",
"Longtao",
""
],
[
"Wang",
"Rundong",
""
],
[
"Wang",
"Xinrun",
""
],
[
"An",
"Bo",
""
]
]
|
2306.08397 | Arseny Skryagin | Arseny Skryagin and Daniel Ochs and Devendra Singh Dhami and Kristian
Kersting | Scalable Neural-Probabilistic Answer Set Programming | 37 pages, 14 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The goal of combining the robustness of neural networks and the
expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic
AI. Deep Probabilistic Programming Languages (DPPLs) have been developed for
probabilistic logic programming to be carried out via the probability
estimations of deep neural networks. However, recent SOTA DPPL approaches allow
only for limited conditional probabilistic queries and do not offer the power
of true joint probability estimation. In our work, we propose an easy
integration of tractable probabilistic inference within a DPPL. To this end, we
introduce SLASH, a novel DPPL that consists of Neural-Probabilistic Predicates
(NPPs) and a logic program, united via answer set programming (ASP). NPPs are a
novel design principle allowing for combining all deep model types and
combinations thereof to be represented as a single probabilistic predicate. In
this context, we introduce a novel $+/-$ notation for answering various types
of probabilistic queries by adjusting the atom notations of a predicate. To
scale well, we show how to prune the stochastically insignificant parts of the
(ground) program, speeding up reasoning without sacrificing the predictive
performance. We evaluate SLASH on a variety of different tasks, including the
benchmark task of MNIST addition and Visual Question Answering (VQA).
| [
{
"version": "v1",
"created": "Wed, 14 Jun 2023 09:45:29 GMT"
}
]
| 1,686,873,600,000 | [
[
"Skryagin",
"Arseny",
""
],
[
"Ochs",
"Daniel",
""
],
[
"Dhami",
"Devendra Singh",
""
],
[
"Kersting",
"Kristian",
""
]
]
|
2306.08680 | Ramon Fraga Pereira | Ramon Fraga Pereira, Francesco Fuggitti, Felipe Meneguzzi, Giuseppe De
Giacomo | Temporally Extended Goal Recognition in Fully Observable
Non-Deterministic Domain Models | arXiv admin note: substantial text overlap with arXiv:2103.11692 | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Goal Recognition is the task of discerning the correct intended goal that an
agent aims to achieve, given a set of goal hypotheses, a domain model, and a
sequence of observations (i.e., a sample of the plan executed in the
environment). Existing approaches assume that goal hypotheses comprise a single
conjunctive formula over a single final state and that the environment dynamics
are deterministic, preventing the recognition of temporally extended goals in
more complex settings. In this paper, we expand goal recognition to temporally
extended goals in Fully Observable Non-Deterministic (FOND) planning domain
models, focusing on goals on finite traces expressed in Linear Temporal Logic
(LTLf) and Pure Past Linear Temporal Logic (PLTLf). We develop the first
approach capable of recognizing goals in such settings and evaluate it using
different LTLf and PLTLf goals over six FOND planning domain models. Empirical
results show that our approach is accurate in recognizing temporally extended
goals in different recognition settings.
| [
{
"version": "v1",
"created": "Wed, 14 Jun 2023 18:02:00 GMT"
}
]
| 1,686,873,600,000 | [
[
"Pereira",
"Ramon Fraga",
""
],
[
"Fuggitti",
"Francesco",
""
],
[
"Meneguzzi",
"Felipe",
""
],
[
"De Giacomo",
"Giuseppe",
""
]
]
|
2306.09042 | Anthony Hunter | Antonis Bikakis, Aissatou Diallo, Luke Dickens, Anthony Hunter, and
Rob Miller | A Graphical Formalism for Commonsense Reasoning with Recipes | 10 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Whilst cooking is a very important human activity, there has been little
consideration given to how we can formalize recipes for use in a reasoning
framework. We address this need by proposing a graphical formalization that
captures the comestibles (ingredients, intermediate food items, and final
products), and the actions on comestibles in the form of a labelled bipartite
graph. We then propose formal definitions for comparing recipes, for composing
recipes from subrecipes, and for deconstructing recipes into subrecipes. We
also introduce and compare two formal definitions for substitution into recipes
which are required when there are missing ingredients, or some actions are not
possible, or because there is a need to change the final product somehow.
| [
{
"version": "v1",
"created": "Thu, 15 Jun 2023 11:04:30 GMT"
}
]
| 1,686,873,600,000 | [
[
"Bikakis",
"Antonis",
""
],
[
"Diallo",
"Aissatou",
""
],
[
"Dickens",
"Luke",
""
],
[
"Hunter",
"Anthony",
""
],
[
"Miller",
"Rob",
""
]
]
|
2306.09082 | Federico Malato | Federico Malato, Florian Leopold, Ville Hautamaki, Andrew Melnik | Behavioral Cloning via Search in Embedded Demonstration Dataset | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Behavioural cloning uses a dataset of demonstrations to learn a behavioural
policy. To overcome various learning and policy adaptation problems, we propose
to use latent space to index a demonstration dataset, instantly access similar
relevant experiences, and copy behavior from these situations. Actions from a
selected similar situation can be performed by the agent until representations
of the agent's current situation and the selected experience diverge in the
latent space. Thus, we formulate our control problem as a search problem over a
dataset of experts' demonstrations. We test our approach on BASALT
MineRL-dataset in the latent representation of a Video PreTraining model. We
compare our model to state-of-the-art Minecraft agents. Our approach can
effectively recover meaningful demonstrations and show human-like behavior of
an agent in the Minecraft environment in a wide variety of scenarios.
Experimental results reveal that performance of our search-based approach is
comparable to trained models, while allowing zero-shot task adaptation by
changing the demonstration examples.
| [
{
"version": "v1",
"created": "Thu, 15 Jun 2023 12:25:41 GMT"
}
]
| 1,686,873,600,000 | [
[
"Malato",
"Federico",
""
],
[
"Leopold",
"Florian",
""
],
[
"Hautamaki",
"Ville",
""
],
[
"Melnik",
"Andrew",
""
]
]
|
2306.09538 | Anahita Pakiman | Anahita Pakiman, Jochen Garcke, Axel Schumacher | Graph Extraction for Assisting Crash Simulation Data Analysis | Graph-Based Representation and Reasoning: 28th International
Conference on Conceptual Structures, ICCS 2023, Berlin, Germany, September
11--13, 2023, Proceedings, book title to be confirmed | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this work, we establish a method for abstracting information from Computer
Aided Engineering (CAE) into graphs. Such graph representations of CAE data can
improve design guidelines and support recommendation systems by enabling the
comparison of simulations, highlighting unexplored experimental designs, and
correlating different designs. We focus on the load-path in crashworthiness
analysis, a complex sub-discipline in vehicle design. The load-path is the
sequence of parts that absorb most of the energy caused by the impact. To
detect the load-path, we generate a directed weighted graph from the CAE data.
The vertices represent the vehicle's parts, and the edges are an abstraction of
the connectivity of the parts. The edge direction follows the temporal
occurrence of the collision, where the edge weights reflect aspects of the
energy absorption. We introduce and assess three methods for graph extraction
and an additional method for further updating each graph with the sequences of
absorption. Based on longest-path calculations, we introduce an automated
detection of the load-path, which we analyse for the different graph extraction
methods and weights. Finally, we show how our method for the detection of
load-paths helps in the classification and labelling of CAE simulations.
| [
{
"version": "v1",
"created": "Thu, 15 Jun 2023 22:47:01 GMT"
}
]
| 1,687,132,800,000 | [
[
"Pakiman",
"Anahita",
""
],
[
"Garcke",
"Jochen",
""
],
[
"Schumacher",
"Axel",
""
]
]
|
2306.09966 | Nasim Baharisangari | Zeyuan Jin, Nasim Baharisangari, Zhe Xu, and Sze Zheng Yong | Data-Driven Model Discrimination of Switched Nonlinear Systems with
Temporal Logic Inference | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This paper addresses the problem of data-driven model discrimination for
unknown switched systems with unknown linear temporal logic (LTL)
specifications, representing tasks, that govern their mode sequences, where
only sampled data of the unknown dynamics and tasks are available. To tackle
this problem, we propose data-driven methods to over-approximate the unknown
dynamics and to infer the unknown specifications such that both set-membership
models of the unknown dynamics and LTL formulas are guaranteed to include the
ground truth model and specification/task. Moreover, we present an
optimization-based algorithm for analyzing the distinguishability of a set of
learned/inferred model-task pairs as well as a model discrimination algorithm
for ruling out model-task pairs from this set that are inconsistent with new
observations at run time. Further, we present an approach for reducing the size
of inferred specifications to increase the computational efficiency of the
model discrimination algorithms.
| [
{
"version": "v1",
"created": "Fri, 16 Jun 2023 16:50:54 GMT"
}
]
| 1,687,132,800,000 | [
[
"Jin",
"Zeyuan",
""
],
[
"Baharisangari",
"Nasim",
""
],
[
"Xu",
"Zhe",
""
],
[
"Yong",
"Sze Zheng",
""
]
]
|
2306.10290 | Jining Wang | Jining Wang, Chuan Chen, Zibin Zheng, Yuren Zhou | DsMtGCN: A Direction-sensitive Multi-task framework for Knowledge Graph
Completion | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | To solve the inherent incompleteness of knowledge graphs (KGs), numbers of
knowledge graph completion (KGC) models have been proposed to predict missing
links from known triples. Among those, several works have achieved more
advanced results via exploiting the structure information on KGs with Graph
Convolutional Networks (GCN). However, we observe that entity embeddings
aggregated from neighbors in different directions are just simply averaged to
complete single-tasks by existing GCN based models, ignoring the specific
requirements of forward and backward sub-tasks. In this paper, we propose a
Direction-sensitive Multi-task GCN (DsMtGCN) to make full use of the direction
information, the multi-head self-attention is applied to specifically combine
embeddings in different directions based on various entities and sub-tasks, the
geometric constraints are imposed to adjust the distribution of embeddings, and
the traditional binary cross-entropy loss is modified to reflect the triple
uncertainty. Moreover, the competitive experiments results on several benchmark
datasets verify the effectiveness of our model.
| [
{
"version": "v1",
"created": "Sat, 17 Jun 2023 08:21:47 GMT"
}
]
| 1,687,305,600,000 | [
[
"Wang",
"Jining",
""
],
[
"Chen",
"Chuan",
""
],
[
"Zheng",
"Zibin",
""
],
[
"Zhou",
"Yuren",
""
]
]
|
2306.10999 | Matija Franklin | Matija Franklin, Rebecca Gorman, Hal Ashton, Stuart Armstrong | Concept Extrapolation: A Conceptual Primer | Accepted at the AAMAS-23 First International Workshop on
Citizen-Centric Multiagent Systems held at the 22nd International Conference
on Autonomous Agents and Multiagent Systems, 6 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | This article is a primer on concept extrapolation - the ability to take a
concept, a feature, or a goal that is defined in one context and extrapolate it
safely to a more general context. Concept extrapolation aims to solve model
splintering - a ubiquitous occurrence wherein the features or concepts shift as
the world changes over time. Through discussing value splintering and value
extrapolation the article argues that concept extrapolation is necessary for
Artificial Intelligence alignment.
| [
{
"version": "v1",
"created": "Mon, 19 Jun 2023 15:07:16 GMT"
}
]
| 1,687,305,600,000 | [
[
"Franklin",
"Matija",
""
],
[
"Gorman",
"Rebecca",
""
],
[
"Ashton",
"Hal",
""
],
[
"Armstrong",
"Stuart",
""
]
]
|
2306.11434 | Alex Fukunaga | Yuta Takata and Alex Fukunaga | Plausibility-Based Heuristics for Latent Space Classical Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent work on LatPlan has shown that it is possible to learn models for
domain-independent classical planners from unlabeled image data. Although PDDL
models acquired by LatPlan can be solved using standard PDDL planners, the
resulting latent-space plan may be invalid with respect to the underlying,
ground-truth domain (e.g., the latent-space plan may include
hallucinatory/invalid states). We propose Plausibility-Based Heuristics, which
are domain-independent plausibility metrics which can be computed for each
state evaluated during search and uses as a heuristic function for best-first
search. We show that PBH significantly increases the number of valid found
plans on image-based tile puzzle and Towers of Hanoi domains.
| [
{
"version": "v1",
"created": "Tue, 20 Jun 2023 10:26:29 GMT"
}
]
| 1,687,305,600,000 | [
[
"Takata",
"Yuta",
""
],
[
"Fukunaga",
"Alex",
""
]
]
|
2306.13157 | Dustin Dannenhauer | Adam Amos-Binks, Dustin Dannenhauer, Leilani H. Gilpin | Anticipatory Thinking Challenges in Open Worlds: Risk Management | 4 pages, 3 figures, appeared in the non-archival AAAI 2022 Spring
Syposium on "Designing Artificial Intelligence for Open Worlds" | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Anticipatory thinking drives our ability to manage risk - identification and
mitigation - in everyday life, from bringing an umbrella when it might rain to
buying car insurance. As AI systems become part of everyday life, they too have
begun to manage risk. Autonomous vehicles log millions of miles, StarCraft and
Go agents have similar capabilities to humans, implicitly managing risks
presented by their opponents. To further increase performance in these tasks,
out-of-distribution evaluation can characterize a model's bias, what we view as
a type of risk management. However, learning to identify and mitigate
low-frequency, high-impact risks is at odds with the observational bias
required to train machine learning models. StarCraft and Go are closed-world
domains whose risks are known and mitigations well documented, ideal for
learning through repetition. Adversarial filtering datasets provide difficult
examples but are laborious to curate and static, both barriers to real-world
risk management. Adversarial robustness focuses on model poisoning under the
assumption there is an adversary with malicious intent, without considering
naturally occurring adversarial examples. These methods are all important steps
towards improving risk management but do so without considering open-worlds. We
unify these open-world risk management challenges with two contributions. The
first is our perception challenges, designed for agents with imperfect
perceptions of their environment whose consequences have a high impact. Our
second contribution are cognition challenges, designed for agents that must
dynamically adjust their risk exposure as they identify new risks and learn new
mitigations. Our goal with these challenges is to spur research into solutions
that assess and improve the anticipatory thinking required by AI agents to
manage risk in open-worlds and ultimately the real-world.
| [
{
"version": "v1",
"created": "Thu, 22 Jun 2023 18:31:17 GMT"
}
]
| 1,687,737,600,000 | [
[
"Amos-Binks",
"Adam",
""
],
[
"Dannenhauer",
"Dustin",
""
],
[
"Gilpin",
"Leilani H.",
""
]
]
|
2306.13546 | Daria de Tinguy | Daria de Tinguy, Toon Van de Maele, Tim Verbelen, Bart Dhoedt | Inferring Hierarchical Structure in Multi-Room Maze Environments | ICML 2023 Workshop | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | Cognitive maps play a crucial role in facilitating flexible behaviour by
representing spatial and conceptual relationships within an environment. The
ability to learn and infer the underlying structure of the environment is
crucial for effective exploration and navigation. This paper introduces a
hierarchical active inference model addressing the challenge of inferring
structure in the world from pixel-based observations. We propose a three-layer
hierarchical model consisting of a cognitive map, an allocentric, and an
egocentric world model, combining curiosity-driven exploration with
goal-oriented behaviour at the different levels of reasoning from context to
place to motion. This allows for efficient exploration and goal-directed search
in room-structured mini-grid environments.
| [
{
"version": "v1",
"created": "Fri, 23 Jun 2023 15:15:57 GMT"
}
]
| 1,687,737,600,000 | [
[
"de Tinguy",
"Daria",
""
],
[
"Van de Maele",
"Toon",
""
],
[
"Verbelen",
"Tim",
""
],
[
"Dhoedt",
"Bart",
""
]
]
|
2306.13572 | Paul Rosenbloom | Paul S. Rosenbloom | Thoughts on Architecture | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The term architecture has evolved considerably from its original Greek roots
and its application to buildings and computers to its more recent manifestation
for minds. This article considers lessons from this history, in terms of a set
of relevant distinctions introduced at each of these stages and a definition of
architecture that spans all three, and a reconsideration of three key issues
from cognitive architectures for architectures in general and cognitive
architectures more particularly.
| [
{
"version": "v1",
"created": "Fri, 23 Jun 2023 15:47:17 GMT"
}
]
| 1,687,737,600,000 | [
[
"Rosenbloom",
"Paul S.",
""
]
]
|
2306.13723 | Luca Pappalardo | Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo
Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina
Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis
Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John
Shawe-Taylor, Alessandro Vespignani | Human-AI Coevolution | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Human-AI coevolution, defined as a process in which humans and AI algorithms
continuously influence each other, increasingly characterises our society, but
is understudied in artificial intelligence and complexity science literature.
Recommender systems and assistants play a prominent role in human-AI
coevolution, as they permeate many facets of daily life and influence human
choices on online platforms. The interaction between users and AI results in a
potentially endless feedback loop, wherein users' choices generate data to
train AI models, which, in turn, shape subsequent user preferences. This
human-AI feedback loop has peculiar characteristics compared to traditional
human-machine interaction and gives rise to complex and often ``unintended''
social outcomes. This paper introduces Coevolution AI as the cornerstone for a
new field of study at the intersection between AI and complexity science
focused on the theoretical, empirical, and mathematical investigation of the
human-AI feedback loop. In doing so, we: (i) outline the pros and cons of
existing methodologies and highlight shortcomings and potential ways for
capturing feedback loop mechanisms; (ii) propose a reflection at the
intersection between complexity science, AI and society; (iii) provide
real-world examples for different human-AI ecosystems; and (iv) illustrate
challenges to the creation of such a field of study, conceptualising them at
increasing levels of abstraction, i.e., technical, epistemological, legal and
socio-political.
| [
{
"version": "v1",
"created": "Fri, 23 Jun 2023 18:10:54 GMT"
},
{
"version": "v2",
"created": "Fri, 3 May 2024 13:38:55 GMT"
}
]
| 1,714,953,600,000 | [
[
"Pedreschi",
"Dino",
""
],
[
"Pappalardo",
"Luca",
""
],
[
"Ferragina",
"Emanuele",
""
],
[
"Baeza-Yates",
"Ricardo",
""
],
[
"Barabasi",
"Albert-Laszlo",
""
],
[
"Dignum",
"Frank",
""
],
[
"Dignum",
"Virginia",
""
],
[
"Eliassi-Rad",
"Tina",
""
],
[
"Giannotti",
"Fosca",
""
],
[
"Kertesz",
"Janos",
""
],
[
"Knott",
"Alistair",
""
],
[
"Ioannidis",
"Yannis",
""
],
[
"Lukowicz",
"Paul",
""
],
[
"Passarella",
"Andrea",
""
],
[
"Pentland",
"Alex Sandy",
""
],
[
"Shawe-Taylor",
"John",
""
],
[
"Vespignani",
"Alessandro",
""
]
]
|
2306.13760 | Michael Lingelbach | Michael Lingelbach, Chengshu Li, Minjune Hwang, Andrey Kurenkov, Alan
Lou, Roberto Mart\'in-Mart\'in, Ruohan Zhang, Li Fei-Fei, Jiajun Wu | Task-Driven Graph Attention for Hierarchical Relational Object
Navigation | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Embodied AI agents in large scenes often need to navigate to find objects. In
this work, we study a naturally emerging variant of the object navigation task,
hierarchical relational object navigation (HRON), where the goal is to find
objects specified by logical predicates organized in a hierarchical structure -
objects related to furniture and then to rooms - such as finding an apple on
top of a table in the kitchen. Solving such a task requires an efficient
representation to reason about object relations and correlate the relations in
the environment and in the task goal. HRON in large scenes (e.g. homes) is
particularly challenging due to its partial observability and long horizon,
which invites solutions that can compactly store the past information while
effectively exploring the scene. We demonstrate experimentally that scene
graphs are the best-suited representation compared to conventional
representations such as images or 2D maps. We propose a solution that uses
scene graphs as part of its input and integrates graph neural networks as its
backbone, with an integrated task-driven attention mechanism, and demonstrate
its better scalability and learning efficiency than state-of-the-art baselines.
| [
{
"version": "v1",
"created": "Fri, 23 Jun 2023 19:50:48 GMT"
}
]
| 1,687,824,000,000 | [
[
"Lingelbach",
"Michael",
""
],
[
"Li",
"Chengshu",
""
],
[
"Hwang",
"Minjune",
""
],
[
"Kurenkov",
"Andrey",
""
],
[
"Lou",
"Alan",
""
],
[
"Martín-Martín",
"Roberto",
""
],
[
"Zhang",
"Ruohan",
""
],
[
"Fei-Fei",
"Li",
""
],
[
"Wu",
"Jiajun",
""
]
]
|
2306.13885 | Sofie Goethals | Sofie Goethals and David Martens and Theodoros Evgeniou | Manipulation Risks in Explainable AI: The Implications of the
Disagreement Problem | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Artificial Intelligence (AI) systems are increasingly used in high-stakes
domains of our life, increasing the need to explain these decisions and to make
sure that they are aligned with how we want the decision to be made. The field
of Explainable AI (XAI) has emerged in response. However, it faces a
significant challenge known as the disagreement problem, where multiple
explanations are possible for the same AI decision or prediction. While the
existence of the disagreement problem is acknowledged, the potential
implications associated with this problem have not yet been widely studied.
First, we provide an overview of the different strategies explanation providers
could deploy to adapt the returned explanation to their benefit. We make a
distinction between strategies that attack the machine learning model or
underlying data to influence the explanations, and strategies that leverage the
explanation phase directly. Next, we analyse several objectives and concrete
scenarios the providers could have to engage in this behavior, and the
potential dangerous consequences this manipulative behavior could have on
society. We emphasize that it is crucial to investigate this issue now, before
these methods are widely implemented, and propose some mitigation strategies.
| [
{
"version": "v1",
"created": "Sat, 24 Jun 2023 07:21:28 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Jun 2023 08:42:34 GMT"
}
]
| 1,687,910,400,000 | [
[
"Goethals",
"Sofie",
""
],
[
"Martens",
"David",
""
],
[
"Evgeniou",
"Theodoros",
""
]
]
|
2306.13935 | Abhishek Ghose | Emma Thuong Nguyen, Abhishek Ghose | Are Good Explainers Secretly Human-in-the-Loop Active Learners? | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Explainable AI (XAI) techniques have become popular for multiple use-cases in
the past few years. Here we consider its use in studying model predictions to
gather additional training data. We argue that this is equivalent to Active
Learning, where the query strategy involves a human-in-the-loop. We provide a
mathematical approximation for the role of the human, and present a general
formalization of the end-to-end workflow. This enables us to rigorously compare
this use with standard Active Learning algorithms, while allowing for
extensions to the workflow. An added benefit is that their utility can be
assessed via simulation instead of conducting expensive user-studies. We also
present some initial promising results.
| [
{
"version": "v1",
"created": "Sat, 24 Jun 2023 10:50:42 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Jul 2023 14:03:55 GMT"
},
{
"version": "v3",
"created": "Tue, 16 Apr 2024 16:33:07 GMT"
}
]
| 1,713,312,000,000 | [
[
"Nguyen",
"Emma Thuong",
""
],
[
"Ghose",
"Abhishek",
""
]
]
|
2306.13956 | Noel Brindise | Noel Brindise and Cedric Langbort | Pointwise-in-Time Explanation for Linear Temporal Logic Rules | See related publication in Conference on Decision and Control (CDC)
2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The new field of Explainable Planning (XAIP) has produced a variety of
approaches to explain and describe the behavior of autonomous agents to human
observers. Many summarize agent behavior in terms of the constraints, or
''rules,'' which the agent adheres to during its trajectories. In this work, we
narrow the focus from summary to specific moments in individual trajectories,
offering a ''pointwise-in-time'' view. Our novel framework, which we define on
Linear Temporal Logic (LTL) rules, assigns an intuitive status to any rule in
order to describe the trajectory progress at individual time steps; here, a
rule is classified as active, satisfied, inactive, or violated. Given a
trajectory, a user may query for status of specific LTL rules at individual
trajectory time steps. In this paper, we present this novel framework, named
Rule Status Assessment (RSA), and provide an example of its implementation. We
find that pointwise-in-time status assessment is useful as a post-hoc
diagnostic, enabling a user to systematically track the agent's behavior with
respect to a set of rules.
| [
{
"version": "v1",
"created": "Sat, 24 Jun 2023 13:07:08 GMT"
},
{
"version": "v2",
"created": "Sun, 1 Oct 2023 16:35:12 GMT"
}
]
| 1,696,291,200,000 | [
[
"Brindise",
"Noel",
""
],
[
"Langbort",
"Cedric",
""
]
]
|
2306.14256 | Marcelo Jos\'e Sc.D. | Marcelo Archanjo Jose and Fabio Gagliardi Cozman | A Multilingual Translator to SQL with Database Schema Pruning to Improve
Self-Attention | This preprint has not undergone peer review or any post-submission
improvements or corrections. The Version of Record of this article is
published in International Journal of Information Technology, and is
available online at https://doi.org/10.1007/s41870-023-01342-3 . SharedIt
link: https://rdcu.be/dff19 | null | 10.1007/s41870-023-01342-3 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Long sequences of text are challenging in the context of transformers, due to
quadratic memory increase in the self-attention mechanism. As this issue
directly affects the translation from natural language to SQL queries (as
techniques usually take as input a concatenated text with the question and the
database schema), we present techniques that allow long text sequences to be
handled by transformers with up to 512 input tokens. We propose a training
process with database schema pruning (removal of tables and columns names that
are useless for the query of interest). In addition, we used a multilingual
approach with the mT5-large model fine-tuned with a data-augmented Spider
dataset in four languages simultaneously: English, Portuguese, Spanish, and
French. Our proposed technique used the Spider dataset and increased the exact
set match accuracy results from 0.718 to 0.736 in a validation dataset (Dev).
Source code, evaluations, and checkpoints are available at:
\underline{https://github.com/C4AI/gap-text2sql}.
| [
{
"version": "v1",
"created": "Sun, 25 Jun 2023 14:28:12 GMT"
}
]
| 1,687,824,000,000 | [
[
"Jose",
"Marcelo Archanjo",
""
],
[
"Cozman",
"Fabio Gagliardi",
""
]
]
|
2306.14356 | Md. Russell Talukder | Md. Russell Talukder | Smart Transformation of EFL Teaching and Learning Approaches | 59 pages , 7 figures, 30 tables, multidisciplinary research article | null | 10.33166/AETiC.2023.03.002 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The calibration of the EFL teaching and learning approaches with Artificial
Intelligence can potentially facilitate a smart transformation, fostering a
personalized and engaging experience in teaching and learning among the
stakeholders. The paper focuses on developing an EFL Big Data Ecosystem that is
based on Big Data, Analytics, Machine Learning and cluster domain of EFL
teaching and learning contents. Accordingly, the paper uses two membranes to
construe its framework, namely (i) Open Big Data Membrane that stores random
data collected from various source domains and (ii) Machine Learning Membrane
that stores specially prepared structured and semi-structured data.
Theoretically, the structured and semi structured data are to be prepared
skill-wise, attribute-wise, method-wise, and preference-wise to accommodate the
personalized preferences and diverse teaching and learning needs of different
individuals. The ultimate goal is to optimize the learning experience by
leveraging machine learning to create tailored content that aligns with the
diverse teaching and learning needs of the EFL communities.
| [
{
"version": "v1",
"created": "Sun, 25 Jun 2023 22:16:59 GMT"
}
]
| 1,687,824,000,000 | [
[
"Talukder",
"Md. Russell",
""
]
]
|
2306.14421 | Siqi Lai | Siqi Lai (1), Weijia Zhang (1), Hao Liu (1, 2) ((1) The Hong Kong
University of Science and Technology (Guangzhou), (2) The Hong Kong
University of Science and Technology) | A Preference-aware Meta-optimization Framework for Personalized Vehicle
Energy Consumption Estimation | null | null | 10.1145/3580305.3599767 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Vehicle Energy Consumption (VEC) estimation aims to predict the total energy
required for a given trip before it starts, which is of great importance to
trip planning and transportation sustainability. Existing approaches mainly
focus on extracting statistically significant factors from typical trips to
improve the VEC estimation. However, the energy consumption of each vehicle may
diverge widely due to the personalized driving behavior under varying travel
contexts. To this end, this paper proposes a preference-aware meta-optimization
framework Meta-Pec for personalized vehicle energy consumption estimation.
Specifically, we first propose a spatiotemporal behavior learning module to
capture the latent driver preference hidden in historical trips. Moreover,
based on the memorization of driver preference, we devise a selection-based
driving behavior prediction module to infer driver-specific driving patterns on
a given route, which provides additional basis and supervision signals for VEC
estimation. Besides, a driver-specific meta-optimization scheme is proposed to
enable fast model adaption by learning and sharing transferable knowledge
globally. Extensive experiments on two real-world datasets show the superiority
of our proposed framework against ten numerical and data-driven machine
learning baselines. The source code is available at
https://github.com/usail-hkust/Meta-Pec.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2023 05:03:24 GMT"
}
]
| 1,687,824,000,000 | [
[
"Lai",
"Siqi",
""
],
[
"Zhang",
"Weijia",
""
],
[
"Liu",
"Hao",
""
]
]
|
2306.14546 | Samy Badreddine | Samy Badreddine, Luciano Serafini, Michael Spranger | logLTN: Differentiable Fuzzy Logic in the Logarithm Space | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The AI community is increasingly focused on merging logic with deep learning
to create Neuro-Symbolic (NeSy) paradigms and assist neural approaches with
symbolic knowledge. A significant trend in the literature involves integrating
axioms and facts in loss functions by grounding logical symbols with neural
networks and operators with fuzzy semantics. Logic Tensor Networks (LTN) is one
of the main representatives in this category, known for its simplicity,
efficiency, and versatility. However, it has been previously shown that not all
fuzzy operators perform equally when applied in a differentiable setting.
Researchers have proposed several configurations of operators, trading off
between effectiveness, numerical stability, and generalization to different
formulas. This paper presents a configuration of fuzzy operators for grounding
formulas end-to-end in the logarithm space. Our goal is to develop a
configuration that is more effective than previous proposals, able to handle
any formula, and numerically stable. To achieve this, we propose semantics that
are best suited for the logarithm space and introduce novel simplifications and
improvements that are crucial for optimization via gradient-descent. We use LTN
as the framework for our experiments, but the conclusions of our work apply to
any similar NeSy framework. Our findings, both formal and empirical, show that
the proposed configuration outperforms the state-of-the-art and that each of
our modifications is essential in achieving these results.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2023 09:39:05 GMT"
}
]
| 1,687,824,000,000 | [
[
"Badreddine",
"Samy",
""
],
[
"Serafini",
"Luciano",
""
],
[
"Spranger",
"Michael",
""
]
]
|
2306.14722 | LingXi Zhang | Lingxi Zhang, Jing Zhang, Yanling Wang, Shulin Cao, Xinmei Huang,
Cuiping Li, Hong Chen, Juanzi Li | FC-KBQA: A Fine-to-Coarse Composition Framework for Knowledge Base
Question Answering | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The generalization problem on KBQA has drawn considerable attention. Existing
research suffers from the generalization issue brought by the entanglement in
the coarse-grained modeling of the logical expression, or inexecutability
issues due to the fine-grained modeling of disconnected classes and relations
in real KBs. We propose a Fine-to-Coarse Composition framework for KBQA
(FC-KBQA) to both ensure the generalization ability and executability of the
logical expression. The main idea of FC-KBQA is to extract relevant
fine-grained knowledge components from KB and reformulate them into
middle-grained knowledge pairs for generating the final logical expressions.
FC-KBQA derives new state-of-the-art performance on GrailQA and WebQSP, and
runs 4 times faster than the baseline.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2023 14:19:46 GMT"
}
]
| 1,687,824,000,000 | [
[
"Zhang",
"Lingxi",
""
],
[
"Zhang",
"Jing",
""
],
[
"Wang",
"Yanling",
""
],
[
"Cao",
"Shulin",
""
],
[
"Huang",
"Xinmei",
""
],
[
"Li",
"Cuiping",
""
],
[
"Chen",
"Hong",
""
],
[
"Li",
"Juanzi",
""
]
]
|
2306.14816 | Isma\"il Sahbane | Ismail Sahbane, Francis Rhys Ward, C Henrik {\AA}slund | Experiments with Detecting and Mitigating AI Deception | 4 pages, 2 figures, 3 algorithms, 1 table | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | How to detect and mitigate deceptive AI systems is an open problem for the
field of safe and trustworthy AI. We analyse two algorithms for mitigating
deception: The first is based on the path-specific objectives framework where
paths in the game that incentivise deception are removed. The second is based
on shielding, i.e., monitoring for unsafe policies and replacing them with a
safe reference policy. We construct two simple games and evaluate our
algorithms empirically. We find that both methods ensure that our agent is not
deceptive, however, shielding tends to achieve higher reward.
| [
{
"version": "v1",
"created": "Mon, 26 Jun 2023 16:22:13 GMT"
}
]
| 1,687,824,000,000 | [
[
"Sahbane",
"Ismail",
""
],
[
"Ward",
"Francis Rhys",
""
],
[
"Åslund",
"C Henrik",
""
]
]
|
2306.15266 | Hanrong Zhang | Xingyue Wang, Hanrong Zhang, Ke Ma, Shuting Tao, Peng Peng, Hongwei
Wang | Internal Contrastive Learning for Generalized Out-of-distribution Fault
Diagnosis (GOOFD) Framework | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Fault diagnosis is essential in industrial processes for monitoring the
conditions of important machines. With the ever-increasing complexity of
working conditions and demand for safety during production and operation,
different diagnosis methods are required, and more importantly, an integrated
fault diagnosis system that can cope with multiple tasks is highly desired.
However, the diagnosis subtasks are often studied separately, and the currently
available methods still need improvement for such a generalized system. To
address this issue, we propose the Generalized Out-of-distribution Fault
Diagnosis (GOOFD) framework to integrate diagnosis subtasks, such as fault
detection, fault classification, and novel fault diagnosis. Additionally, a
unified fault diagnosis method based on internal contrastive learning is put
forward to underpin the proposed generalized framework. The method extracts
features utilizing the internal contrastive learning technique and then
recognizes the outliers based on the Mahalanobis distance. Experiments are
conducted on a simulated benchmark dataset as well as two practical process
datasets to evaluate the proposed framework. As demonstrated in the
experiments, the proposed method achieves better performance compared with
several existing techniques and thus verifies the effectiveness of the proposed
framework.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 07:50:25 GMT"
}
]
| 1,687,910,400,000 | [
[
"Wang",
"Xingyue",
""
],
[
"Zhang",
"Hanrong",
""
],
[
"Ma",
"Ke",
""
],
[
"Tao",
"Shuting",
""
],
[
"Peng",
"Peng",
""
],
[
"Wang",
"Hongwei",
""
]
]
|
2306.15362 | Nils Wilken | Nils Wilken and Lea Cohausz and Christian Bartelt and Heiner
Stuckenschmidt | Planning Landmark Based Goal Recognition Revisited: Does Using Initial
State Landmarks Make Sense? | Full publication: Wilken, N., Cohausz, L., Bartelt, C.,
Stuckenschmidt, H. (2023). Planning Landmark Based Goal Recognition
Revisited: Does Using Initial State Landmarks Make Sense?. In: Seipel, D.,
Steen, A. (eds) KI 2023: Advances in Artificial Intelligence. KI 2023.
Lecture Notes in Computer Science(), vol 14236. Springer, Cham. arXiv admin
note: text overlap with arXiv:2301.10571 | null | 10.1007/978-3-031-42608-7_19 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Goal recognition is an important problem in many application domains (e.g.,
pervasive computing, intrusion detection, computer games, etc.). In many
application scenarios, it is important that goal recognition algorithms can
recognize goals of an observed agent as fast as possible. However, many early
approaches in the area of Plan Recognition As Planning, require quite large
amounts of computation time to calculate a solution. Mainly to address this
issue, recently, Pereira et al. developed an approach that is based on planning
landmarks and is much more computationally efficient than previous approaches.
However, the approach, as proposed by Pereira et al., also uses trivial
landmarks (i.e., facts that are part of the initial state and goal description
are landmarks by definition). In this paper, we show that it does not provide
any benefit to use landmarks that are part of the initial state in a planning
landmark based goal recognition approach. The empirical results show that
omitting initial state landmarks for goal recognition improves goal recognition
performance.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 10:20:28 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Nov 2023 09:44:04 GMT"
}
]
| 1,699,833,600,000 | [
[
"Wilken",
"Nils",
""
],
[
"Cohausz",
"Lea",
""
],
[
"Bartelt",
"Christian",
""
],
[
"Stuckenschmidt",
"Heiner",
""
]
]
|
2306.15365 | Andreia Mart | Andreia Martins, Eva Maia, Isabel Pra\c{c}a | Herb-Drug Interactions: A Holistic Decision Support System in Healthcare | null | 2022 IEEE International Conference on E-health Networking,
Application & Services (HealthCom) | 10.1109/HealthCom54947.2022.9982729 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Complementary and alternative medicine are commonly used concomitantly with
conventional medications leading to adverse drug reactions and even fatality in
some cases. Furthermore, the vast possibility of herb-drug interactions
prevents health professionals from remembering or manually searching them in a
database. Decision support systems are a powerful tool that can be used to
assist clinicians in making diagnostic and therapeutic decisions in patient
care. Therefore, an original and hybrid decision support system was designed to
identify herb-drug interactions, applying artificial intelligence techniques to
identify new possible interactions. Different machine learning models will be
used to strengthen the typical rules engine used in these cases. Thus, using
the proposed system, the pharmacy community, people's first line of contact
within the Healthcare System, will be able to make better and more accurate
therapeutic decisions and mitigate possible adverse events.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 10:30:51 GMT"
}
]
| 1,687,910,400,000 | [
[
"Martins",
"Andreia",
""
],
[
"Maia",
"Eva",
""
],
[
"Praça",
"Isabel",
""
]
]
|
2306.15489 | SheoYon Jhin | Sheo Yon Jhin, Jaehoon Lee, Noseong Park | Precursor-of-Anomaly Detection for Irregular Time Series | KDD 2023 accepted paper | null | 10.1145/3580305.3599469 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Anomaly detection is an important field that aims to identify unexpected
patterns or data points, and it is closely related to many real-world problems,
particularly to applications in finance, manufacturing, cyber security, and so
on. While anomaly detection has been studied extensively in various fields,
detecting future anomalies before they occur remains an unexplored territory.
In this paper, we present a novel type of anomaly detection, called
Precursor-of-Anomaly (PoA) detection. Unlike conventional anomaly detection,
which focuses on determining whether a given time series observation is an
anomaly or not, PoA detection aims to detect future anomalies before they
happen. To solve both problems at the same time, we present a neural controlled
differential equation-based neural network and its multi-task learning
algorithm. We conduct experiments using 17 baselines and 3 datasets, including
regular and irregular time series, and demonstrate that our presented method
outperforms the baselines in almost all cases. Our ablation studies also
indicate that the multitasking training method significantly enhances the
overall performance for both anomaly and PoA detection.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 14:10:09 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Jun 2023 02:38:11 GMT"
},
{
"version": "v3",
"created": "Fri, 13 Oct 2023 06:36:20 GMT"
}
]
| 1,697,414,400,000 | [
[
"Jhin",
"Sheo Yon",
""
],
[
"Lee",
"Jaehoon",
""
],
[
"Park",
"Noseong",
""
]
]
|
2306.15664 | Wei-Yao Wang | Wei-Yao Wang, Wei-Wei Du, Wen-Chih Peng, Tsi-Ui Ik | Benchmarking Stroke Forecasting with Stroke-Level Badminton Dataset | IJCAI-24 Demo, IT4PSS @ IJCAI-23, and CoachAI Badminton Challenge
Track 2 @ IJCAI-23. Challenge website:
https://sites.google.com/view/coachai-challenge-2023/ | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In recent years, badminton analytics has drawn attention due to the
advancement of artificial intelligence and the efficiency of data collection.
While there is a line of effective applications to improve and investigate
player performance, there are only a few public badminton datasets that can be
used by researchers outside the badminton domain. Existing badminton singles
datasets focus on specific matchups; however, they cannot provide comprehensive
studies on different players and various matchups. In this paper, we provide a
badminton singles dataset, ShuttleSet22, which is collected from high-ranking
matches in 2022. ShuttleSet22 consists of 30,172 strokes in 2,888 rallies in
the training set, 1,400 strokes in 450 rallies in the validation set, and 2,040
strokes in 654 rallies in the testing set, with detailed stroke-level metadata
within a rally. To benchmark existing work with ShuttleSet22, we hold a
challenge, Track 2: Forecasting Future Turn-Based Strokes in Badminton Rallies,
at CoachAI Badminton Challenge @ IJCAI 2023, to encourage researchers to tackle
this real-world problem through innovative approaches and to summarize insights
between the state-of-the-art baseline and improved techniques, exchanging
inspiring ideas. The baseline codes and the dataset are made available at
https://github.com/wywyWang/CoachAI-Projects/tree/main/CoachAI-Challenge-IJCAI2023.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 17:57:34 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Jun 2023 20:50:24 GMT"
},
{
"version": "v3",
"created": "Mon, 22 Apr 2024 03:43:10 GMT"
}
]
| 1,713,830,400,000 | [
[
"Wang",
"Wei-Yao",
""
],
[
"Du",
"Wei-Wei",
""
],
[
"Peng",
"Wen-Chih",
""
],
[
"Ik",
"Tsi-Ui",
""
]
]
|
2306.15796 | Yakun Yu | Yakun Yu, Mingjun Zhao, Shi-ang Qi, Feiran Sun, Baoxun Wang, Weidong
Guo, Xiaoli Wang, Lei Yang, Di Niu | ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis | Accepted by ACL Findings 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Multimodal Sentiment Analysis leverages multimodal signals to detect the
sentiment of a speaker. Previous approaches concentrate on performing
multimodal fusion and representation learning based on general knowledge
obtained from pretrained models, which neglects the effect of domain-specific
knowledge. In this paper, we propose Contrastive Knowledge Injection (ConKI)
for multimodal sentiment analysis, where specific-knowledge representations for
each modality can be learned together with general knowledge representations
via knowledge injection based on an adapter architecture. In addition, ConKI
uses a hierarchical contrastive learning procedure performed between knowledge
types within every single modality, across modalities within each sample, and
across samples to facilitate the effective learning of the proposed
representations, hence improving multimodal sentiment predictions. The
experiments on three popular multimodal sentiment analysis benchmarks show that
ConKI outperforms all prior methods on a variety of performance metrics.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 20:51:03 GMT"
}
]
| 1,687,996,800,000 | [
[
"Yu",
"Yakun",
""
],
[
"Zhao",
"Mingjun",
""
],
[
"Qi",
"Shi-ang",
""
],
[
"Sun",
"Feiran",
""
],
[
"Wang",
"Baoxun",
""
],
[
"Guo",
"Weidong",
""
],
[
"Wang",
"Xiaoli",
""
],
[
"Yang",
"Lei",
""
],
[
"Niu",
"Di",
""
]
]
|
2306.15803 | Jordi Planes | Ram\'on B\'ejar and Ant\'onio Morgado and Jordi Planes and Joao
Marques-Silva | On Logic-Based Explainability with Partially Specified Inputs | 14 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In the practical deployment of machine learning (ML) models, missing data
represents a recurring challenge. Missing data is often addressed when training
ML models. But missing data also needs to be addressed when deciding
predictions and when explaining those predictions. Missing data represents an
opportunity to partially specify the inputs of the prediction to be explained.
This paper studies the computation of logic-based explanations in the presence
of partially specified inputs. The paper shows that most of the algorithms
proposed in recent years for computing logic-based explanations can be
generalized for computing explanations given the partially specified inputs.
One related result is that the complexity of computing logic-based explanations
remains unchanged. A similar result is proved in the case of logic-based
explainability subject to input constraints. Furthermore, the proposed solution
for computing explanations given partially specified inputs is applied to
classifiers obtained from well-known public datasets, thereby illustrating a
number of novel explainability use cases.
| [
{
"version": "v1",
"created": "Tue, 27 Jun 2023 21:09:25 GMT"
}
]
| 1,687,996,800,000 | [
[
"Béjar",
"Ramón",
""
],
[
"Morgado",
"António",
""
],
[
"Planes",
"Jordi",
""
],
[
"Marques-Silva",
"Joao",
""
]
]
|
2306.15887 | Mohammad Mofrad | Salmonn Talebi, Elizabeth Tong and Mohammad R. K. Mofrad | Beyond the Hype: Assessing the Performance, Trustworthiness, and
Clinical Suitability of GPT3.5 | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The use of large language models (LLMs) in healthcare is gaining popularity,
but their practicality and safety in clinical settings have not been thoroughly
assessed. In high-stakes environments like medical settings, trust and safety
are critical issues for LLMs. To address these concerns, we present an approach
to evaluate the performance and trustworthiness of a GPT3.5 model for medical
image protocol assignment. We compare it with a fine-tuned BERT model and a
radiologist. In addition, we have a radiologist review the GPT3.5 output to
evaluate its decision-making process. Our evaluation dataset consists of 4,700
physician entries across 11 imaging protocol classes spanning the entire head.
Our findings suggest that the GPT3.5 performance falls behind BERT and a
radiologist. However, GPT3.5 outperforms BERT in its ability to explain its
decision, detect relevant word indicators, and model calibration. Furthermore,
by analyzing the explanations of GPT3.5 for misclassifications, we reveal
systematic errors that need to be resolved to enhance its safety and
suitability for clinical use.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2023 03:03:51 GMT"
}
]
| 1,687,996,800,000 | [
[
"Talebi",
"Salmonn",
""
],
[
"Tong",
"Elizabeth",
""
],
[
"Mofrad",
"Mohammad R. K.",
""
]
]
|
2306.15903 | Chenglu Sun | Chenglu Sun, Shuo Shen, Sijia Xu, Weidong Zhang | Diversity is Strength: Mastering Football Full Game with Interactive
Reinforcement Learning of Multiple AIs | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Training AI with strong and rich strategies in multi-agent environments
remains an important research topic in Deep Reinforcement Learning (DRL). The
AI's strength is closely related to its diversity of strategies, and this
relationship can guide us to train AI with both strong and rich strategies. To
prove this point, we propose Diversity is Strength (DIS), a novel DRL training
framework that can simultaneously train multiple kinds of AIs. These AIs are
linked through an interconnected history model pool structure, which enhances
their capabilities and strategy diversities. We also design a model evaluation
and screening scheme to select the best models to enrich the model pool and
obtain the final AI. The proposed training method provides diverse,
generalizable, and strong AI strategies without using human data. We tested our
method in an AI competition based on Google Research Football (GRF) and won the
5v5 and 11v11 tracks. The method enables a GRF AI to have a high level on both
5v5 and 11v11 tracks for the first time, which are under complex multi-agent
environments. The behavior analysis shows that the trained AI has rich
strategies, and the ablation experiments proved that the designed modules
benefit the training process.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2023 03:56:57 GMT"
}
]
| 1,687,996,800,000 | [
[
"Sun",
"Chenglu",
""
],
[
"Shen",
"Shuo",
""
],
[
"Xu",
"Sijia",
""
],
[
"Zhang",
"Weidong",
""
]
]
|
2306.16088 | Max Boettinger | Max Boettinger, David Klotz | Mastering Nordschleife -- A comprehensive race simulation for AI
strategy decision-making in motorsports | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | In the realm of circuit motorsports, race strategy plays a pivotal role in
determining race outcomes. This strategy focuses on the timing of pit stops,
which are necessary due to fuel consumption and tire performance degradation.
The objective of race strategy is to balance the advantages of pit stops, such
as tire replacement and refueling, with the time loss incurred in the pit lane.
Current race simulations, used to estimate the best possible race strategy,
vary in granularity, modeling of probabilistic events, and require manual input
for in-laps. This paper addresses these limitations by developing a novel
simulation model tailored to GT racing and leveraging artificial intelligence
to automate strategic decisions. By integrating the simulation with OpenAI's
Gym framework, a reinforcement learning environment is created and an agent is
trained. The study evaluates various hyperparameter configurations, observation
spaces, and reward functions, drawing upon historical timing data from the 2020
N\"urburgring Langstrecken Serie for empirical parameter validation. The
results demonstrate the potential of reinforcement learning for improving race
strategy decision-making, as the trained agent makes sensible decisions
regarding pit stop timing and refueling amounts. Key parameters, such as
learning rate, decay rate and the number of episodes, are identified as crucial
factors, while the combination of fuel mass and current race position proves
most effective for policy development. The paper contributes to the broader
application of reinforcement learning in race simulations and unlocks the
potential for race strategy optimization beyond FIA Formula~1, specifically in
the GT racing domain.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2023 10:39:31 GMT"
}
]
| 1,687,996,800,000 | [
[
"Boettinger",
"Max",
""
],
[
"Klotz",
"David",
""
]
]
|
2306.16205 | David Radke | David Radke, Kate Larson, Tim Brecht and Kyle Tilbury | Towards a Better Understanding of Learning with Multiagent Teams | 15 pages, 11 figures, published at the International Joint Conference
on Artificial Intelligence (IJCAI) in 2023 | null | null | null | cs.AI | http://creativecommons.org/publicdomain/zero/1.0/ | While it has long been recognized that a team of individual learning agents
can be greater than the sum of its parts, recent work has shown that larger
teams are not necessarily more effective than smaller ones. In this paper, we
study why and under which conditions certain team structures promote effective
learning for a population of individual learning agents. We show that,
depending on the environment, some team structures help agents learn to
specialize into specific roles, resulting in more favorable global results.
However, large teams create credit assignment challenges that reduce
coordination, leading to large teams performing poorly compared to smaller
ones. We support our conclusions with both theoretical analysis and empirical
results.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2023 13:37:48 GMT"
}
]
| 1,687,996,800,000 | [
[
"Radke",
"David",
""
],
[
"Larson",
"Kate",
""
],
[
"Brecht",
"Tim",
""
],
[
"Tilbury",
"Kyle",
""
]
]
|
2306.16368 | Renju Rajan | Renju Rajan | Lagrangian based A* algorithm for automated reasoning | 8 pages, 3 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, a modification of A* algorithm is considered for the shortest
path problem. A weightage is introduced in the heuristic part of the A*
algorithm to improve its efficiency. An application of the algorithm is
considered for UAV path planning wherein velocity is taken as the weigtage to
the heuristic. At the outset, calculus of variations based Lagrange's equation
was used to identify velocity as the decisive factor for the dynamical system.
This approach would be useful for other problems as well to improve the
efficiency of algorithms in those areas.
| [
{
"version": "v1",
"created": "Wed, 28 Jun 2023 17:01:03 GMT"
}
]
| 1,687,996,800,000 | [
[
"Rajan",
"Renju",
""
]
]
|
2306.16902 | Lyuzhou Chen | Taiyu Ban, Lyvzhou Chen, Xiangyu Wang, Huanhuan Chen | From Query Tools to Causal Architects: Harnessing Large Language Models
for Advanced Causal Discovery from Data | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Large Language Models (LLMs) exhibit exceptional abilities for causal
analysis between concepts in numerous societally impactful domains, including
medicine, science, and law. Recent research on LLM performance in various
causal discovery and inference tasks has given rise to a new ladder in the
classical three-stage framework of causality. In this paper, we advance the
current research of LLM-driven causal discovery by proposing a novel framework
that combines knowledge-based LLM causal analysis with data-driven causal
structure learning. To make LLM more than a query tool and to leverage its
power in discovering natural and new laws of causality, we integrate the
valuable LLM expertise on existing causal mechanisms into statistical analysis
of objective data to build a novel and practical baseline for causal structure
learning.
We introduce a universal set of prompts designed to extract causal graphs
from given variables and assess the influence of LLM prior causality on
recovering causal structures from data. We demonstrate the significant
enhancement of LLM expertise on the quality of recovered causal structures from
data, while also identifying critical challenges and issues, along with
potential approaches to address them. As a pioneering study, this paper aims to
emphasize the new frontier that LLMs are opening for classical causal discovery
and inference, and to encourage the widespread adoption of LLM capabilities in
data-driven causal analysis.
| [
{
"version": "v1",
"created": "Thu, 29 Jun 2023 12:48:00 GMT"
}
]
| 1,688,083,200,000 | [
[
"Ban",
"Taiyu",
""
],
[
"Chen",
"Lyvzhou",
""
],
[
"Wang",
"Xiangyu",
""
],
[
"Chen",
"Huanhuan",
""
]
]
|
2306.16914 | Ananya Joshi | Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld, Bryan Wilder | Computationally Assisted Quality Control for Public Health Data Streams | https://github.com/cmu-delphi/covidcast-indicators/tree/main/_delphi_utils_python/delphi_utils/flash_eval | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Irregularities in public health data streams (like COVID-19 Cases) hamper
data-driven decision-making for public health stakeholders. A real-time,
computer-generated list of the most important, outlying data points from
thousands of daily-updated public health data streams could assist an expert
reviewer in identifying these irregularities. However, existing outlier
detection frameworks perform poorly on this task because they do not account
for the data volume or for the statistical properties of public health streams.
Accordingly, we developed FlaSH (Flagging Streams in public Health), a
practical outlier detection framework for public health data users that uses
simple, scalable models to capture these statistical properties explicitly. In
an experiment where human experts evaluate FlaSH and existing methods
(including deep learning approaches), FlaSH scales to the data volume of this
task, matches or exceeds these other methods in mean accuracy, and identifies
the outlier points that users empirically rate as more helpful. Based on these
results, FlaSH has been deployed on data streams used by public health
stakeholders.
| [
{
"version": "v1",
"created": "Thu, 29 Jun 2023 13:08:12 GMT"
},
{
"version": "v2",
"created": "Tue, 2 Jan 2024 23:09:07 GMT"
}
]
| 1,704,326,400,000 | [
[
"Joshi",
"Ananya",
""
],
[
"Mazaitis",
"Kathryn",
""
],
[
"Rosenfeld",
"Roni",
""
],
[
"Wilder",
"Bryan",
""
]
]
|
2306.16958 | Simon Ferreira | Simon Ferreira and Charles K. Assaad | Identifiability of Direct Effects from Summary Causal Graphs | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Dynamic structural causal models (SCMs) are a powerful framework for
reasoning in dynamic systems about direct effects which measure how a change in
one variable affects another variable while holding all other variables
constant. The causal relations in a dynamic structural causal model can be
qualitatively represented with an acyclic full-time causal graph. Assuming
linearity and no hidden confounding and given the full-time causal graph, the
direct causal effect is always identifiable. However, in many application such
a graph is not available for various reasons but nevertheless experts have
access to the summary causal graph of the full-time causal graph which
represents causal relations between time series while omitting temporal
information and allowing cycles. This paper presents a complete identifiability
result which characterizes all cases for which the direct effect is graphically
identifiable from a summary causal graph and gives two sound finite adjustment
sets that can be used to estimate the direct effect whenever it is
identifiable.
| [
{
"version": "v1",
"created": "Thu, 29 Jun 2023 14:05:35 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Jul 2023 15:07:46 GMT"
},
{
"version": "v3",
"created": "Thu, 1 Feb 2024 16:38:41 GMT"
},
{
"version": "v4",
"created": "Thu, 15 Feb 2024 16:42:00 GMT"
}
]
| 1,708,041,600,000 | [
[
"Ferreira",
"Simon",
""
],
[
"Assaad",
"Charles K.",
""
]
]
|
2306.17070 | Nadia M. Ady | Nadia M. Ady and Faun Rice | Interdisciplinary Methods in Computational Creativity: How Human
Variables Shape Human-Inspired AI Research | 5 pages, published in the Proceedings of the 14th International
Conference on Computational Creativity, ICCC'23 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The word creativity originally described a concept from human psychology, but
in the realm of computational creativity (CC), it has become much more. The
question of what creativity means when it is part of a computational system
might be considered core to CC. Pinning down the meaning of creativity, and
concepts like it, becomes salient when researchers port concepts from human
psychology to computation, a widespread practice extending beyond CC into
artificial intelligence (AI). Yet, the human processes shaping human-inspired
computational systems have been little investigated. In this paper, we question
which human literatures (social sciences, psychology, neuroscience) enter AI
scholarship and how they are translated at the port of entry. This study is
based on 22 in-depth, semi-structured interviews, primarily with human-inspired
AI researchers, half of whom focus on creativity as a major research area. This
paper focuses on findings most relevant to CC. We suggest that which human
literature enters AI bears greater scrutiny because ideas may become
disconnected from context in their home discipline. Accordingly, we recommend
that CC researchers document the decisions and context of their practices,
particularly those practices formalizing human concepts for machines.
Publishing reflexive commentary on human elements in CC and AI would provide a
useful record and permit greater dialogue with other disciplines.
| [
{
"version": "v1",
"created": "Thu, 29 Jun 2023 16:17:04 GMT"
}
]
| 1,688,083,200,000 | [
[
"Ady",
"Nadia M.",
""
],
[
"Rice",
"Faun",
""
]
]
|
2306.17337 | Alexander Peysakhovich | Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs | Diagnosis Uncertain Models For Medical Risk Prediction | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We consider a patient risk models which has access to patient features such
as vital signs, lab values, and prior history but does not have access to a
patient's diagnosis. For example, this occurs in a model deployed at intake
time for triage purposes. We show that such `all-cause' risk models have good
generalization across diagnoses but have a predictable failure mode. When the
same lab/vital/history profiles can result from diagnoses with different risk
profiles (e.g. E.coli vs. MRSA) the risk estimate is a probability weighted
average of these two profiles. This leads to an under-estimation of risk for
rare but highly risky diagnoses. We propose a fix for this problem by
explicitly modeling the uncertainty in risk prediction coming from uncertainty
in patient diagnoses. This gives practitioners an interpretable way to
understand patient risk beyond a single risk number.
| [
{
"version": "v1",
"created": "Thu, 29 Jun 2023 23:36:04 GMT"
}
]
| 1,688,342,400,000 | [
[
"Peysakhovich",
"Alexander",
""
],
[
"Caruana",
"Rich",
""
],
[
"Aphinyanaphongs",
"Yin",
""
]
]
|
2306.17504 | Peng Mi | Peng Mi, Li Shen, Tianhe Ren, Yiyi Zhou, Tianshuo Xu, Xiaoshuai Sun,
Tongliang Liu, Rongrong Ji, Dacheng Tao | Systematic Investigation of Sparse Perturbed Sharpness-Aware
Minimization Optimizer | arXiv admin note: substantial text overlap with arXiv:2210.05177 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep neural networks often suffer from poor generalization due to complex and
non-convex loss landscapes. Sharpness-Aware Minimization (SAM) is a popular
solution that smooths the loss landscape by minimizing the maximized change of
training loss when adding a perturbation to the weight. However, indiscriminate
perturbation of SAM on all parameters is suboptimal and results in excessive
computation, double the overhead of common optimizers like Stochastic Gradient
Descent (SGD). In this paper, we propose Sparse SAM (SSAM), an efficient and
effective training scheme that achieves sparse perturbation by a binary mask.
To obtain the sparse mask, we provide two solutions based on Fisher information
and dynamic sparse training, respectively. We investigate the impact of
different masks, including unstructured, structured, and $N$:$M$ structured
patterns, as well as explicit and implicit forms of implementing sparse
perturbation. We theoretically prove that SSAM can converge at the same rate as
SAM, i.e., $O(\log T/\sqrt{T})$. Sparse SAM has the potential to accelerate
training and smooth the loss landscape effectively. Extensive experimental
results on CIFAR and ImageNet-1K confirm that our method is superior to SAM in
terms of efficiency, and the performance is preserved or even improved with a
perturbation of merely 50\% sparsity. Code is available at
https://github.com/Mi-Peng/Systematic-Investigation-of-Sparse-Perturbed-Sharpness-Aware-Minimization-Optimizer.
| [
{
"version": "v1",
"created": "Fri, 30 Jun 2023 09:33:41 GMT"
}
]
| 1,688,342,400,000 | [
[
"Mi",
"Peng",
""
],
[
"Shen",
"Li",
""
],
[
"Ren",
"Tianhe",
""
],
[
"Zhou",
"Yiyi",
""
],
[
"Xu",
"Tianshuo",
""
],
[
"Sun",
"Xiaoshuai",
""
],
[
"Liu",
"Tongliang",
""
],
[
"Ji",
"Rongrong",
""
],
[
"Tao",
"Dacheng",
""
]
]
|
2306.17766 | Eric Pulick | Eric Pulick, Vladimir Menkov, Yonatan Mintz, Paul Kantor, Vicki Bier | Comparing Reinforcement Learning and Human Learning using the Game of
Hidden Rules | 9 pages, 4 figures, additional content in appendix | IEEE Access Volume 12 (2024) | 10.1109/ACCESS.2024.3395249 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Reliable real-world deployment of reinforcement learning (RL) methods
requires a nuanced understanding of their strengths and weaknesses and how they
compare to those of humans. Human-machine systems are becoming more prevalent
and the design of these systems relies on a task-oriented understanding of both
human learning (HL) and RL. Thus, an important line of research is
characterizing how the structure of a learning task affects learning
performance. While increasingly complex benchmark environments have led to
improved RL capabilities, such environments are difficult to use for the
dedicated study of task structure. To address this challenge we present a
learning environment built to support rigorous study of the impact of task
structure on HL and RL. We demonstrate the environment's utility for such study
through example experiments in task structure that show performance differences
between humans and RL algorithms.
| [
{
"version": "v1",
"created": "Fri, 30 Jun 2023 16:18:07 GMT"
}
]
| 1,716,249,600,000 | [
[
"Pulick",
"Eric",
""
],
[
"Menkov",
"Vladimir",
""
],
[
"Mintz",
"Yonatan",
""
],
[
"Kantor",
"Paul",
""
],
[
"Bier",
"Vicki",
""
]
]
|
2307.00735 | Chao Lei | Chao Lei, Nir Lipovetzky, Krista A. Ehinger | Novelty and Lifted Helpful Actions in Generalized Planning | Accepted at SoCS 2023 (extended version) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It has been shown recently that successful techniques in classical planning,
such as goal-oriented heuristics and landmarks, can improve the ability to
compute planning programs for generalized planning (GP) problems. In this work,
we introduce the notion of action novelty rank, which computes novelty with
respect to a planning program, and propose novelty-based generalized planning
solvers, which prune a newly generated planning program if its most frequent
action repetition is greater than a given bound $v$, implemented by
novelty-based best-first search BFS($v$) and its progressive variant PGP($v$).
Besides, we introduce lifted helpful actions in GP derived from action schemes,
and propose new evaluation functions and structural program restrictions to
scale up the search. Our experiments show that the new algorithms BFS($v$) and
PGP($v$) outperform the state-of-the-art in GP over the standard generalized
planning benchmarks. Practical findings on the above-mentioned methods in
generalized planning are briefly discussed.
| [
{
"version": "v1",
"created": "Mon, 3 Jul 2023 03:44:12 GMT"
}
]
| 1,688,428,800,000 | [
[
"Lei",
"Chao",
""
],
[
"Lipovetzky",
"Nir",
""
],
[
"Ehinger",
"Krista A.",
""
]
]
|
2307.01532 | Filip Cano C\'ordoba | Filip Cano C\'ordoba, Samuel Judson, Timos Antonopoulos, Katrine
Bj{\o}rner, Nicholas Shoemaker, Scott J. Shapiro, Ruzica Piskac and Bettina
K\"onighofer | Analyzing Intentional Behavior in Autonomous Agents under Uncertainty | 10 pages. Accepted for publication at IJCAI 2023 (Main Track) | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Principled accountability for autonomous decision-making in uncertain
environments requires distinguishing intentional outcomes from negligent
designs from actual accidents. We propose analyzing the behavior of autonomous
agents through a quantitative measure of the evidence of intentional behavior.
We model an uncertain environment as a Markov Decision Process (MDP). For a
given scenario, we rely on probabilistic model checking to compute the ability
of the agent to influence reaching a certain event. We call this the scope of
agency. We say that there is evidence of intentional behavior if the scope of
agency is high and the decisions of the agent are close to being optimal for
reaching the event. Our method applies counterfactual reasoning to
automatically generate relevant scenarios that can be analyzed to increase the
confidence of our assessment. In a case study, we show how our method can
distinguish between 'intentional' and 'accidental' traffic collisions.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2023 07:36:11 GMT"
}
]
| 1,688,601,600,000 | [
[
"Córdoba",
"Filip Cano",
""
],
[
"Judson",
"Samuel",
""
],
[
"Antonopoulos",
"Timos",
""
],
[
"Bjørner",
"Katrine",
""
],
[
"Shoemaker",
"Nicholas",
""
],
[
"Shapiro",
"Scott J.",
""
],
[
"Piskac",
"Ruzica",
""
],
[
"Könighofer",
"Bettina",
""
]
]
|
2307.01548 | Hussam Ghanem | Hussam Ghanem (ICB), Massinissa Atmani (ICB), Christophe Cruz (ICB) | Knowledge Graph for NLG in the context of conversational agents | null | French Regional Conference on Complex Systems (FRCCS 2023), May
2023, Le Havre, France | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The use of knowledge graphs (KGs) enhances the accuracy and comprehensiveness
of the responses provided by a conversational agent. While generating answers
during conversations consists in generating text from these KGs, it is still
regarded as a challenging task that has gained significant attention in recent
years. In this document, we provide a review of different architectures used
for knowledge graph-to-text generation including: Graph Neural Networks, the
Graph Transformer, and linearization with seq2seq models. We discuss the
advantages and limitations of each architecture and conclude that the choice of
architecture will depend on the specific requirements of the task at hand. We
also highlight the importance of considering constraints such as execution time
and model validity, particularly in the context of conversational agents. Based
on these constraints and the availability of labeled data for the domains of
DAVI, we choose to use seq2seq Transformer-based models (PLMs) for the
Knowledge Graph-to-Text Generation task. We aim to refine benchmark datasets of
kg-to-text generation on PLMs and to explore the emotional and multilingual
dimensions in our future work. Overall, this review provides insights into the
different approaches for knowledge graph-to-text generation and outlines future
directions for research in this area.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2023 08:03:33 GMT"
}
]
| 1,688,601,600,000 | [
[
"Ghanem",
"Hussam",
"",
"ICB"
],
[
"Atmani",
"Massinissa",
"",
"ICB"
],
[
"Cruz",
"Christophe",
"",
"ICB"
]
]
|
2307.01676 | Hyeonchang Jeon | Hyeon-Chang Jeon, In-Chang Baek, Cheong-mok Bae, Taehwa Park, Wonsang
You, Taegwan Ha, Hoyun Jung, Jinha Noh, Seungwon Oh, Kyung-Joong Kim | RaidEnv: Exploring New Challenges in Automated Content Balancing for
Boss Raid Games | 14 pages, 6 figures, 6 tables, 2 algorithms | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | The balance of game content significantly impacts the gaming experience.
Unbalanced game content diminishes engagement or increases frustration because
of repetitive failure. Although game designers intend to adjust the difficulty
of game content, this is a repetitive, labor-intensive, and challenging
process, especially for commercial-level games with extensive content. To
address this issue, the game research community has explored automated game
balancing using artificial intelligence (AI) techniques. However, previous
studies have focused on limited game content and did not consider the
importance of the generalization ability of playtesting agents when
encountering content changes. In this study, we propose RaidEnv, a new game
simulator that includes diverse and customizable content for the boss raid
scenario in MMORPG games. Additionally, we design two benchmarks for the boss
raid scenario that can aid in the practical application of game AI. These
benchmarks address two open problems in automatic content balancing, and we
introduce two evaluation metrics to provide guidance for AI in automatic
content balancing. This novel game research platform expands the frontiers of
automatic game balancing problems and offers a framework within a realistic
game production pipeline.
| [
{
"version": "v1",
"created": "Tue, 4 Jul 2023 12:07:25 GMT"
}
]
| 1,688,601,600,000 | [
[
"Jeon",
"Hyeon-Chang",
""
],
[
"Baek",
"In-Chang",
""
],
[
"Bae",
"Cheong-mok",
""
],
[
"Park",
"Taehwa",
""
],
[
"You",
"Wonsang",
""
],
[
"Ha",
"Taegwan",
""
],
[
"Jung",
"Hoyun",
""
],
[
"Noh",
"Jinha",
""
],
[
"Oh",
"Seungwon",
""
],
[
"Kim",
"Kyung-Joong",
""
]
]
|
2307.02131 | Toygar Tanyel | Toygar Tanyel, Serkan Ayvaz and Bilgin Keserci | Beyond Known Reality: Exploiting Counterfactual Explanations for Medical
Research | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The field of explainability in artificial intelligence (AI) has witnessed a
growing number of studies and increasing scholarly interest. However, the lack
of human-friendly and individual interpretations in explaining the outcomes of
machine learning algorithms has significantly hindered the acceptance of these
methods by clinicians in their research and clinical practice. To address this
issue, our study uses counterfactual explanations to explore the applicability
of "what if?" scenarios in medical research. Our aim is to expand our
understanding of magnetic resonance imaging (MRI) features used for diagnosing
pediatric posterior fossa brain tumors beyond existing boundaries. In our case
study, the proposed concept provides a novel way to examine alternative
decision-making scenarios that offer personalized and context-specific
insights, enabling the validation of predictions and clarification of
variations under diverse circumstances. Additionally, we explore the potential
use of counterfactuals for data augmentation and evaluate their feasibility as
an alternative approach in our medical research case. The results demonstrate
the promising potential of using counterfactual explanations to enhance
acceptance of AI-driven methods in clinical research.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2023 09:14:09 GMT"
},
{
"version": "v2",
"created": "Fri, 28 Jul 2023 12:28:05 GMT"
},
{
"version": "v3",
"created": "Wed, 6 Sep 2023 15:04:21 GMT"
},
{
"version": "v4",
"created": "Fri, 22 Sep 2023 08:18:33 GMT"
},
{
"version": "v5",
"created": "Sat, 14 Oct 2023 07:16:49 GMT"
}
]
| 1,697,500,800,000 | [
[
"Tanyel",
"Toygar",
""
],
[
"Ayvaz",
"Serkan",
""
],
[
"Keserci",
"Bilgin",
""
]
]
|
2307.02164 | Filip Cano C\'ordoba | Filip Cano C\'ordoba, Alexander Palmisano, Martin Fr\"anzle, Roderick
Bloem, Bettina K\"onighofer | Safety Shielding under Delayed Observation | 6 pages, Published at ICAPS 2023 (Main Track) | null | 10.1609/icaps.v33i1.27181 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Agents operating in physical environments need to be able to handle delays in
the input and output signals since neither data transmission nor sensing or
actuating the environment are instantaneous. Shields are
correct-by-construction runtime enforcers that guarantee safe execution by
correcting any action that may cause a violation of a formal safety
specification. Besides providing safety guarantees, shields should interfere
minimally with the agent. Therefore, shields should pick the safe corrective
actions in such a way that future interferences are most likely minimized.
Current shielding approaches do not consider possible delays in the input
signals in their safety analyses. In this paper, we address this issue. We
propose synthesis algorithms to compute \emph{delay-resilient shields} that
guarantee safety under worst-case assumptions on the delays of the input
signals. We also introduce novel heuristics for deciding between multiple
corrective actions, designed to minimize future shield interferences caused by
delays. As a further contribution, we present the first integration of shields
in a realistic driving simulator. We implemented our delayed shields in the
driving simulator \textsc{Carla}. We shield potentially unsafe autonomous
driving agents in different safety-critical scenarios and show the effect of
delays on the safety analysis.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2023 10:06:10 GMT"
}
]
| 1,688,601,600,000 | [
[
"Córdoba",
"Filip Cano",
""
],
[
"Palmisano",
"Alexander",
""
],
[
"Fränzle",
"Martin",
""
],
[
"Bloem",
"Roderick",
""
],
[
"Könighofer",
"Bettina",
""
]
]
|
2307.02254 | Suvojit Dhara | Suvojit Dhara and Adrijit Goswami | Analyzing Different Expert-Opined Strategies to Enhance the Effect on
the Goal of a Multi-Attribute Decision-Making System Using a Concept of
Effort Propagation and Application in Enhancement of High School Students'
Performance | 23 pages, 6 tables, 7 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In many real-world multi-attribute decision-making (MADM) problems, mining
the inter-relationships and possible hierarchical structures among the factors
are considered to be one of the primary tasks. But, besides that, one major
task is to determine an optimal strategy to work on the factors to enhance the
effect on the goal attribute. This paper proposes two such strategies, namely
parallel and hierarchical effort assignment, and propagation strategies. The
concept of effort propagation through a strategy is formally defined and
described in the paper. Both the parallel and hierarchical strategies are
divided into sub-strategies based on whether the assignment of efforts to the
factors is uniform or depends upon some appropriate heuristics related to the
factors in the system. The adapted and discussed heuristics are the relative
significance and effort propagability of the factors. The strategies are
analyzed for a real-life case study regarding Indian high school administrative
factors that play an important role in enhancing students' performance. Total
effort propagation of around 7%-15% to the goal is seen across the proposed
strategies given a total of 1 unit of effort to the directly accessible factors
of the system. A comparative analysis is adapted to determine the optimal
strategy among the proposed ones to enhance student performance most
effectively. The highest effort propagation achieved in the work is
approximately 14.4348%. The analysis in the paper establishes the necessity of
research towards the direction of effort propagation analysis in case of
decision-making problems.
| [
{
"version": "v1",
"created": "Wed, 5 Jul 2023 12:53:40 GMT"
}
]
| 1,688,601,600,000 | [
[
"Dhara",
"Suvojit",
""
],
[
"Goswami",
"Adrijit",
""
]
]
|
2307.02709 | Yongquan Yang | Yongquan Yang and Hong Bu | Validation of the Practicability of Logical Assessment Formula for
Evaluations with Inaccurate Ground-Truth Labels | arXiv admin note: substantial text overlap with arXiv:2110.11567 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Logical assessment formula (LAF) is a new theory proposed for evaluations
with inaccurate ground-truth labels (IAGTLs) to assess the predictive models
for various artificial intelligence applications. However, the practicability
of LAF for evaluations with IAGTLs has not yet been validated in real-world
practice. In this paper, to address this issue, we applied LAF to tumour
segmentation for breast cancer (TSfBC) in medical histopathology whole slide
image analysis (MHWSIA). Experimental results and analysis show the validity of
LAF for evaluations with IAGTLs in the case of TSfBC and reflect the potentials
of LAF applied to MHWSIA.
| [
{
"version": "v1",
"created": "Thu, 6 Jul 2023 01:17:29 GMT"
}
]
| 1,688,688,000,000 | [
[
"Yang",
"Yongquan",
""
],
[
"Bu",
"Hong",
""
]
]
|
2307.03171 | Rahul Mihir Patel | Rahul Patel, Elias B. Khalil | LEO: Learning Efficient Orderings for Multiobjective Binary Decision
Diagrams | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Approaches based on Binary decision diagrams (BDDs) have recently achieved
state-of-the-art results for multiobjective integer programming problems. The
variable ordering used in constructing BDDs can have a significant impact on
their size and on the quality of bounds derived from relaxed or restricted BDDs
for single-objective optimization problems. We first showcase a similar impact
of variable ordering on the Pareto frontier (PF) enumeration time for the
multiobjective knapsack problem, suggesting the need for deriving variable
ordering methods that improve the scalability of the multiobjective BDD
approach. To that end, we derive a novel parameter configuration space based on
variable scoring functions which are linear in a small set of interpretable and
easy-to-compute variable features. We show how the configuration space can be
efficiently explored using black-box optimization, circumventing the curse of
dimensionality (in the number of variables and objectives), and finding good
orderings that reduce the PF enumeration time. However, black-box optimization
approaches incur a computational overhead that outweighs the reduction in time
due to good variable ordering. To alleviate this issue, we propose LEO, a
supervised learning approach for finding efficient variable orderings that
reduce the enumeration time. Experiments on benchmark sets from the knapsack
problem with 3-7 objectives and up to 80 variables show that LEO is ~30-300%
and ~10-200% faster at PF enumeration than common ordering strategies and
algorithm configuration. Our code and instances are available at
https://github.com/khalil-research/leo.
| [
{
"version": "v1",
"created": "Thu, 6 Jul 2023 17:52:29 GMT"
}
]
| 1,688,688,000,000 | [
[
"Patel",
"Rahul",
""
],
[
"Khalil",
"Elias B.",
""
]
]
|
2307.03379 | Rodrigue de Schaetzen | Rodrigue de Schaetzen, Alessandro Sestini | Efficient Ground Vehicle Path Following in Game AI | 4 pages, 3 figures, to be published in IEEE Conference on Games 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | This short paper presents an efficient path following solution for ground
vehicles tailored to game AI. Our focus is on adapting established techniques
to design simple solutions with parameters that are easily tunable for an
efficient benchmark path follower. Our solution pays particular attention to
computing a target speed which uses quadratic Bezier curves to estimate the
path curvature. The performance of the proposed path follower is evaluated
through a variety of test scenarios in a first-person shooter game,
demonstrating its effectiveness and robustness in handling different types of
paths and vehicles. We achieved a 70% decrease in the total number of stuck
events compared to an existing path following solution.
| [
{
"version": "v1",
"created": "Fri, 7 Jul 2023 04:20:07 GMT"
}
]
| 1,688,947,200,000 | [
[
"de Schaetzen",
"Rodrigue",
""
],
[
"Sestini",
"Alessandro",
""
]
]
|
2307.03637 | Nikhil Prakash | Xander Davies, Max Nadeau, Nikhil Prakash, Tamar Rott Shaham, David
Bau | Discovering Variable Binding Circuitry with Desiderata | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recent work has shown that computation in language models may be
human-understandable, with successful efforts to localize and intervene on both
single-unit features and input-output circuits. Here, we introduce an approach
which extends causal mediation experiments to automatically identify model
components responsible for performing a specific subtask by solely specifying a
set of \textit{desiderata}, or causal attributes of the model components
executing that subtask. As a proof of concept, we apply our method to
automatically discover shared \textit{variable binding circuitry} in LLaMA-13B,
which retrieves variable values for multiple arithmetic tasks. Our method
successfully localizes variable binding to only 9 attention heads (of the 1.6k)
and one MLP in the final token's residual stream.
| [
{
"version": "v1",
"created": "Fri, 7 Jul 2023 14:51:30 GMT"
}
]
| 1,688,947,200,000 | [
[
"Davies",
"Xander",
""
],
[
"Nadeau",
"Max",
""
],
[
"Prakash",
"Nikhil",
""
],
[
"Shaham",
"Tamar Rott",
""
],
[
"Bau",
"David",
""
]
]
|
2307.03937 | Shixuan Liu | Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou
Sun, Zhong Liu | Inductive Meta-path Learning for Schema-complex Heterogeneous
Information Networks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Heterogeneous Information Networks (HINs) are information networks with
multiple types of nodes and edges. The concept of meta-path, i.e., a sequence
of entity types and relation types connecting two entities, is proposed to
provide the meta-level explainable semantics for various HIN tasks.
Traditionally, meta-paths are primarily used for schema-simple HINs, e.g.,
bibliographic networks with only a few entity types, where meta-paths are often
enumerated with domain knowledge. However, the adoption of meta-paths for
schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and
relation types, has been limited due to the computational complexity associated
with meta-path enumeration. Additionally, effectively assessing meta-paths
requires enumerating relevant path instances, which adds further complexity to
the meta-path learning process. To address these challenges, we propose
SchemaWalk, an inductive meta-path learning framework for schema-complex HINs.
We represent meta-paths with schema-level representations to support the
learning of the scores of meta-paths for varying relations, mitigating the need
of exhaustive path instance enumeration for each relation. Further, we design a
reinforcement-learning based path-finding agent, which directly navigates the
network schema (i.e., schema graph) to learn policies for establishing
meta-paths with high coverage and confidence for multiple relations. Extensive
experiments on real data sets demonstrate the effectiveness of our proposed
paradigm.
| [
{
"version": "v1",
"created": "Sat, 8 Jul 2023 09:10:43 GMT"
}
]
| 1,689,033,600,000 | [
[
"Liu",
"Shixuan",
""
],
[
"Fan",
"Changjun",
""
],
[
"Cheng",
"Kewei",
""
],
[
"Wang",
"Yunfei",
""
],
[
"Cui",
"Peng",
""
],
[
"Sun",
"Yizhou",
""
],
[
"Liu",
"Zhong",
""
]
]
|
2307.04029 | Nimrod Megiddo | Nimrod Megiddo | On "Indifference" and Backward Induction in Games with Perfect
Information | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Indifference of a player with respect to two distinct outcomes of a game
cannot be handled by small perturbations, because the actual choice may have
significant impact on other players, and cause them to act in a way that has
significant impact of the indifferent player. It is argued that ties among
rational choices can be resolved by refinements of the concept of rationality
based on the utilities of other players. One such refinement is the concept of
Tit-for-Tat.
| [
{
"version": "v1",
"created": "Sat, 8 Jul 2023 18:38:56 GMT"
}
]
| 1,689,033,600,000 | [
[
"Megiddo",
"Nimrod",
""
]
]
|
2307.04608 | Yannet Interian | Yannet Interian and Sara Bernardini | Learning Interpretable Heuristics for WalkSAT | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Local search algorithms are well-known methods for solving large, hard
instances of the satisfiability problem (SAT). The performance of these
algorithms crucially depends on heuristics for setting noise parameters and
scoring variables. The optimal setting for these heuristics varies for
different instance distributions. In this paper, we present an approach for
learning effective variable scoring functions and noise parameters by using
reinforcement learning. We consider satisfiability problems from different
instance distributions and learn specialized heuristics for each of them. Our
experimental results show improvements with respect to both a WalkSAT baseline
and another local search learned heuristic.
| [
{
"version": "v1",
"created": "Mon, 10 Jul 2023 14:52:14 GMT"
}
]
| 1,689,033,600,000 | [
[
"Interian",
"Yannet",
""
],
[
"Bernardini",
"Sara",
""
]
]
|
2307.04701 | Ebaa Alnazer | Ebaa Alnazer and Ilche Georgievski | Understanding Real-World AI Planning Domains: A Conceptual Framework | 21 pages, 3 figures, 17th Symposium and Summer School (SummerSOC)
2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Planning is a pivotal ability of any intelligent system being developed for
real-world applications. AI planning is concerned with researching and
developing planning systems that automatically compute plans that satisfy some
user objective. Identifying and understanding the relevant and realistic
aspects that characterise real-world application domains are crucial to the
development of AI planning systems. This provides guidance to knowledge
engineers and software engineers in the process of designing, identifying, and
categorising resources required for the development process. To the best of our
knowledge, such support does not exist. We address this research gap by
developing a conceptual framework that identifies and categorises the aspects
of real-world planning domains in varying levels of granularity. Our framework
provides not only a common terminology but also a comprehensive overview of a
broad range of planning aspects exemplified using the domain of sustainable
buildings as a prominent application domain of AI planning. The framework has
the potential to impact the design, development, and applicability of AI
planning systems in real-world application domains.
| [
{
"version": "v1",
"created": "Mon, 10 Jul 2023 16:58:37 GMT"
}
]
| 1,689,033,600,000 | [
[
"Alnazer",
"Ebaa",
""
],
[
"Georgievski",
"Ilche",
""
]
]
|
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