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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2205.02562 | Annet Onnes | Annet Onnes | Monitoring AI systems: A Problem Analysis, Framework and Outlook | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge-based systems have been used to monitor machines and processes in
the real world. In this paper we propose the use of knowledge-based systems to
monitor other AI systems in operation. We motivate and provide a problem
analysis of this novel setting and subsequently propose a framework that allows
for structuring future research related to this setting. Several directions for
further research are also discussed.
| [
{
"version": "v1",
"created": "Thu, 5 May 2022 10:51:59 GMT"
}
] | 1,651,795,200,000 | [
[
"Onnes",
"Annet",
""
]
] |
2205.02919 | Camilo Sarmiento | Camilo Sarmiento, Gauvain Bourgne, Katsumi Inoue, Daniele Cavalli,
Jean-Gabriel Ganascia | Action Languages Based Actual Causality for Computational Ethics: a
Sound and Complete Implementation in ASP | 22 pages, 7 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Although moral responsibility is not circumscribed by causality, they are
both closely intermixed. Furthermore, rationally understanding the evolution of
the physical world is inherently linked with the idea of causality. Thus, the
decision-making applications based on automated planning inevitably have to
deal with causality, especially if they consider imputability aspects or
integrate references to ethical norms. The many debates around causation in the
last decades have shown how complex this notion is and thus, how difficult is
its integration with planning. As a result, much of the work in computational
ethics relegates causality to the background, despite the considerations stated
above. This paper's contribution is to provide a complete and sound translation
into logic programming from an actual causation definition suitable for action
languages, this definition is a formalisation of Wright's NESS test. The
obtained logic program allows to deal with complex causal relations. In
addition to enabling agents to reason about causality, this contribution
specifically enables the computational ethics domain to handle situations that
were previously out of reach. In a context where ethical considerations in
decision-making are increasingly important, advances in computational ethics
can greatly benefit the entire AI community.
| [
{
"version": "v1",
"created": "Thu, 5 May 2022 21:00:59 GMT"
},
{
"version": "v2",
"created": "Wed, 24 May 2023 12:43:13 GMT"
}
] | 1,684,972,800,000 | [
[
"Sarmiento",
"Camilo",
""
],
[
"Bourgne",
"Gauvain",
""
],
[
"Inoue",
"Katsumi",
""
],
[
"Cavalli",
"Daniele",
""
],
[
"Ganascia",
"Jean-Gabriel",
""
]
] |
2205.02923 | Shereen Elsayed | Shereen Elsayed, Lukas Brinkmeyer and Lars Schmidt-Thieme | End-to-End Image-Based Fashion Recommendation | Accepted in FashionXRecsys 2021 workshop | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In fashion-based recommendation settings, incorporating the item image
features is considered a crucial factor, and it has shown significant
improvements to many traditional models, including but not limited to matrix
factorization, auto-encoders, and nearest neighbor models. While there are
numerous image-based recommender approaches that utilize dedicated deep neural
networks, comparisons to attribute-aware models are often disregarded despite
their ability to be easily extended to leverage items' image features. In this
paper, we propose a simple yet effective attribute-aware model that
incorporates image features for better item representation learning in item
recommendation tasks. The proposed model utilizes items' image features
extracted by a calibrated ResNet50 component. We present an ablation study to
compare incorporating the image features using three different techniques into
the recommender system component that can seamlessly leverage any available
items' attributes. Experiments on two image-based real-world recommender
systems datasets show that the proposed model significantly outperforms all
state-of-the-art image-based models.
| [
{
"version": "v1",
"created": "Thu, 5 May 2022 21:14:42 GMT"
}
] | 1,652,054,400,000 | [
[
"Elsayed",
"Shereen",
""
],
[
"Brinkmeyer",
"Lukas",
""
],
[
"Schmidt-Thieme",
"Lars",
""
]
] |
2205.03151 | Wolfgang Dvo\v{r}\'ak | Wolfgang Dvo\v{r}\'ak, Matthias K\"onig, Markus Ulbricht, Stefan
Woltran | Rediscovering Argumentation Principles Utilizing Collective Attacks | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Argumentation Frameworks (AFs) are a key formalism in AI research. Their
semantics have been investigated in terms of principles, which define
characteristic properties in order to deliver guidance for analysing
established and developing new semantics. Because of the simple structure of
AFs, many desired properties hold almost trivially, at the same time hiding
interesting concepts behind syntactic notions. We extend the principle-based
approach to Argumentation Frameworks with Collective Attacks (SETAFs) and
provide a comprehensive overview of common principles for their semantics. Our
analysis shows that investigating principles based on decomposing the given
SETAF (e.g. directionality or SCC-recursiveness) poses additional challenges in
comparison to usual AFs. We introduce the notion of the reduct as well as the
modularization principle for SETAFs which will prove beneficial for this kind
of investigation. We then demonstrate how our findings can be utilized for
incremental computation of extensions and give a novel parameterized
tractability result for verifying preferred extensions.
| [
{
"version": "v1",
"created": "Fri, 6 May 2022 11:41:23 GMT"
}
] | 1,652,054,400,000 | [
[
"Dvořák",
"Wolfgang",
""
],
[
"König",
"Matthias",
""
],
[
"Ulbricht",
"Markus",
""
],
[
"Woltran",
"Stefan",
""
]
] |
2205.03219 | Prerna Agarwal | Prerna Agarwal, Avani Gupta, Renuka Sindhgatta, Sampath Dechu | Goal-Oriented Next Best Activity Recommendation using Reinforcement
Learning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Recommending a sequence of activities for an ongoing case requires that the
recommendations conform to the underlying business process and meet the
performance goal of either completion time or process outcome. Existing work on
next activity prediction can predict the future activity but cannot provide
guarantees of the prediction being conformant or meeting the goal. Hence, we
propose a goal-oriented next best activity recommendation. Our proposed
framework uses a deep learning model to predict the next best activity and an
estimated value of a goal given the activity. A reinforcement learning method
explores the sequence of activities based on the estimates likely to meet one
or more goals. We further address a real-world problem of multiple goals by
introducing an additional reward function to balance the outcome of a
recommended activity and satisfy the goal. We demonstrate the effectiveness of
the proposed method on four real-world datasets with different characteristics.
The results show that the recommendations from our proposed approach outperform
in goal satisfaction and conformance compared to the existing state-of-the-art
next best activity recommendation techniques.
| [
{
"version": "v1",
"created": "Fri, 6 May 2022 13:48:14 GMT"
}
] | 1,652,054,400,000 | [
[
"Agarwal",
"Prerna",
""
],
[
"Gupta",
"Avani",
""
],
[
"Sindhgatta",
"Renuka",
""
],
[
"Dechu",
"Sampath",
""
]
] |
2205.03375 | Debarun Bhattacharjya | Debarun Bhattacharjya, Saurabh Sihag, Oktie Hassanzadeh, Liza Bialik | Summary Markov Models for Event Sequences | In Proceedings of International Joint Conference on Artificial
Intelligence (IJCAI) 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Datasets involving sequences of different types of events without meaningful
time stamps are prevalent in many applications, for instance when extracted
from textual corpora. We propose a family of models for such event sequences --
summary Markov models -- where the probability of observing an event type
depends only on a summary of historical occurrences of its influencing set of
event types. This Markov model family is motivated by Granger causal models for
time series, with the important distinction that only one event can occur in a
position in an event sequence. We show that a unique minimal influencing set
exists for any set of event types of interest and choice of summary function,
formulate two novel models from the general family that represent specific
sequence dynamics, and propose a greedy search algorithm for learning them from
event sequence data. We conduct an experimental investigation comparing the
proposed models with relevant baselines, and illustrate their knowledge
acquisition and discovery capabilities through case studies involving sequences
from text.
| [
{
"version": "v1",
"created": "Fri, 6 May 2022 17:16:24 GMT"
}
] | 1,652,054,400,000 | [
[
"Bhattacharjya",
"Debarun",
""
],
[
"Sihag",
"Saurabh",
""
],
[
"Hassanzadeh",
"Oktie",
""
],
[
"Bialik",
"Liza",
""
]
] |
2205.03468 | Daniel Zhang | Daniel Zhang, Nestor Maslej, Erik Brynjolfsson, John Etchemendy, Terah
Lyons, James Manyika, Helen Ngo, Juan Carlos Niebles, Michael Sellitto, Ellie
Sakhaee, Yoav Shoham, Jack Clark, Raymond Perrault | The AI Index 2022 Annual Report | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Welcome to the fifth edition of the AI Index Report! The latest edition
includes data from a broad set of academic, private, and nonprofit
organizations as well as more self-collected data and original analysis than
any previous editions, including an expanded technical performance chapter, a
new survey of robotics researchers around the world, data on global AI
legislation records in 25 countries, and a new chapter with an in-depth
analysis of technical AI ethics metrics.
The AI Index Report tracks, collates, distills, and visualizes data related
to artificial intelligence. Its mission is to provide unbiased, rigorously
vetted, and globally sourced data for policymakers, researchers, executives,
journalists, and the general public to develop a more thorough and nuanced
understanding of the complex field of AI. The report aims to be the world's
most credible and authoritative source for data and insights about AI.
| [
{
"version": "v1",
"created": "Mon, 2 May 2022 20:59:33 GMT"
}
] | 1,652,140,800,000 | [
[
"Zhang",
"Daniel",
""
],
[
"Maslej",
"Nestor",
""
],
[
"Brynjolfsson",
"Erik",
""
],
[
"Etchemendy",
"John",
""
],
[
"Lyons",
"Terah",
""
],
[
"Manyika",
"James",
""
],
[
"Ngo",
"Helen",
""
],
[
"Niebles",
"Juan Carlos",
""
],
[
"Sellitto",
"Michael",
""
],
[
"Sakhaee",
"Ellie",
""
],
[
"Shoham",
"Yoav",
""
],
[
"Clark",
"Jack",
""
],
[
"Perrault",
"Raymond",
""
]
] |
2205.03824 | Zhenghua Chen | Zhenghua Chen, Min Wu, Alvin Chan, Xiaoli Li, Yew-Soon Ong | A Survey on AI Sustainability: Emerging Trends on Learning Algorithms
and Research Challenges | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Artificial Intelligence (AI) is a fast-growing research and development (R&D)
discipline which is attracting increasing attention because of its promises to
bring vast benefits for consumers and businesses, with considerable benefits
promised in productivity growth and innovation. To date it has reported
significant accomplishments in many areas that have been deemed as challenging
for machines, ranging from computer vision, natural language processing, audio
analysis to smart sensing and many others. The technical trend in realizing the
successes has been towards increasing complex and large size AI models so as to
solve more complex problems at superior performance and robustness. This rapid
progress, however, has taken place at the expense of substantial environmental
costs and resources. Besides, debates on the societal impacts of AI, such as
fairness, safety and privacy, have continued to grow in intensity. These issues
have presented major concerns pertaining to the sustainable development of AI.
In this work, we review major trends in machine learning approaches that can
address the sustainability problem of AI. Specifically, we examine emerging AI
methodologies and algorithms for addressing the sustainability issue of AI in
two major aspects, i.e., environmental sustainability and social sustainability
of AI. We will also highlight the major limitations of existing studies and
propose potential research challenges and directions for the development of
next generation of sustainable AI techniques. We believe that this technical
review can help to promote a sustainable development of AI R&D activities for
the research community.
| [
{
"version": "v1",
"created": "Sun, 8 May 2022 09:38:35 GMT"
}
] | 1,652,140,800,000 | [
[
"Chen",
"Zhenghua",
""
],
[
"Wu",
"Min",
""
],
[
"Chan",
"Alvin",
""
],
[
"Li",
"Xiaoli",
""
],
[
"Ong",
"Yew-Soon",
""
]
] |
2205.03854 | John Laird | John E. Laird | Introduction to Soar | 29 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | This paper is the recommended initial reading for a functional overview of
Soar, version 9.6. It includes an abstract overview of the architectural
structure of Soar including its processing, memories, learning modules, their
interfaces, and the representations of knowledge used by those modules. From
there it describes the processing supported by those modules, including
decision making, impasses and substates, procedure learning via chunking,
reinforcement learning, semantic memory, episodic memory, and spatial-visual
reasoning. It then reviews the levels of decision making and variety of
learning in Soar, and analysis of Soar as an architecture supporting general
human-level AI. Following the references is an appendix that contains short
descriptions of recent Soar agents and a glossary of the terminology we use in
describing Soar.
| [
{
"version": "v1",
"created": "Sun, 8 May 2022 12:44:51 GMT"
}
] | 1,652,140,800,000 | [
[
"Laird",
"John E.",
""
]
] |
2205.03931 | Hammaad Adam | Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles
Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi | Write It Like You See It: Detectable Differences in Clinical Notes By
Race Lead To Differential Model Recommendations | null | Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and
Society (AIES 2022) | 10.1145/3514094.3534203 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Clinical notes are becoming an increasingly important data source for machine
learning (ML) applications in healthcare. Prior research has shown that
deploying ML models can perpetuate existing biases against racial minorities,
as bias can be implicitly embedded in data. In this study, we investigate the
level of implicit race information available to ML models and human experts and
the implications of model-detectable differences in clinical notes. Our work
makes three key contributions. First, we find that models can identify patient
self-reported race from clinical notes even when the notes are stripped of
explicit indicators of race. Second, we determine that human experts are not
able to accurately predict patient race from the same redacted clinical notes.
Finally, we demonstrate the potential harm of this implicit information in a
simulation study, and show that models trained on these race-redacted clinical
notes can still perpetuate existing biases in clinical treatment decisions.
| [
{
"version": "v1",
"created": "Sun, 8 May 2022 18:24:11 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Nov 2022 18:07:27 GMT"
}
] | 1,667,433,600,000 | [
[
"Adam",
"Hammaad",
""
],
[
"Yang",
"Ming Ying",
""
],
[
"Cato",
"Kenrick",
""
],
[
"Baldini",
"Ioana",
""
],
[
"Senteio",
"Charles",
""
],
[
"Celi",
"Leo Anthony",
""
],
[
"Zeng",
"Jiaming",
""
],
[
"Singh",
"Moninder",
""
],
[
"Ghassemi",
"Marzyeh",
""
]
] |
2205.04522 | John Rushby | Robin Bloomfield and John Rushby | Assessing Confidence with Assurance 2.0 | Second Edition | null | null | SRI-CSL-2022-02 R2 | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | An assurance case is intended to provide justifiable confidence in the truth
of its top claim, which typically concerns safety or security. A natural
question is then "how much" confidence does the case provide? We argue that
confidence cannot be reduced to a single attribute or measurement. Instead, we
suggest it should be based on attributes that draw on three different
perspectives: positive, negative, and residual doubts.
Positive Perspectives consider the extent to which the evidence and overall
argument of the case combine to make a positive statement justifying belief in
its claims. We set a high bar for justification, requiring it to be
indefeasible. The primary positive measure for this is soundness, which
interprets the argument as a logical proof. Confidence in evidence can be
expressed probabilistically and we use confirmation measures to ensure that the
"weight" of evidence crosses some threshold. In addition, probabilities can be
aggregated from evidence through the steps of the argument using probability
logics to yield what we call probabilistic valuations for the claims.
Negative Perspectives record doubts and challenges to the case, typically
expressed as defeaters, and their exploration and resolution. Assurance
developers must guard against confirmation bias and should vigorously explore
potential defeaters as they develop the case, and should record them and their
resolution to avoid rework and to aid reviewers.
Residual Doubts: the world is uncertain so not all potential defeaters can be
resolved. We explore risks and may deem them acceptable or unavoidable. It is
crucial however that these judgments are conscious ones and that they are
recorded in the assurance case.
This report examines the perspectives in detail and indicates how Clarissa,
our prototype toolset for Assurance 2.0, assists in their evaluation.
| [
{
"version": "v1",
"created": "Tue, 3 May 2022 22:10:59 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Jun 2022 19:33:34 GMT"
},
{
"version": "v3",
"created": "Fri, 26 May 2023 06:27:51 GMT"
},
{
"version": "v4",
"created": "Fri, 3 May 2024 05:36:36 GMT"
}
] | 1,714,953,600,000 | [
[
"Bloomfield",
"Robin",
""
],
[
"Rushby",
"John",
""
]
] |
2205.04541 | Jesse Heyninck | Simon Marynissen, Jesse Heyninck, Bart Bogaerts, Marc Denecker | On Nested Justification Systems (full version) | Paper presented at the 38th International Conference on Logic
Programming (ICLP 2022), 16 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Justification theory is a general framework for the definition of semantics
of rule-based languages that has a high explanatory potential. Nested
justification systems, first introduced by Denecker et al. (2015), allow for
the composition of justification systems. This notion of nesting thus enables
the modular definition of semantics of rule-based languages, and increases the
representational capacities of justification theory. As we show in this paper,
the original semantics for nested justification systems lead to the loss of
information relevant for explanations. In view of this problem, we provide an
alternative characterization of semantics of nested justification systems and
show that this characterization is equivalent to the original semantics.
Furthermore, we show how nested justification systems allow representing
fixpoint definitions (Hou and Denecker 2009).
| [
{
"version": "v1",
"created": "Mon, 9 May 2022 20:23:22 GMT"
}
] | 1,652,227,200,000 | [
[
"Marynissen",
"Simon",
""
],
[
"Heyninck",
"Jesse",
""
],
[
"Bogaerts",
"Bart",
""
],
[
"Denecker",
"Marc",
""
]
] |
2205.04827 | Marco Pegoraro | Marco Pegoraro | Probabilistic and Non-Deterministic Event Data in Process Mining:
Embedding Uncertainty in Process Analysis Techniques | 12 pages, 4 figures, 4 tables, 16 references. arXiv admin note: text
overlap with arXiv:2010.00334 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Process mining is a subfield of process science that analyzes event data
collected in databases called event logs. Recently, novel types of event data
have become of interest due to the wide industrial application of process
mining analyses. In this paper, we examine uncertain event data. Such data
contain meta-attributes describing the amount of imprecision tied with
attributes recorded in an event log. We provide examples of uncertain event
data, present the state of the art in regard of uncertainty in process mining,
and illustrate open challenges related to this research direction.
| [
{
"version": "v1",
"created": "Tue, 10 May 2022 12:00:02 GMT"
},
{
"version": "v2",
"created": "Wed, 11 May 2022 09:33:53 GMT"
}
] | 1,652,313,600,000 | [
[
"Pegoraro",
"Marco",
""
]
] |
2205.04850 | Javier Segovia Aguas | Javier Segovia-Aguas, Sergio Jim\'enez, Anders Jonsson and Laura
Sebasti\'a | Scaling-up Generalized Planning as Heuristic Search with Landmarks | Accepted at SoCS 2022 (extended version) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Landmarks are one of the most effective search heuristics for classical
planning, but largely ignored in generalized planning. Generalized planning
(GP) is usually addressed as a combinatorial search in a given space of
algorithmic solutions, where candidate solutions are evaluated w.r.t.~the
instances they solve. This type of solution evaluation ignores any sub-goal
information that is not explicit in the representation of the planning
instances, causing plateaus in the space of candidate generalized plans.
Furthermore, node expansion in GP is a run-time bottleneck since it requires
evaluating every child node over the entire batch of classical planning
instances in a GP problem. In this paper we define a landmark counting
heuristic for GP (that considers sub-goal information that is not explicitly
represented in the planning instances), and a novel heuristic search algorithm
for GP (that we call PGP) and that progressively processes subsets of the
planning instances of a GP problem. Our two orthogonal contributions are
analyzed in an ablation study, showing that both improve the state-of-the-art
in GP as heuristic search, and that both benefit from each other when used in
combination.
| [
{
"version": "v1",
"created": "Tue, 10 May 2022 12:42:48 GMT"
}
] | 1,652,227,200,000 | [
[
"Segovia-Aguas",
"Javier",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Jonsson",
"Anders",
""
],
[
"Sebastiá",
"Laura",
""
]
] |
2205.05030 | Emmanuelle-Anna Dietz | Emmanuelle Dietz, Johannes K. Fichte, Florim Hamiti | A Quantitative Symbolic Approach to Individual Human Reasoning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Cognitive theories for reasoning are about understanding how humans come to
conclusions from a set of premises. Starting from hypothetical thoughts, we are
interested which are the implications behind basic everyday language and how do
we reason with them. A widely studied topic is whether cognitive theories can
account for typical reasoning tasks and be confirmed by own empirical
experiments. This paper takes a different view and we do not propose a theory,
but instead take findings from the literature and show how these, formalized as
cognitive principles within a logical framework, can establish a quantitative
notion of reasoning, which we call plausibility. For this purpose, we employ
techniques from non-monotonic reasoning and computer science, namely, a solving
paradigm called answer set programming (ASP). Finally, we can fruitfully use
plausibility reasoning in ASP to test the effects of an existing experiment and
explain different majority responses.
| [
{
"version": "v1",
"created": "Tue, 10 May 2022 16:43:47 GMT"
}
] | 1,652,227,200,000 | [
[
"Dietz",
"Emmanuelle",
""
],
[
"Fichte",
"Johannes K.",
""
],
[
"Hamiti",
"Florim",
""
]
] |
2205.05228 | Sungkweon Hong | Sungkweon Hong and Brian C. Williams | Hierarchical Constrained Stochastic Shortest Path Planning via Cost
Budget Allocation | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Stochastic sequential decision making often requires hierarchical structure
in the problem where each high-level action should be further planned with
primitive states and actions. In addition, many real-world applications require
a plan that satisfies constraints on the secondary costs such as risk measure
or fuel consumption. In this paper, we propose a hierarchical constrained
stochastic shortest path problem (HC-SSP) that meets those two crucial
requirements in a single framework. Although HC-SSP provides a useful framework
to model such planning requirements in many real-world applications, the
resulting problem has high complexity and makes it difficult to find an optimal
solution fast which prevents user from applying it to real-time and
risk-sensitive applications. To address this problem, we present an algorithm
that iteratively allocates cost budget to lower level planning problems based
on branch-and-bound scheme to find a feasible solution fast and incrementally
update the incumbent solution. We demonstrate the proposed algorithm in an
evacuation scenario and prove the advantage over a state-of-the-art
mathematical programming based approach.
| [
{
"version": "v1",
"created": "Wed, 11 May 2022 01:25:38 GMT"
}
] | 1,652,313,600,000 | [
[
"Hong",
"Sungkweon",
""
],
[
"Williams",
"Brian C.",
""
]
] |
2205.05268 | Toby Walsh | Toby Walsh | The Meta-Turing Test | Appeared in AAAI 2017 Workshop - Technical Report, San Francisco,
California USA, pp. 132 - 137, presented at AAAI 2017 conference | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose an alternative to the Turing test that removes the inherent
asymmetry between humans and machines in Turing's original imitation game. In
this new test, both humans and machines judge each other. We argue that this
makes the test more robust against simple deceptions. We also propose a small
number of refinements to improve further the test. These refinements could be
applied also to Turing's original imitation game.
| [
{
"version": "v1",
"created": "Wed, 11 May 2022 04:54:14 GMT"
}
] | 1,652,313,600,000 | [
[
"Walsh",
"Toby",
""
]
] |
2205.05793 | Hjalmar Wijk | Hjalmar Wijk, Benjie Wang, Marta Kwiatkowska | Robustness Guarantees for Credal Bayesian Networks via Constraint
Relaxation over Probabilistic Circuits | 11 pages (8+3 Appendix). To be published in IJCAI 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In many domains, worst-case guarantees on the performance (e.g., prediction
accuracy) of a decision function subject to distributional shifts and
uncertainty about the environment are crucial. In this work we develop a method
to quantify the robustness of decision functions with respect to credal
Bayesian networks, formal parametric models of the environment where
uncertainty is expressed through credal sets on the parameters. In particular,
we address the maximum marginal probability (MARmax) problem, that is,
determining the greatest probability of an event (such as misclassification)
obtainable for parameters in the credal set. We develop a method to faithfully
transfer the problem into a constrained optimization problem on a probabilistic
circuit. By performing a simple constraint relaxation, we show how to obtain a
guaranteed upper bound on MARmax in linear time in the size of the circuit. We
further theoretically characterize this constraint relaxation in terms of the
original Bayesian network structure, which yields insight into the tightness of
the bound. We implement the method and provide experimental evidence that the
upper bound is often near tight and demonstrates improved scalability compared
to other methods.
| [
{
"version": "v1",
"created": "Wed, 11 May 2022 22:37:07 GMT"
}
] | 1,652,400,000,000 | [
[
"Wijk",
"Hjalmar",
""
],
[
"Wang",
"Benjie",
""
],
[
"Kwiatkowska",
"Marta",
""
]
] |
2205.06241 | Marko Tesic | Marko Tesic, Ulrike Hahn | Can counterfactual explanations of AI systems' predictions skew lay
users' causal intuitions about the world? If so, can we correct for that? | null | Patterns, 3(12), 2022 | 10.1016/j.patter.2022.100635 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Counterfactual (CF) explanations have been employed as one of the modes of
explainability in explainable AI-both to increase the transparency of AI
systems and to provide recourse. Cognitive science and psychology, however,
have pointed out that people regularly use CFs to express causal relationships.
Most AI systems are only able to capture associations or correlations in data
so interpreting them as casual would not be justified. In this paper, we
present two experiment (total N = 364) exploring the effects of CF explanations
of AI system's predictions on lay people's causal beliefs about the real world.
In Experiment 1 we found that providing CF explanations of an AI system's
predictions does indeed (unjustifiably) affect people's causal beliefs
regarding factors/features the AI uses and that people are more likely to view
them as causal factors in the real world. Inspired by the literature on
misinformation and health warning messaging, Experiment 2 tested whether we can
correct for the unjustified change in causal beliefs. We found that pointing
out that AI systems capture correlations and not necessarily causal
relationships can attenuate the effects of CF explanations on people's causal
beliefs.
| [
{
"version": "v1",
"created": "Thu, 12 May 2022 17:39:54 GMT"
},
{
"version": "v2",
"created": "Mon, 12 Dec 2022 14:49:22 GMT"
}
] | 1,670,976,000,000 | [
[
"Tesic",
"Marko",
""
],
[
"Hahn",
"Ulrike",
""
]
] |
2205.06259 | Javier Segovia Aguas | Javier Segovia-Aguas, Sergio Jim\'enez, Anders Jonsson | Computing Programs for Generalized Planning as Heuristic Search | Extended abstract accepted at IJCAI-22 Sister Conferences Best Paper
Track. arXiv admin note: substantial text overlap with arXiv:2103.14434 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Although heuristic search is one of the most successful approaches to
classical planning, this planning paradigm does not apply straightforwardly to
Generalized Planning (GP). This paper adapts the planning as heuristic search
paradigm to the particularities of GP, and presents the first native heuristic
search approach to GP. First, the paper defines a program-based solution space
for GP that is independent of the number of planning instances in a GP problem,
and the size of these instances. Second, the paper defines the BFGP algorithm
for GP, that implements a best-first search in our program-based solution
space, and that is guided by different evaluation and heuristic functions.
| [
{
"version": "v1",
"created": "Thu, 12 May 2022 17:57:09 GMT"
}
] | 1,652,400,000,000 | [
[
"Segovia-Aguas",
"Javier",
""
],
[
"Jiménez",
"Sergio",
""
],
[
"Jonsson",
"Anders",
""
]
] |
2205.06454 | Shengyao Lu | Shengyao Lu, Bang Liu, Keith G. Mills, Shangling Jui, Di Niu | R5: Rule Discovery with Reinforced and Recurrent Relational Reasoning | ICLR 2022 Spotlight | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Systematicity, i.e., the ability to recombine known parts and rules to form
new sequences while reasoning over relational data, is critical to machine
intelligence. A model with strong systematicity is able to train on small-scale
tasks and generalize to large-scale tasks. In this paper, we propose R5, a
relational reasoning framework based on reinforcement learning that reasons
over relational graph data and explicitly mines underlying compositional
logical rules from observations. R5 has strong systematicity and being robust
to noisy data. It consists of a policy value network equipped with Monte Carlo
Tree Search to perform recurrent relational prediction and a backtrack
rewriting mechanism for rule mining. By alternately applying the two
components, R5 progressively learns a set of explicit rules from data and
performs explainable and generalizable relation prediction. We conduct
extensive evaluations on multiple datasets. Experimental results show that R5
outperforms various embedding-based and rule induction baselines on relation
prediction tasks while achieving a high recall rate in discovering ground truth
rules.
| [
{
"version": "v1",
"created": "Fri, 13 May 2022 05:53:32 GMT"
}
] | 1,652,659,200,000 | [
[
"Lu",
"Shengyao",
""
],
[
"Liu",
"Bang",
""
],
[
"Mills",
"Keith G.",
""
],
[
"Jui",
"Shangling",
""
],
[
"Niu",
"Di",
""
]
] |
2205.06483 | Andrew Fuchs | Andrew Fuchs and Andrea Passarella and Marco Conti | Modeling Human Behavior Part II -- Cognitive approaches and Uncertainty | This is Part 2 of our review (see Modeling Human Behavior Part I -
Learning and Belief Approaches) relating to learning and modeling behavior.
This work was partially funded by the following projects. European Union's
Horizon 2020 research and innovation programme: HumaneAI-Net (No 952026).
CHIST-ERA program: SAI project (grant CHIST-ERA-19-XAI-010, funded by MUR,
grant number not yet available) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | As we discussed in Part I of this topic, there is a clear desire to model and
comprehend human behavior. Given the popular presupposition of human reasoning
as the standard for learning and decision-making, there have been significant
efforts and a growing trend in research to replicate these innate human
abilities in artificial systems. In Part I, we discussed learning methods which
generate a model of behavior from exploration of the system and feedback based
on the exhibited behavior as well as topics relating to the use of or
accounting for beliefs with respect to applicable skills or mental states of
others. In this work, we will continue the discussion from the perspective of
methods which focus on the assumed cognitive abilities, limitations, and biases
demonstrated in human reasoning. We will arrange these topics as follows (i)
methods such as cognitive architectures, cognitive heuristics, and related
which demonstrate assumptions of limitations on cognitive resources and how
that impacts decisions and (ii) methods which generate and utilize
representations of bias or uncertainty to model human decision-making or the
future outcomes of decisions.
| [
{
"version": "v1",
"created": "Fri, 13 May 2022 07:29:15 GMT"
}
] | 1,652,659,200,000 | [
[
"Fuchs",
"Andrew",
""
],
[
"Passarella",
"Andrea",
""
],
[
"Conti",
"Marco",
""
]
] |
2205.06485 | Andrew Fuchs | Andrew Fuchs and Andrea Passarella and Marco Conti | Modeling Human Behavior Part I -- Learning and Belief Approaches | Part 1 of our review (see Modeling Human Behavior Part II - Cognitive
approaches and Uncertainty) relating to learning and modeling behavior. This
work was partially funded by the following projects. European Union's Horizon
2020 research and innovation programme: HumaneAI-Net (No 952026). CHIST-ERA
program: SAI project (grant CHIST-ERA-19-XAI-010, funded by MUR, grant number
not yet available) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There is a clear desire to model and comprehend human behavior. Trends in
research covering this topic show a clear assumption that many view human
reasoning as the presupposed standard in artificial reasoning. As such, topics
such as game theory, theory of mind, machine learning, etc. all integrate
concepts which are assumed components of human reasoning. These serve as
techniques to attempt to both replicate and understand the behaviors of humans.
In addition, next generation autonomous and adaptive systems will largely
include AI agents and humans working together as teams. To make this possible,
autonomous agents will require the ability to embed practical models of human
behavior, which allow them not only to replicate human models as a technique to
"learn", but to to understand the actions of users and anticipate their
behavior, so as to truly operate in symbiosis with them. The main objective of
this paper it to provide a succinct yet systematic review of the most important
approaches in two areas dealing with quantitative models of human behaviors.
Specifically, we focus on (i) techniques which learn a model or policy of
behavior through exploration and feedback, such as Reinforcement Learning, and
(ii) directly model mechanisms of human reasoning, such as beliefs and bias,
without going necessarily learning via trial-and-error.
| [
{
"version": "v1",
"created": "Fri, 13 May 2022 07:33:49 GMT"
}
] | 1,652,659,200,000 | [
[
"Fuchs",
"Andrew",
""
],
[
"Passarella",
"Andrea",
""
],
[
"Conti",
"Marco",
""
]
] |
2205.06544 | G\"on\"ul Ayc{\i} | Gonul Ayci, Murat Sensoy, Arzucan \"Ozg\"ur, P{\i}nar Yolum | Uncertainty-aware Personal Assistant for Making Personalized Privacy
Decisions | 24 pages, 11 figures, 7 tables | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Many software systems, such as online social networks enable users to share
information about themselves. While the action of sharing is simple, it
requires an elaborate thought process on privacy: what to share, with whom to
share, and for what purposes. Thinking about these for each piece of content to
be shared is tedious. Recent approaches to tackle this problem build personal
assistants that can help users by learning what is private over time and
recommending privacy labels such as private or public to individual content
that a user considers sharing. However, privacy is inherently ambiguous and
highly personal. Existing approaches to recommend privacy decisions do not
address these aspects of privacy sufficiently. Ideally, a personal assistant
should be able to adjust its recommendation based on a given user, considering
that user's privacy understanding. Moreover, the personal assistant should be
able to assess when its recommendation would be uncertain and let the user make
the decision on her own. Accordingly, this paper proposes a personal assistant
that uses evidential deep learning to classify content based on its privacy
label. An important characteristic of the personal assistant is that it can
model its uncertainty in its decisions explicitly, determine that it does not
know the answer, and delegate from making a recommendation when its uncertainty
is high. By factoring in the user's own understanding of privacy, such as risk
factors or own labels, the personal assistant can personalize its
recommendations per user. We evaluate our proposed personal assistant using a
well-known data set. Our results show that our personal assistant can
accurately identify uncertain cases, personalize them to its user's needs, and
thus helps users preserve their privacy well.
| [
{
"version": "v1",
"created": "Fri, 13 May 2022 10:15:04 GMT"
},
{
"version": "v2",
"created": "Wed, 18 May 2022 15:15:26 GMT"
},
{
"version": "v3",
"created": "Fri, 8 Jul 2022 12:35:20 GMT"
},
{
"version": "v4",
"created": "Thu, 28 Jul 2022 11:21:21 GMT"
}
] | 1,659,052,800,000 | [
[
"Ayci",
"Gonul",
""
],
[
"Sensoy",
"Murat",
""
],
[
"Özgür",
"Arzucan",
""
],
[
"Yolum",
"Pınar",
""
]
] |
2205.07635 | Anatol Slissenko | Anatol Slissenko | Relating Information and Proof | 9 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In mathematics information is a number that measures uncertainty (entropy)
based on a probabilistic distribution, often of an obscure origin. In real life
language information is a datum, a statement, more precisely, a formula. But
such a formula should be justified by a proof. I try to formalize this
perception of information. The measure of informativeness of a proof is based
on the set of proofs related to the formulas under consideration. This set of
possible proofs (`a knowledge base') defines a probabilistic measure, and
entropic weight is defined using this measure. The paper is mainly conceptual,
it is not clear where and how this approach can be applied.
| [
{
"version": "v1",
"created": "Thu, 12 May 2022 08:00:42 GMT"
}
] | 1,652,745,600,000 | [
[
"Slissenko",
"Anatol",
""
]
] |
2205.08018 | Udayan Khurana | Udayan Khurana and Kavitha Srinivas and Horst Samulowitz | A Survey on Semantics in Automated Data Science | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Data Scientists leverage common sense reasoning and domain knowledge to
understand and enrich data for building predictive models. In recent years, we
have witnessed a surge in tools and techniques for {\em automated machine
learning}. While data scientists can employ various such tools to help with
model building, many other aspects such as {\em feature engineering} that
require semantic understanding of concepts, remain manual to a large extent. In
this paper we discuss important shortcomings of current automated data science
solutions and machine learning. We discuss how leveraging basic semantic
reasoning on data in combination with novel tools for data science automation
can help with consistent and explainable data augmentation and transformation.
Moreover, semantics can assist data scientists in a new manner by helping with
challenges related to {\em trust}, {\em bias}, and {\em explainability}.
| [
{
"version": "v1",
"created": "Mon, 16 May 2022 23:16:09 GMT"
}
] | 1,652,832,000,000 | [
[
"Khurana",
"Udayan",
""
],
[
"Srinivas",
"Kavitha",
""
],
[
"Samulowitz",
"Horst",
""
]
] |
2205.08683 | Florian Richoux | Florian Richoux | Terrain Analysis in StarCraft 1 and 2 as Combinatorial Optimization | Accepted to IEEE CEC 2022 | null | 10.1109/CEC55065.2022.9870230 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Terrain analysis in Real-Time Strategy games is a necessary step to allow
spacial reasoning. The goal of terrain analysis is to gather and process data
about the map topology and properties to have a qualitative spatial
representation. On StarCraft games, all previous works on terrain analysis
propose a crisp analysis based on connected component detection, Voronoi
diagram computation and pruning, and region merging. Those methods have been
implemented as game-specific libraries, and they can only offer the same kind
of analysis for all maps and all users. In this paper, we propose a way to
consider terrain analysis as a combinatorial optimization problem. Our method
allows different kinds of analysis by changing constraints or the objective
function in the problem model. We also present a library, Taunt, implementing
our method and able to handle both StarCraft 1 and StarCraft 2 maps. This makes
our library a universal tool for StarCraft bots with different spatial
representation needs. We believe our library unlocks the possibility to have
real adaptive AIs playing StarCraft, and can be the starting point of a new
wave of bots.
| [
{
"version": "v1",
"created": "Wed, 18 May 2022 01:34:40 GMT"
}
] | 1,678,320,000,000 | [
[
"Richoux",
"Florian",
""
]
] |
2205.08719 | Bin Yang | Wei Li, Bin Yang, Junsheng Qiao | $(O,G)$-granular variable precision fuzzy rough sets based on overlap
and grouping functions | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Since Bustince et al. introduced the concepts of overlap and grouping
functions, these two types of aggregation functions have attracted a lot of
interest in both theory and applications. In this paper, the depiction of
$(O,G)$-granular variable precision fuzzy rough sets ($(O,G)$-GVPFRSs for
short) is first given based on overlap and grouping functions. Meanwhile, to
work out the approximation operators efficiently, we give another expression of
upper and lower approximation operators by means of fuzzy implications and
co-implications. Furthermore, starting from the perspective of construction
methods, $(O,G)$-GVPFRSs are represented under diverse fuzzy relations.
Finally, some conclusions on the granular variable precision fuzzy rough sets
(GVPFRSs for short) are extended to $(O,G)$-GVPFRSs under some additional
conditions.
| [
{
"version": "v1",
"created": "Wed, 18 May 2022 04:37:15 GMT"
}
] | 1,652,918,400,000 | [
[
"Li",
"Wei",
""
],
[
"Yang",
"Bin",
""
],
[
"Qiao",
"Junsheng",
""
]
] |
2205.08777 | Deepak Chaurasiya | Deepak Chaurasiya, Anil Surisetty, Nitish Kumar, Alok Singh, Vikrant
Dey, Aakarsh Malhotra, Gaurav Dhama and Ankur Arora | Entity Alignment For Knowledge Graphs: Progress, Challenges, and
Empirical Studies | 8 pages, 8 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Entity Alignment (EA) identifies entities across databases that refer to the
same entity. Knowledge graph-based embedding methods have recently dominated EA
techniques. Such methods map entities to a low-dimension space and align them
based on their similarities. With the corpus of EA methodologies growing
rapidly, this paper presents a comprehensive analysis of various existing EA
methods, elaborating their applications and limitations. Further, we
distinguish the methods based on their underlying algorithms and the
information they incorporate to learn entity representations. Based on
challenges in industrial datasets, we bring forward $4$ research questions
(RQs). These RQs empirically analyse the algorithms from the perspective of
\textit{Hubness, Degree distribution, Non-isomorphic neighbourhood,} and
\textit{Name bias}. For Hubness, where one entity turns up as the nearest
neighbour of many other entities, we define an $h$-score to quantify its effect
on the performance of various algorithms. Additionally, we try to level the
playing field for algorithms that rely primarily on name-bias existing in the
benchmarking open-source datasets by creating a low name bias dataset. We
further create an open-source repository for $14$ embedding-based EA methods
and present the analysis for invoking further research motivations in the field
of EA.
| [
{
"version": "v1",
"created": "Wed, 18 May 2022 07:59:03 GMT"
}
] | 1,652,918,400,000 | [
[
"Chaurasiya",
"Deepak",
""
],
[
"Surisetty",
"Anil",
""
],
[
"Kumar",
"Nitish",
""
],
[
"Singh",
"Alok",
""
],
[
"Dey",
"Vikrant",
""
],
[
"Malhotra",
"Aakarsh",
""
],
[
"Dhama",
"Gaurav",
""
],
[
"Arora",
"Ankur",
""
]
] |
2205.09201 | Shufang Zhu | Giuseppe De Giacomo, Dror Fried, Fabio Patrizi, Shufang Zhu | Mimicking Behaviors in Separated Domains | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Devising a strategy to make a system mimicking behaviors from another system
is a problem that naturally arises in many areas of Computer Science. In this
work, we interpret this problem in the context of intelligent agents, from the
perspective of LTLf, a formalism commonly used in AI for expressing
finite-trace properties. Our model consists of two separated dynamic domains,
D_A and D_B, and an LTLf specification that formalizes the notion of mimicking
by mapping properties on behaviors (traces) of D_A into properties on behaviors
of D_B. The goal is to synthesize a strategy that step-by-step maps every
behavior of D_A into a behavior of D_B so that the specification is met. We
consider several forms of mapping specifications, ranging from simple ones to
full LTLf, and for each we study synthesis algorithms and computational
properties.
| [
{
"version": "v1",
"created": "Wed, 18 May 2022 20:19:42 GMT"
}
] | 1,653,004,800,000 | [
[
"De Giacomo",
"Giuseppe",
""
],
[
"Fried",
"Dror",
""
],
[
"Patrizi",
"Fabio",
""
],
[
"Zhu",
"Shufang",
""
]
] |
2205.09362 | Yizheng Hu | Yizheng Hu, Zhihua Zhang | Sparse Adversarial Attack in Multi-agent Reinforcement Learning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Cooperative multi-agent reinforcement learning (cMARL) has many real
applications, but the policy trained by existing cMARL algorithms is not robust
enough when deployed. There exist also many methods about adversarial attacks
on the RL system, which implies that the RL system can suffer from adversarial
attacks, but most of them focused on single agent RL. In this paper, we propose
a \textit{sparse adversarial attack} on cMARL systems. We use (MA)RL with
regularization to train the attack policy. Our experiments show that the policy
trained by the current cMARL algorithm can obtain poor performance when only
one or a few agents in the team (e.g., 1 of 8 or 5 of 25) were attacked at a
few timesteps (e.g., attack 3 of total 40 timesteps).
| [
{
"version": "v1",
"created": "Thu, 19 May 2022 07:46:26 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Aug 2022 10:50:03 GMT"
}
] | 1,660,003,200,000 | [
[
"Hu",
"Yizheng",
""
],
[
"Zhang",
"Zhihua",
""
]
] |
2205.09705 | Yoshinari Motokawa | Yoshinari Motokawa and Toshiharu Sugawara | Distributed Multi-Agent Deep Reinforcement Learning for Robust
Coordination against Noise | Accepted to The 2022 International Joint Conference on Neural
Networks (IJCNN 2022) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In multi-agent systems, noise reduction techniques are important for
improving the overall system reliability as agents are required to rely on
limited environmental information to develop cooperative and coordinated
behaviors with the surrounding agents. However, previous studies have often
applied centralized noise reduction methods to build robust and versatile
coordination in noisy multi-agent environments, while distributed and
decentralized autonomous agents are more plausible for real-world application.
In this paper, we introduce a \emph{distributed attentional actor architecture
model for a multi-agent system} (DA3-X), using which we demonstrate that agents
with DA3-X can selectively learn the noisy environment and behave
cooperatively. We experimentally evaluate the effectiveness of DA3-X by
comparing learning methods with and without DA3-X and show that agents with
DA3-X can achieve better performance than baseline agents. Furthermore, we
visualize heatmaps of \emph{attentional weights} from the DA3-X to analyze how
the decision-making process and coordinated behavior are influenced by noise.
| [
{
"version": "v1",
"created": "Thu, 19 May 2022 17:18:51 GMT"
}
] | 1,653,004,800,000 | [
[
"Motokawa",
"Yoshinari",
""
],
[
"Sugawara",
"Toshiharu",
""
]
] |
2205.09729 | Eric Chalmers | Eric Chalmers and Artur Luczak | Reinforcement Learning with Brain-Inspired Modulation can Improve
Adaptation to Environmental Changes | 9 pages, 5 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Developments in reinforcement learning (RL) have allowed algorithms to
achieve impressive performance in highly complex, but largely static problems.
In contrast, biological learning seems to value efficiency of adaptation to a
constantly-changing world. Here we build on a recently-proposed neuronal
learning rule that assumes each neuron can optimize its energy balance by
predicting its own future activity. That assumption leads to a neuronal
learning rule that uses presynaptic input to modulate prediction error. We
argue that an analogous RL rule would use action probability to modulate reward
prediction error. This modulation makes the agent more sensitive to negative
experiences, and more careful in forming preferences. We embed the proposed
rule in both tabular and deep-Q-network RL algorithms, and find that it
outperforms conventional algorithms in simple, but highly-dynamic tasks. We
suggest that the new rule encapsulates a core principle of biological
intelligence; an important component for allowing algorithms to adapt to change
in a human-like way.
| [
{
"version": "v1",
"created": "Thu, 19 May 2022 17:39:40 GMT"
}
] | 1,653,004,800,000 | [
[
"Chalmers",
"Eric",
""
],
[
"Luczak",
"Artur",
""
]
] |
2205.09738 | Corina Catarau-Cotutiu | Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso | AIGenC: An AI generalisation model via creativity | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Inspired by cognitive theories of creativity, this paper introduces a
computational model (AIGenC) that lays down the necessary components to enable
artificial agents to learn, use and generate transferable representations.
Unlike machine representation learning, which relies exclusively on raw sensory
data, biological representations incorporate relational and associative
information that embeds rich and structured concept spaces. The AIGenC model
poses a hierarchical graph architecture with various levels and types of
representations procured by different components. The first component, Concept
Processing, extracts objects and affordances from sensory input and encodes
them into a concept space. The resulting representations are stored in a dual
memory system and enriched with goal-directed and temporal information acquired
through reinforcement learning, creating a higher-level of abstraction. Two
additional components work in parallel to detect and recover relevant concepts
and create new ones, respectively, in a process akin to cognitive Reflective
Reasoning and Blending. The Reflective Reasoning unit detects and recovers from
memory concepts relevant to the task by means of a matching process that
calculates a similarity value between the current state and memory graph
structures. Once the matching interaction ends, rewards and temporal
information are added to the graph, building further abstractions. If the
reflective reasoning processing fails to offer a suitable solution, a blending
operation comes into place, creating new concepts by combining past
information. We discuss the model's capability to yield better
out-of-distribution generalisation in artificial agents, thus advancing toward
Artificial General Intelligence.
| [
{
"version": "v1",
"created": "Thu, 19 May 2022 17:43:31 GMT"
},
{
"version": "v2",
"created": "Mon, 23 May 2022 13:17:40 GMT"
},
{
"version": "v3",
"created": "Fri, 7 Oct 2022 15:10:22 GMT"
},
{
"version": "v4",
"created": "Mon, 13 Mar 2023 18:46:21 GMT"
},
{
"version": "v5",
"created": "Wed, 21 Jun 2023 00:58:12 GMT"
}
] | 1,687,392,000,000 | [
[
"Catarau-Cotutiu",
"Corina",
""
],
[
"Mondragon",
"Esther",
""
],
[
"Alonso",
"Eduardo",
""
]
] |
2205.10018 | Ze Wang | Guogang Liao, Xuejian Li, Ze Wang, Fan Yang, Muzhi Guan, Bingqi Zhu,
Yongkang Wang, Xingxing Wang, Dong Wang | NMA: Neural Multi-slot Auctions with Externalities for Online
Advertising | 10 pages, 3figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Online advertising driven by auctions brings billions of dollars in revenue
for social networking services and e-commerce platforms. GSP auctions, which
are simple and easy to understand for advertisers, have almost become the
benchmark for ad auction mechanisms in the industry. However, most GSP-based
industrial practices assume that the user click only relies on the ad itself,
which overlook the effect of external items, referred to as externalities.
Recently, DNA has attempted to upgrade GSP with deep neural networks and models
local externalities to some extent. However, it only considers set-level
contexts from auctions and ignores the order and displayed position of ads,
which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG,
WVCG) make it theoretically possible to model global externalities (e.g., the
order and positions of ads and so on), they lack an efficient balance of both
revenue and social welfare. In this paper, we propose novel auction mechanisms
named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned
challenges. Specifically, we model the global externalities effectively with a
context-aware list-wise prediction module to achieve better performance. We
design a list-wise deep rank module to guarantee incentive compatibility in
end-to-end learning. Furthermore, we propose an auxiliary loss for social
welfare to effectively reduce the decline of social welfare while maximizing
revenue. Experiment results on both offline large-scale datasets and online A/B
tests demonstrate that NMA obtains higher revenue with balanced social welfare
than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial
practice, and we have successfully deployed NMA on Meituan food delivery
platform.
| [
{
"version": "v1",
"created": "Fri, 20 May 2022 08:21:59 GMT"
},
{
"version": "v2",
"created": "Mon, 6 Feb 2023 08:48:29 GMT"
},
{
"version": "v3",
"created": "Fri, 8 Sep 2023 08:21:07 GMT"
}
] | 1,694,390,400,000 | [
[
"Liao",
"Guogang",
""
],
[
"Li",
"Xuejian",
""
],
[
"Wang",
"Ze",
""
],
[
"Yang",
"Fan",
""
],
[
"Guan",
"Muzhi",
""
],
[
"Zhu",
"Bingqi",
""
],
[
"Wang",
"Yongkang",
""
],
[
"Wang",
"Xingxing",
""
],
[
"Wang",
"Dong",
""
]
] |
2205.10127 | Anitha K | R. Aruna Devi and K. Anitha | Construction of Rough graph to handle uncertain pattern from an
Information System | 13 pages, 11 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Rough membership function defines the measurement of relationship between
conditional and decision attribute from an Information system. In this paper we
propose a new method to construct rough graph through rough membership function
$\omega_{G}^F(f)$. Rough graph identifies the pattern between the objects with
imprecise and uncertain information. We explore the operations and properties
of rough graph in various stages of its structure.
| [
{
"version": "v1",
"created": "Tue, 17 May 2022 08:41:04 GMT"
}
] | 1,653,264,000,000 | [
[
"Devi",
"R. Aruna",
""
],
[
"Anitha",
"K.",
""
]
] |
2205.10207 | John Lalor | John P. Lalor, Hong Guo | Measuring algorithmic interpretability: A human-learning-based framework
and the corresponding cognitive complexity score | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Algorithmic interpretability is necessary to build trust, ensure fairness,
and track accountability. However, there is no existing formal measurement
method for algorithmic interpretability. In this work, we build upon
programming language theory and cognitive load theory to develop a framework
for measuring algorithmic interpretability. The proposed measurement framework
reflects the process of a human learning an algorithm. We show that the
measurement framework and the resulting cognitive complexity score have the
following desirable properties - universality, computability, uniqueness, and
monotonicity. We illustrate the measurement framework through a toy example,
describe the framework and its conceptual underpinnings, and demonstrate the
benefits of the framework, in particular for managers considering tradeoffs
when selecting algorithms.
| [
{
"version": "v1",
"created": "Fri, 20 May 2022 14:31:06 GMT"
}
] | 1,653,264,000,000 | [
[
"Lalor",
"John P.",
""
],
[
"Guo",
"Hong",
""
]
] |
2205.10513 | Michael Timothy Bennett | Michael Timothy Bennett | Computable Artificial General Intelligence | Experiment code available on TechRxiv:
https://www.techrxiv.org/articles/preprint/Computable_Artificial_General_Intelligence/19740190 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Artificial general intelligence (AGI) may herald our extinction, according to
AI safety research. Yet claims regarding AGI must rely upon mathematical
formalisms -- theoretical agents we may analyse or attempt to build. AIXI
appears to be the only such formalism supported by proof that its behaviour is
optimal, a consequence of its use of compression as a proxy for intelligence.
Unfortunately, AIXI is incomputable and claims regarding its behaviour highly
subjective. We argue that this is because AIXI formalises cognition as taking
place in isolation from the environment in which goals are pursued (Cartesian
dualism). We propose an alternative, supported by proof and experiment, which
overcomes these problems. Integrating research from cognitive science with AI,
we formalise an enactive model of learning and reasoning to address the problem
of subjectivity. This allows us to formulate a different proxy for
intelligence, called weakness, which addresses the problem of incomputability.
We prove optimal behaviour is attained when weakness is maximised. This proof
is supplemented by experimental results comparing weakness and description
length (the closest analogue to compression possible without reintroducing
subjectivity). Weakness outperforms description length, suggesting it is a
better proxy. Furthermore we show that, if cognition is enactive, then
minimisation of description length is neither necessary nor sufficient to
attain optimal performance, undermining the notion that compression is closely
related to intelligence. However, there remain open questions regarding the
implementation of scale-able AGI. In the short term, these results may be best
utilised to improve the performance of existing systems. For example, our
results explain why Deepmind's Apperception Engine is able to generalise
effectively, and how to replicate that performance by maximising weakness.
| [
{
"version": "v1",
"created": "Sat, 21 May 2022 06:32:09 GMT"
},
{
"version": "v2",
"created": "Tue, 24 May 2022 05:54:20 GMT"
},
{
"version": "v3",
"created": "Tue, 31 May 2022 01:31:09 GMT"
},
{
"version": "v4",
"created": "Tue, 2 Aug 2022 03:39:09 GMT"
},
{
"version": "v5",
"created": "Mon, 15 Aug 2022 09:54:26 GMT"
},
{
"version": "v6",
"created": "Wed, 5 Oct 2022 02:03:31 GMT"
},
{
"version": "v7",
"created": "Tue, 22 Nov 2022 01:40:46 GMT"
}
] | 1,669,161,600,000 | [
[
"Bennett",
"Michael Timothy",
""
]
] |
2205.10530 | Xueying Zhang | Xueying Zhang, Kai Shen, Chi Zhang, Xiaochuan Fan, Yun Xiao, Zhen He,
Bo Long, Lingfei Wu | Scenario-based Multi-product Advertising Copywriting Generation for
E-Commerce | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this paper, we proposed an automatic Scenario-based Multi-product
Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has
been deployed on a leading Chinese e-commerce platform. The proposed SMPACG
consists of two main components: 1) an automatic multi-product combination
selection module, which itself is consisted of a topic prediction model, a
pattern and attribute-based selection model and an arbitrator model; and 2) an
automatic multi-product advertising copywriting generation module, which
combines our proposed domain-specific pretrained language model and
knowledge-based data enhancement model. The SMPACG is the first system that
realizes automatic scenario-based multi-product advertising contents
generation, which achieves significant improvements over other state-of-the-art
methods. The SMPACG has been not only developed for directly serving for our
e-commerce recommendation system, but also used as a real-time writing
assistant tool for merchants.
| [
{
"version": "v1",
"created": "Sat, 21 May 2022 07:45:53 GMT"
}
] | 1,653,350,400,000 | [
[
"Zhang",
"Xueying",
""
],
[
"Shen",
"Kai",
""
],
[
"Zhang",
"Chi",
""
],
[
"Fan",
"Xiaochuan",
""
],
[
"Xiao",
"Yun",
""
],
[
"He",
"Zhen",
""
],
[
"Long",
"Bo",
""
],
[
"Wu",
"Lingfei",
""
]
] |
2205.10575 | Vinh Nguyen | Vinh Nguyen, Olivier Bodenreider | UVA Resources for the Biomedical Vocabulary Alignment at Scale in the
UMLS Metathesaurus | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The construction and maintenance process of the UMLS (Unified Medical
Language System) Metathesaurus is time-consuming, costly, and error-prone as it
relies on (1) the lexical and semantic processing for suggesting synonymous
terms, and (2) the expertise of UMLS editors for curating the suggestions. For
improving the UMLS Metathesaurus construction process, our research group has
defined a new task called UVA (UMLS Vocabulary Alignment) and generated a
dataset for evaluating the task. Our group has also developed different
baselines for this task using logical rules (RBA), and neural networks (LexLM
and ConLM).
In this paper, we present a set of reusable and reproducible resources
including (1) a dataset generator, (2) three datasets generated by using the
generator, and (3) three baseline approaches. We describe the UVA dataset
generator and its implementation generalized for any given UMLS release. We
demonstrate the use of the dataset generator by generating datasets
corresponding to three UMLS releases, 2020AA, 2021AA, and 2021AB. We provide
three UVA baselines using the three existing approaches (LexLM, ConLM, and
RBA). The code, the datasets, and the experiments are publicly available,
reusable, and reproducible with any UMLS release (a no-cost license agreement
is required for downloading the UMLS).
| [
{
"version": "v1",
"created": "Sat, 21 May 2022 12:00:53 GMT"
}
] | 1,653,350,400,000 | [
[
"Nguyen",
"Vinh",
""
],
[
"Bodenreider",
"Olivier",
""
]
] |
2205.10607 | Dianbo Liu Dr | Dianbo Liu, Vedant Shah, Oussama Boussif, Cristian Meo, Anirudh Goyal,
Tianmin Shu, Michael Mozer, Nicolas Heess, Yoshua Bengio | Coordinating Policies Among Multiple Agents via an Intelligent
Communication Channel | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In Multi-Agent Reinforcement Learning (MARL), specialized channels are often
introduced that allow agents to communicate directly with one another. In this
paper, we propose an alternative approach whereby agents communicate through an
intelligent facilitator that learns to sift through and interpret signals
provided by all agents to improve the agents' collective performance. To ensure
that this facilitator does not become a centralized controller, agents are
incentivized to reduce their dependence on the messages it conveys, and the
messages can only influence the selection of a policy from a fixed set, not
instantaneous actions given the policy. We demonstrate the strength of this
architecture over existing baselines on several cooperative MARL environments.
| [
{
"version": "v1",
"created": "Sat, 21 May 2022 14:11:33 GMT"
},
{
"version": "v2",
"created": "Wed, 25 May 2022 16:11:52 GMT"
}
] | 1,653,523,200,000 | [
[
"Liu",
"Dianbo",
""
],
[
"Shah",
"Vedant",
""
],
[
"Boussif",
"Oussama",
""
],
[
"Meo",
"Cristian",
""
],
[
"Goyal",
"Anirudh",
""
],
[
"Shu",
"Tianmin",
""
],
[
"Mozer",
"Michael",
""
],
[
"Heess",
"Nicolas",
""
],
[
"Bengio",
"Yoshua",
""
]
] |
2205.10893 | Albert Qiaochu Jiang | Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski,
Tomasz Odrzyg\'o\'zd\'z, Piotr Mi{\l}o\'s, Yuhuai Wu, Mateja Jamnik | Thor: Wielding Hammers to Integrate Language Models and Automated
Theorem Provers | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In theorem proving, the task of selecting useful premises from a large
library to unlock the proof of a given conjecture is crucially important. This
presents a challenge for all theorem provers, especially the ones based on
language models, due to their relative inability to reason over huge volumes of
premises in text form. This paper introduces Thor, a framework integrating
language models and automated theorem provers to overcome this difficulty. In
Thor, a class of methods called hammers that leverage the power of automated
theorem provers are used for premise selection, while all other tasks are
designated to language models. Thor increases a language model's success rate
on the PISA dataset from $39\%$ to $57\%$, while solving $8.2\%$ of problems
neither language models nor automated theorem provers are able to solve on
their own. Furthermore, with a significantly smaller computational budget, Thor
can achieve a success rate on the MiniF2F dataset that is on par with the best
existing methods. Thor can be instantiated for the majority of popular
interactive theorem provers via a straightforward protocol we provide.
| [
{
"version": "v1",
"created": "Sun, 22 May 2022 18:03:03 GMT"
}
] | 1,653,350,400,000 | [
[
"Jiang",
"Albert Q.",
""
],
[
"Li",
"Wenda",
""
],
[
"Tworkowski",
"Szymon",
""
],
[
"Czechowski",
"Konrad",
""
],
[
"Odrzygóźdź",
"Tomasz",
""
],
[
"Miłoś",
"Piotr",
""
],
[
"Wu",
"Yuhuai",
""
],
[
"Jamnik",
"Mateja",
""
]
] |
2205.10990 | Lei Zhang | Lei Zhang, Yu Pan, Yi Liu, Qibin Zheng and Zhisong Pan | Multiple Domain Cyberspace Attack and Defense Game Based on Reward
Randomization Reinforcement Learning | 10 pages,4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The existing network attack and defense method can be regarded as game, but
most of the game only involves network domain, not multiple domain cyberspace.
To address this challenge, this paper proposed a multiple domain cyberspace
attack and defense game model based on reinforcement learning. We define the
multiple domain cyberspace include physical domain, network domain and digital
domain. By establishing two agents, representing the attacker and the defender
respectively, defender will select the multiple domain actions in the multiple
domain cyberspace to obtain defender's optimal reward by reinforcement
learning. In order to improve the defense ability of defender, a game model
based on reward randomization reinforcement learning is proposed. When the
defender takes the multiple domain defense action, the reward is randomly given
and subject to linear distribution, so as to find the better defense policy and
improve defense success rate. The experimental results show that the game model
can effectively simulate the attack and defense state of multiple domain
cyberspace, and the proposed method has a higher defense success rate than DDPG
and DQN.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 01:38:23 GMT"
}
] | 1,653,350,400,000 | [
[
"Zhang",
"Lei",
""
],
[
"Pan",
"Yu",
""
],
[
"Liu",
"Yi",
""
],
[
"Zheng",
"Qibin",
""
],
[
"Pan",
"Zhisong",
""
]
] |
2205.11005 | Yuchao Li | Yuchao Li, Fuli Luo, Chuanqi Tan, Mengdi Wang, Songfang Huang, Shen
Li, Junjie Bai | Parameter-Efficient Sparsity for Large Language Models Fine-Tuning | This paper is published in IJCAI 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | With the dramatically increased number of parameters in language models,
sparsity methods have received ever-increasing research focus to compress and
accelerate the models. While most research focuses on how to accurately retain
appropriate weights while maintaining the performance of the compressed model,
there are challenges in the computational overhead and memory footprint of
sparse training when compressing large-scale language models. To address this
problem, we propose a Parameter-efficient Sparse Training (PST) method to
reduce the number of trainable parameters during sparse-aware training in
downstream tasks. Specifically, we first combine the data-free and data-driven
criteria to efficiently and accurately measure the importance of weights. Then
we investigate the intrinsic redundancy of data-driven weight importance and
derive two obvious characteristics i.e., low-rankness and structuredness. Based
on that, two groups of small matrices are introduced to compute the data-driven
importance of weights, instead of using the original large importance score
matrix, which therefore makes the sparse training resource-efficient and
parameter-efficient. Experiments with diverse networks (i.e., BERT, RoBERTa and
GPT-2) on dozens of datasets demonstrate PST performs on par or better than
previous sparsity methods, despite only training a small number of parameters.
For instance, compared with previous sparsity methods, our PST only requires
1.5% trainable parameters to achieve comparable performance on BERT.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 02:43:45 GMT"
}
] | 1,653,350,400,000 | [
[
"Li",
"Yuchao",
""
],
[
"Luo",
"Fuli",
""
],
[
"Tan",
"Chuanqi",
""
],
[
"Wang",
"Mengdi",
""
],
[
"Huang",
"Songfang",
""
],
[
"Li",
"Shen",
""
],
[
"Bai",
"Junjie",
""
]
] |
2205.11158 | Jie Zhang | Jie Zhang, Chen Chen, Lingjuan Lyu | IDEAL: Query-Efficient Data-Free Learning from Black-box Models | ICLR 2023 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Knowledge Distillation (KD) is a typical method for training a lightweight
student model with the help of a well-trained teacher model. However, most KD
methods require access to either the teacher's training data or model
parameters, which is unrealistic. To tackle this problem, recent works study KD
under data-free and black-box settings. Nevertheless, these works require a
large number of queries to the teacher model, which incurs significant monetary
and computational costs. To address these problems, we propose a novel method
called \emph{query-effIcient Data-free lEarning from blAck-box modeLs} (IDEAL),
which aims to query-efficiently learn from black-box model APIs to train a good
student without any real data. In detail, IDEAL trains the student model in two
stages: data generation and model distillation. Note that IDEAL does not
require any query in the data generation stage and queries the teacher only
once for each sample in the distillation stage. Extensive experiments on
various real-world datasets show the effectiveness of the proposed IDEAL. For
instance, IDEAL can improve the performance of the best baseline method DFME by
5.83% on CIFAR10 dataset with only 0.02x the query budget of DFME.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 09:48:26 GMT"
},
{
"version": "v2",
"created": "Thu, 17 Aug 2023 08:21:07 GMT"
}
] | 1,692,576,000,000 | [
[
"Zhang",
"Jie",
""
],
[
"Chen",
"Chen",
""
],
[
"Lyu",
"Lingjuan",
""
]
] |
2205.11173 | Feng Li | Feng Li, Wen Jun, Tan and Wentong, Cai | Multi-objective Optimization of Clustering-based Scheduling for
Multi-workflow On Clouds Considering Fairness | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Distributed computing, such as cloud computing, provides promising platforms
to execute multiple workflows. Workflow scheduling plays an important role in
multi-workflow execution with multi-objective requirements. Although there
exist many multi-objective scheduling algorithms, they focus mainly on
optimizing makespan and cost for a single workflow. There is a limited research
on multi-objective optimization for multi-workflow scheduling. Considering
multi-workflow scheduling, there is an additional key objective to maintain the
fairness of workflows using the resources. To address such issues, this paper
first defines a new multi-objective optimization model based on makespan, cost,
and fairness, and then proposes a global clustering-based multi-workflow
scheduling strategy for resource allocation. Experimental results show that the
proposed approach performs better than the compared algorithms without
significant compromise of the overall makespan and cost as well as individual
fairness, which can guide the simulation workflow scheduling on clouds.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 10:25:16 GMT"
}
] | 1,653,350,400,000 | [
[
"Li",
"Feng",
""
],
[
"Jun",
"Wen",
""
],
[
"Tan",
"",
""
],
[
"Wentong",
"",
""
],
[
"Cai",
"",
""
]
] |
2205.11215 | Adam Karwan | Jonathan DeGange, Swapnil Gupta, Zhuoyu Han, Krzysztof Wilkosz, Adam
Karwan | Document Intelligence Metrics for Visually Rich Document Evaluation | Accepted to DAS 2022, 15TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT
ANALYSIS SYSTEMS | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The processing of Visually-Rich Documents (VRDs) is highly important in
information extraction tasks associated with Document Intelligence. We
introduce DI-Metrics, a Python library devoted to VRD model evaluation
comprising text-based, geometric-based and hierarchical metrics for information
extraction tasks. We apply DI-Metrics to evaluate information extraction
performance using publicly available CORD dataset, comparing performance of
three SOTA models and one industry model. The open-source library is available
on GitHub.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 11:55:05 GMT"
}
] | 1,653,350,400,000 | [
[
"DeGange",
"Jonathan",
""
],
[
"Gupta",
"Swapnil",
""
],
[
"Han",
"Zhuoyu",
""
],
[
"Wilkosz",
"Krzysztof",
""
],
[
"Karwan",
"Adam",
""
]
] |
2205.11234 | Ghadi S. AlHajj | Ghadi S. Al Hajj, Johan Pensar, Geir Kjetil Sandve | DagSim: Combining DAG-based model structure with unconstrained data
types and relations for flexible, transparent, and modularized data
simulation | 12 pages, 1 figure, 1 table | null | 10.1371/journal.pone.0284443 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Data simulation is fundamental for machine learning and causal inference, as
it allows exploration of scenarios and assessment of methods in settings with
full control of ground truth. Directed acyclic graphs (DAGs) are well
established for encoding the dependence structure over a collection of
variables in both inference and simulation settings. However, while modern
machine learning is applied to data of an increasingly complex nature,
DAG-based simulation frameworks are still confined to settings with relatively
simple variable types and functional forms. We here present DagSim, a
Python-based framework for DAG-based data simulation without any constraints on
variable types or functional relations. A succinct YAML format for defining the
simulation model structure promotes transparency, while separate user-provided
functions for generating each variable based on its parents ensure simulation
code modularization. We illustrate the capabilities of DagSim through use cases
where metadata variables control shapes in an image and patterns in
bio-sequences.
| [
{
"version": "v1",
"created": "Fri, 6 May 2022 17:43:27 GMT"
},
{
"version": "v2",
"created": "Fri, 30 Sep 2022 14:55:51 GMT"
}
] | 1,683,676,800,000 | [
[
"Hajj",
"Ghadi S. Al",
""
],
[
"Pensar",
"Johan",
""
],
[
"Sandve",
"Geir Kjetil",
""
]
] |
2205.11291 | Bor Shiun Wang | Chi-Chun Chao, Jun-Wei Hsieh, Bor-Shiun Wang | Cooperative Reinforcement Learning on Traffic Signal Control | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Traffic signal control is a challenging real-world problem aiming to minimize
overall travel time by coordinating vehicle movements at road intersections.
Existing traffic signal control systems in use still rely heavily on
oversimplified information and rule-based methods. Specifically, the
periodicity of green/red light alternations can be considered as a prior for
better planning of each agent in policy optimization. To better learn such
adaptive and predictive priors, traditional
RL-based methods can only return a fixed length from predefined action pool
with only local agents. If there is no cooperation between these agents, some
agents often make conflicts to other agents and thus decrease the whole
throughput. This paper proposes a cooperative, multi-objective architecture
with age-decaying weights to better estimate multiple reward terms for traffic
signal control optimization, which termed COoperative Multi-Objective
Multi-Agent Deep Deterministic Policy Gradient (COMMA-DDPG). Two types of
agents running to maximize rewards of different goals - one for local traffic
optimization at each intersection and the other for global traffic waiting time
optimization. The global agent is used to guide the local agents as a means for
aiding faster learning but not used in the inference phase. We also provide an
analysis of solution existence together with convergence proof for the proposed
RL optimization. Evaluation is performed using real-world traffic data
collected using traffic cameras from an Asian country. Our method can
effectively reduce the total delayed time by 60\%. Results demonstrate its
superiority when compared to SoTA methods.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 13:25:15 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Aug 2022 13:39:09 GMT"
}
] | 1,660,003,200,000 | [
[
"Chao",
"Chi-Chun",
""
],
[
"Hsieh",
"Jun-Wei",
""
],
[
"Wang",
"Bor-Shiun",
""
]
] |
2205.11367 | Nabeel Mohammed | Md Sazzad Hossain, Pritom Saha, Townim Faisal Chowdhury, Shafin
Rahman, Fuad Rahman, Nabeel Mohammed | Rethinking Task-Incremental Learning Baselines | Accepted in ICPR2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | It is common to have continuous streams of new data that need to be
introduced in the system in real-world applications. The model needs to learn
newly added capabilities (future tasks) while retaining the old knowledge (past
tasks). Incremental learning has recently become increasingly appealing for
this problem. Task-incremental learning is a kind of incremental learning where
task identity of newly included task (a set of classes) remains known during
inference. A common goal of task-incremental methods is to design a network
that can operate on minimal size, maintaining decent performance. To manage the
stability-plasticity dilemma, different methods utilize replay memory of past
tasks, specialized hardware, regularization monitoring etc. However, these
methods are still less memory efficient in terms of architecture growth or
input data costs. In this study, we present a simple yet effective adjustment
network (SAN) for task incremental learning that achieves near state-of-the-art
performance while using minimal architectural size without using memory
instances compared to previous state-of-the-art approaches. We investigate this
approach on both 3D point cloud object (ModelNet40) and 2D image (CIFAR10,
CIFAR100, MiniImageNet, MNIST, PermutedMNIST, notMNIST, SVHN, and FashionMNIST)
recognition tasks and establish a strong baseline result for a fair comparison
with existing methods. On both 2D and 3D domains, we also observe that SAN is
primarily unaffected by different task orders in a task-incremental setting.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 14:52:38 GMT"
}
] | 1,653,350,400,000 | [
[
"Hossain",
"Md Sazzad",
""
],
[
"Saha",
"Pritom",
""
],
[
"Chowdhury",
"Townim Faisal",
""
],
[
"Rahman",
"Shafin",
""
],
[
"Rahman",
"Fuad",
""
],
[
"Mohammed",
"Nabeel",
""
]
] |
2205.11558 | Sreejan Kumar | Sreejan Kumar, Carlos G. Correa, Ishita Dasgupta, Raja Marjieh,
Michael Y. Hu, Robert D. Hawkins, Nathaniel D. Daw, Jonathan D. Cohen,
Karthik Narasimhan, Thomas L. Griffiths | Using Natural Language and Program Abstractions to Instill Human
Inductive Biases in Machines | In Proceedings of the 36th Conference on Neural Information
Processing Systems (NeurIPS 2022), winner of Outstanding Paper Award | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Strong inductive biases give humans the ability to quickly learn to perform a
variety of tasks. Although meta-learning is a method to endow neural networks
with useful inductive biases, agents trained by meta-learning may sometimes
acquire very different strategies from humans. We show that co-training these
agents on predicting representations from natural language task descriptions
and programs induced to generate such tasks guides them toward more human-like
inductive biases. Human-generated language descriptions and program induction
models that add new learned primitives both contain abstract concepts that can
compress description length. Co-training on these representations result in
more human-like behavior in downstream meta-reinforcement learning agents than
less abstract controls (synthetic language descriptions, program induction
without learned primitives), suggesting that the abstraction supported by these
representations is key.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 18:17:58 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Oct 2022 12:32:49 GMT"
},
{
"version": "v3",
"created": "Sun, 5 Feb 2023 18:44:46 GMT"
}
] | 1,675,728,000,000 | [
[
"Kumar",
"Sreejan",
""
],
[
"Correa",
"Carlos G.",
""
],
[
"Dasgupta",
"Ishita",
""
],
[
"Marjieh",
"Raja",
""
],
[
"Hu",
"Michael Y.",
""
],
[
"Hawkins",
"Robert D.",
""
],
[
"Daw",
"Nathaniel D.",
""
],
[
"Cohen",
"Jonathan D.",
""
],
[
"Narasimhan",
"Karthik",
""
],
[
"Griffiths",
"Thomas L.",
""
]
] |
2205.11589 | Antonio Rago | Antonio Rago, Pietro Baroni and Francesca Toni | Explaining Causal Models with Argumentation: the Case of Bi-variate
Reinforcement | 6 pages, 1 figure (to appear at KR 2022) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Causal models are playing an increasingly important role in machine learning,
particularly in the realm of explainable AI. We introduce a conceptualisation
for generating argumentation frameworks (AFs) from causal models for the
purpose of forging explanations for the models' outputs. The conceptualisation
is based on reinterpreting desirable properties of semantics of AFs as
explanation moulds, which are means for characterising the relations in the
causal model argumentatively. We demonstrate our methodology by reinterpreting
the property of bi-variate reinforcement as an explanation mould to forge
bipolar AFs as explanations for the outputs of causal models. We perform a
theoretical evaluation of these argumentative explanations, examining whether
they satisfy a range of desirable explanatory and argumentative properties.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 19:39:51 GMT"
}
] | 1,653,436,800,000 | [
[
"Rago",
"Antonio",
""
],
[
"Baroni",
"Pietro",
""
],
[
"Toni",
"Francesca",
""
]
] |
2205.11590 | Antonio Rago | Benjamin Irwin, Antonio Rago and Francesca Toni | Forecasting Argumentation Frameworks | 9 pages, 2 figures (to appear at KR 2022) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We introduce Forecasting Argumentation Frameworks (FAFs), a novel
argumentation-based methodology for forecasting informed by recent judgmental
forecasting research. FAFs comprise update frameworks which empower (human or
artificial) agents to argue over time about the probability of outcomes, e.g.
the winner of a political election or a fluctuation in inflation rates, whilst
flagging perceived irrationality in the agents' behaviour with a view to
improving their forecasting accuracy. FAFs include five argument types,
amounting to standard pro/con arguments, as in bipolar argumentation, as well
as novel proposal arguments and increase/decrease amendment arguments. We adapt
an existing gradual semantics for bipolar argumentation to determine the
aggregated dialectical strength of proposal arguments and define irrational
behaviour. We then give a simple aggregation function which produces a final
group forecast from rational agents' individual forecasts. We identify and
study properties of FAFs and conduct an empirical evaluation which signals
FAFs' potential to increase the forecasting accuracy of participants.
| [
{
"version": "v1",
"created": "Mon, 23 May 2022 19:41:31 GMT"
}
] | 1,653,436,800,000 | [
[
"Irwin",
"Benjamin",
""
],
[
"Rago",
"Antonio",
""
],
[
"Toni",
"Francesca",
""
]
] |
2205.11898 | Zhenhe Cui | Zhenhe Cui, Weidu Kuang, Yongmei Liu | Automatic Verification of Sound Abstractions for Generalized Planning | 11 pages | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Generalized planning studies the computation of general solutions for a set
of planning problems. Computing general solutions with correctness guarantee
has long been a key issue in generalized planning. Abstractions are widely used
to solve generalized planning problems. Solutions of sound abstractions are
those with correctness guarantees for generalized planning problems. Recently,
Cui et al. proposed a uniform abstraction framework for generalized planning.
They gave the model-theoretic definitions of sound and complete abstractions
for generalized planning problems. In this paper, based on Cui et al.'s work,
we explore automatic verification of sound abstractions for generalized
planning. We firstly present the proof-theoretic characterization for sound
abstraction. Then, based on the characterization, we give a sufficient
condition for sound abstractions which is first-order verifiable. To implement
it, we exploit regression extensions, and develop methods to handle counting
and transitive closure. Finally, we implement a sound abstraction verification
system and report experimental results on several domains.
| [
{
"version": "v1",
"created": "Tue, 24 May 2022 08:48:30 GMT"
}
] | 1,653,436,800,000 | [
[
"Cui",
"Zhenhe",
""
],
[
"Kuang",
"Weidu",
""
],
[
"Liu",
"Yongmei",
""
]
] |
2205.11973 | Huiling Song | Yuan Wang and Huiling Song and Peng Huo and Tao Xu and Jucheng Yang
and Yarui Chen and Tingting Zhao | Exploiting Dynamic and Fine-grained Semantic Scope for Extreme
Multi-label Text Classification | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Extreme multi-label text classification (XMTC) refers to the problem of
tagging a given text with the most relevant subset of labels from a large label
set. A majority of labels only have a few training instances due to large label
dimensionality in XMTC. To solve this data sparsity issue, most existing XMTC
methods take advantage of fixed label clusters obtained in early stage to
balance performance on tail labels and head labels. However, such label
clusters provide static and coarse-grained semantic scope for every text, which
ignores distinct characteristics of different texts and has difficulties
modelling accurate semantics scope for texts with tail labels. In this paper,
we propose a novel framework TReaderXML for XMTC, which adopts dynamic and
fine-grained semantic scope from teacher knowledge for individual text to
optimize text conditional prior category semantic ranges. TReaderXML
dynamically obtains teacher knowledge for each text by similar texts and
hierarchical label information in training sets to release the ability of
distinctly fine-grained label-oriented semantic scope. Then, TReaderXML
benefits from a novel dual cooperative network that firstly learns features of
a text and its corresponding label-oriented semantic scope by parallel Encoding
Module and Reading Module, secondly embeds two parts by Interaction Module to
regularize the text's representation by dynamic and fine-grained label-oriented
semantic scope, and finally find target labels by Prediction Module.
Experimental results on three XMTC benchmark datasets show that our method
achieves new state-of-the-art results and especially performs well for severely
imbalanced and sparse datasets.
| [
{
"version": "v1",
"created": "Tue, 24 May 2022 11:15:35 GMT"
}
] | 1,653,436,800,000 | [
[
"Wang",
"Yuan",
""
],
[
"Song",
"Huiling",
""
],
[
"Huo",
"Peng",
""
],
[
"Xu",
"Tao",
""
],
[
"Yang",
"Jucheng",
""
],
[
"Chen",
"Yarui",
""
],
[
"Zhao",
"Tingting",
""
]
] |
2205.12159 | Glen Smith Jr | Glen Smith, Qiao Zhang, Christopher MacLellan | Do it Like the Doctor: How We Can Design a Model That Uses Domain
Knowledge to Diagnose Pneumothorax | 15 pages, Presented at AAAI Spring Symposium on Machine Learning and
Knowledge Engineering 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Computer-aided diagnosis for medical imaging is a well-studied field that
aims to provide real-time decision support systems for physicians. These
systems attempt to detect and diagnose a plethora of medical conditions across
a variety of image diagnostic technologies including ultrasound, x-ray, MRI,
and CT. When designing AI models for these systems, we are often limited by
little training data, and for rare medical conditions, positive examples are
difficult to obtain. These issues often cause models to perform poorly, so we
needed a way to design an AI model in light of these limitations. Thus, our
approach was to incorporate expert domain knowledge into the design of an AI
model. We conducted two qualitative think-aloud studies with doctors trained in
the interpretation of lung ultrasound diagnosis to extract relevant domain
knowledge for the condition Pneumothorax. We extracted knowledge of key
features and procedures used to make a diagnosis. With this knowledge, we
employed knowledge engineering concepts to make recommendations for an AI model
design to automatically diagnose Pneumothorax.
| [
{
"version": "v1",
"created": "Tue, 24 May 2022 15:42:43 GMT"
}
] | 1,653,436,800,000 | [
[
"Smith",
"Glen",
""
],
[
"Zhang",
"Qiao",
""
],
[
"MacLellan",
"Christopher",
""
]
] |
2205.12179 | Jiaqian Ren | Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, Philip S. Yu | Evidential Temporal-aware Graph-based Social Event Detection via
Dempster-Shafer Theory | Accepted by ICWS2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | The rising popularity of online social network services has attracted lots of
research on mining social media data, especially on mining social events.
Social event detection, due to its wide applications, has now become a trivial
task. State-of-the-art approaches exploiting Graph Neural Networks (GNNs)
usually follow a two-step strategy: 1) constructing text graphs based on
various views (\textit{co-user}, \textit{co-entities} and
\textit{co-hashtags}); and 2) learning a unified text representation by a
specific GNN model. Generally, the results heavily rely on the quality of the
constructed graphs and the specific message passing scheme. However, existing
methods have deficiencies in both aspects: 1) They fail to recognize the noisy
information induced by unreliable views. 2) Temporal information which works as
a vital indicator of events is neglected in most works. To this end, we propose
ETGNN, a novel Evidential Temporal-aware Graph Neural Network. Specifically, we
construct view-specific graphs whose nodes are the texts and edges are
determined by several types of shared elements respectively. To incorporate
temporal information into the message passing scheme, we introduce a novel
temporal-aware aggregator which assigns weights to neighbours according to an
adaptive time exponential decay formula. Considering the view-specific
uncertainty, the representations of all views are converted into mass functions
through evidential deep learning (EDL) neural networks, and further combined
via Dempster-Shafer theory (DST) to make the final detection. Experimental
results on three real-world datasets demonstrate the effectiveness of ETGNN in
accuracy, reliability and robustness in social event detection.
| [
{
"version": "v1",
"created": "Tue, 24 May 2022 16:22:40 GMT"
}
] | 1,653,436,800,000 | [
[
"Ren",
"Jiaqian",
""
],
[
"Jiang",
"Lei",
""
],
[
"Peng",
"Hao",
""
],
[
"Liu",
"Zhiwei",
""
],
[
"Wu",
"Jia",
""
],
[
"Yu",
"Philip S.",
""
]
] |
2205.12735 | Daniel Cunnington | Daniel Cunnington, Mark Law, Jorge Lobo, Alessandra Russo | Neuro-Symbolic Learning of Answer Set Programs from Raw Data | Accepted to IJCAI 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | One of the ultimate goals of Artificial Intelligence is to assist humans in
complex decision making. A promising direction for achieving this goal is
Neuro-Symbolic AI, which aims to combine the interpretability of symbolic
techniques with the ability of deep learning to learn from raw data. However,
most current approaches require manually engineered symbolic knowledge, and
where end-to-end training is considered, such approaches are either restricted
to learning definite programs, or are restricted to training binary neural
networks. In this paper, we introduce Neuro-Symbolic Inductive Learner (NSIL),
an approach that trains a general neural network to extract latent concepts
from raw data, whilst learning symbolic knowledge that maps latent concepts to
target labels. The novelty of our approach is a method for biasing the learning
of symbolic knowledge, based on the in-training performance of both neural and
symbolic components. We evaluate NSIL on three problem domains of different
complexity, including an NP-complete problem. Our results demonstrate that NSIL
learns expressive knowledge, solves computationally complex problems, and
achieves state-of-the-art performance in terms of accuracy and data efficiency.
Code and technical appendix: https://github.com/DanCunnington/NSIL
| [
{
"version": "v1",
"created": "Wed, 25 May 2022 12:41:59 GMT"
},
{
"version": "v2",
"created": "Wed, 10 Aug 2022 10:00:17 GMT"
},
{
"version": "v3",
"created": "Thu, 22 Sep 2022 15:46:17 GMT"
},
{
"version": "v4",
"created": "Sat, 19 Nov 2022 14:44:48 GMT"
},
{
"version": "v5",
"created": "Wed, 4 Jan 2023 09:45:26 GMT"
},
{
"version": "v6",
"created": "Fri, 20 Jan 2023 16:24:40 GMT"
},
{
"version": "v7",
"created": "Tue, 6 Jun 2023 12:21:23 GMT"
},
{
"version": "v8",
"created": "Fri, 2 Feb 2024 20:25:48 GMT"
}
] | 1,707,177,600,000 | [
[
"Cunnington",
"Daniel",
""
],
[
"Law",
"Mark",
""
],
[
"Lobo",
"Jorge",
""
],
[
"Russo",
"Alessandra",
""
]
] |
2205.13646 | Rushit Dave | Nyle Siddiqui, Rushit Dave, Naeem Seliya, Mounika Vanamala | Machine and Deep Learning Applications to Mouse Dynamics for Continuous
User Authentication | null | Mach. Learn. Knowl. Extr. 2022 | 10.3390/make4020023 | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Static authentication methods, like passwords, grow increasingly weak with
advancements in technology and attack strategies. Continuous authentication has
been proposed as a solution, in which users who have gained access to an
account are still monitored in order to continuously verify that the user is
not an imposter who had access to the user credentials. Mouse dynamics is the
behavior of a users mouse movements and is a biometric that has shown great
promise for continuous authentication schemes. This article builds upon our
previous published work by evaluating our dataset of 40 users using three
machine learning and deep learning algorithms. Two evaluation scenarios are
considered: binary classifiers are used for user authentication, with the top
performer being a 1-dimensional convolutional neural network with a peak
average test accuracy of 85.73% across the top 10 users. Multi class
classification is also examined using an artificial neural network which
reaches an astounding peak accuracy of 92.48% the highest accuracy we have seen
for any classifier on this dataset.
| [
{
"version": "v1",
"created": "Thu, 26 May 2022 21:43:59 GMT"
}
] | 1,653,868,800,000 | [
[
"Siddiqui",
"Nyle",
""
],
[
"Dave",
"Rushit",
""
],
[
"Seliya",
"Naeem",
""
],
[
"Vanamala",
"Mounika",
""
]
] |
2205.13728 | Zhiming Li | Yushi Cao, Zhiming Li, Tianpei Yang, Hao Zhang, Yan Zheng, Yi Li,
Jianye Hao, Yang Liu | GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic
Synthesis | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Despite achieving superior performance in human-level control problems,
unlike humans, deep reinforcement learning (DRL) lacks high-order intelligence
(e.g., logic deduction and reuse), thus it behaves ineffectively than humans
regarding learning and generalization in complex problems. Previous works
attempt to directly synthesize a white-box logic program as the DRL policy,
manifesting logic-driven behaviors. However, most synthesis methods are built
on imperative or declarative programming, and each has a distinct limitation,
respectively. The former ignores the cause-effect logic during synthesis,
resulting in low generalizability across tasks. The latter is strictly
proof-based, thus failing to synthesize programs with complex hierarchical
logic. In this paper, we combine the above two paradigms together and propose a
novel Generalizable Logic Synthesis (GALOIS) framework to synthesize
hierarchical and strict cause-effect logic programs. GALOIS leverages the
program sketch and defines a new sketch-based hybrid program language for
guiding the synthesis. Based on that, GALOIS proposes a sketch-based program
synthesis method to automatically generate white-box programs with
generalizable and interpretable cause-effect logic. Extensive evaluations on
various decision-making tasks with complex logic demonstrate the superiority of
GALOIS over mainstream baselines regarding the asymptotic performance,
generalizability, and great knowledge reusability across different
environments.
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 02:50:13 GMT"
}
] | 1,653,868,800,000 | [
[
"Cao",
"Yushi",
""
],
[
"Li",
"Zhiming",
""
],
[
"Yang",
"Tianpei",
""
],
[
"Zhang",
"Hao",
""
],
[
"Zheng",
"Yan",
""
],
[
"Li",
"Yi",
""
],
[
"Hao",
"Jianye",
""
],
[
"Liu",
"Yang",
""
]
] |
2205.13745 | Min Li | Min Li, Zhengyuan Shi, Qiuxia Lai, Sadaf Khan, Shaowei Cai, Qiang Xu | DeepSAT: An EDA-Driven Learning Framework for SAT | 7 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We present DeepSAT, a novel end-to-end learning framework for the Boolean
satisfiability (SAT) problem. Unlike existing solutions trained on random SAT
instances with relatively weak supervision, we propose applying the knowledge
of the well-developed electronic design automation (EDA) field for SAT solving.
Specifically, we first resort to logic synthesis algorithms to pre-process SAT
instances into optimized and-inverter graphs (AIGs). By doing so, the
distribution diversity among various SAT instances can be dramatically reduced,
which facilitates improving the generalization capability of the learned model.
Next, we regard the distribution of SAT solutions being a product of
conditional Bernoulli distributions. Based on this observation, we approximate
the SAT solving procedure with a conditional generative model, leveraging a
novel directed acyclic graph neural network (DAGNN) with two polarity
prototypes for conditional SAT modeling. To effectively train the generative
model, with the help of logic simulation tools, we obtain the probabilities of
nodes in the AIG being logic `1' as rich supervision. We conduct comprehensive
experiments on various SAT problems. Our results show that, DeepSAT achieves
significant accuracy improvements over state-of-the-art learning-based SAT
solutions, especially when generalized to SAT instances that are relatively
large or with diverse distributions.
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 03:20:42 GMT"
},
{
"version": "v2",
"created": "Fri, 20 Jan 2023 02:10:52 GMT"
}
] | 1,674,432,000,000 | [
[
"Li",
"Min",
""
],
[
"Shi",
"Zhengyuan",
""
],
[
"Lai",
"Qiuxia",
""
],
[
"Khan",
"Sadaf",
""
],
[
"Cai",
"Shaowei",
""
],
[
"Xu",
"Qiang",
""
]
] |
2205.13763 | SeokBin Son | Seok Bin Son, Soohyun Park, Haemin Lee, Joongheon Kim, Soyi Jung, and
Donghwa Kim | Tutorial on Course-of-Action (COA) Attack Search Methods in Computer
Networks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | In the literature of modern network security research, deriving effective and
efficient course-of-action (COA) attach search methods are of interests in
industry and academia. As the network size grows, the traditional COA attack
search methods can suffer from the limitations to computing and communication
resources. Therefore, various methods have been developed to solve these
problems, and reinforcement learning (RL)-based intelligent algorithms are one
of the most effective solutions. Therefore, we review the RL-based COA attack
search methods for network attack scenarios in terms of the trends and their
contrib
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 05:37:07 GMT"
}
] | 1,653,868,800,000 | [
[
"Son",
"Seok Bin",
""
],
[
"Park",
"Soohyun",
""
],
[
"Lee",
"Haemin",
""
],
[
"Kim",
"Joongheon",
""
],
[
"Jung",
"Soyi",
""
],
[
"Kim",
"Donghwa",
""
]
] |
2205.13954 | Bin Lu | Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang, Luoyi Fu, Xinbing Wang | Geometer: Graph Few-Shot Class-Incremental Learning via Prototype
Representation | Accepted to KDD2022 | null | 10.1145/3534678.3539280 | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | With the tremendous expansion of graphs data, node classification shows its
great importance in many real-world applications. Existing graph neural network
based methods mainly focus on classifying unlabeled nodes within fixed classes
with abundant labeling. However, in many practical scenarios, graph evolves
with emergence of new nodes and edges. Novel classes appear incrementally along
with few labeling due to its newly emergence or lack of exploration. In this
paper, we focus on this challenging but practical graph few-shot
class-incremental learning (GFSCIL) problem and propose a novel method called
Geometer. Instead of replacing and retraining the fully connected neural
network classifer, Geometer predicts the label of a node by finding the nearest
class prototype. Prototype is a vector representing a class in the metric
space. With the pop-up of novel classes, Geometer learns and adjusts the
attention-based prototypes by observing the geometric proximity, uniformity and
separability. Teacher-student knowledge distillation and biased sampling are
further introduced to mitigate catastrophic forgetting and unbalanced labeling
problem respectively. Experimental results on four public datasets demonstrate
that Geometer achieves a substantial improvement of 9.46% to 27.60% over
state-of-the-art methods.
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 13:02:07 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Jun 2022 08:55:31 GMT"
}
] | 1,654,473,600,000 | [
[
"Lu",
"Bin",
""
],
[
"Gan",
"Xiaoying",
""
],
[
"Yang",
"Lina",
""
],
[
"Zhang",
"Weinan",
""
],
[
"Fu",
"Luoyi",
""
],
[
"Wang",
"Xinbing",
""
]
] |
2205.13958 | Shasha Liu | Hayssam Dahrouj, Shasha Liu, Mohamed-Slim Alouini | Machine Learning-Based User Scheduling in Integrated
Satellite-HAPS-Ground Networks | arXiv admin note: substantial text overlap with arXiv:2204.13257 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Integrated space-air-ground networks promise to offer a valuable solution
space for empowering the sixth generation of communication networks (6G),
particularly in the context of connecting the unconnected and ultraconnecting
the connected. Such digital inclusion thrive makes resource management
problems, especially those accounting for load-balancing considerations, of
particular interest. The conventional model-based optimization methods,
however, often fail to meet the real-time processing and quality-of-service
needs, due to the high heterogeneity of the space-air-ground networks, and the
typical complexity of the classical algorithms. Given the premises of
artificial intelligence at automating wireless networks design and the
large-scale heterogeneity of non-terrestrial networks, this paper focuses on
showcasing the prospects of machine learning in the context of user scheduling
in integrated space-air-ground communications. The paper first overviews the
most relevant state-of-the art in the context of machine learning applications
to the resource allocation problems, with a dedicated attention to
space-air-ground networks. The paper then proposes, and shows the benefit of,
one specific use case that uses ensembling deep neural networks for optimizing
the user scheduling policies in integrated space-high altitude platform station
(HAPS)-ground networks. Finally, the paper sheds light on the challenges and
open issues that promise to spur the integration of machine learning in
space-air-ground networks, namely, online HAPS power adaptation, learning-based
channel sensing, data-driven multi-HAPSs resource management, and intelligent
flying taxis-empowered systems.
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 13:09:29 GMT"
},
{
"version": "v2",
"created": "Tue, 31 May 2022 12:14:33 GMT"
},
{
"version": "v3",
"created": "Sat, 4 Jun 2022 11:42:40 GMT"
},
{
"version": "v4",
"created": "Sun, 4 Dec 2022 10:57:38 GMT"
},
{
"version": "v5",
"created": "Sun, 18 Dec 2022 07:23:40 GMT"
}
] | 1,671,494,400,000 | [
[
"Dahrouj",
"Hayssam",
""
],
[
"Liu",
"Shasha",
""
],
[
"Alouini",
"Mohamed-Slim",
""
]
] |
2205.14032 | Cogan Shimizu | Cogan Shimizu, Andrew Eells, Seila Gonzalez, Lu Zhou, Pascal Hitzler,
Alicia Sheill, Catherine Foley, Dean Rehberger | Ontology Design Facilitating Wikibase Integration -- and a Worked
Example for Historical Data | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-sa/4.0/ | Wikibase -- which is the software underlying Wikidata -- is a powerful
platform for knowledge graph creation and management. However, it has been
developed with a crowd-sourced knowledge graph creation scenario in mind, which
in particular means that it has not been designed for use case scenarios in
which a tightly controlled high-quality schema, in the form of an ontology, is
to be imposed, and indeed, independently developed ontologies do not
necessarily map seamlessly to the Wikibase approach. In this paper, we provide
the key ingredients needed in order to combine traditional ontology modeling
with use of the Wikibase platform, namely a set of \emph{axiom} patterns that
bridge the paradigm gap, together with usage instructions and a worked example
for historical data.
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 15:01:35 GMT"
}
] | 1,653,868,800,000 | [
[
"Shimizu",
"Cogan",
""
],
[
"Eells",
"Andrew",
""
],
[
"Gonzalez",
"Seila",
""
],
[
"Zhou",
"Lu",
""
],
[
"Hitzler",
"Pascal",
""
],
[
"Sheill",
"Alicia",
""
],
[
"Foley",
"Catherine",
""
],
[
"Rehberger",
"Dean",
""
]
] |
2205.14094 | Melanie Bernhardt | Melanie Bernhardt, Fabio De Sousa Ribeiro, Ben Glocker | Failure Detection in Medical Image Classification: A Reality Check and
Benchmarking Testbed | Published in Transactions on Machine Learning Research (10/2022) | Transactions on Machine Learning Research (10/2022) | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Failure detection in automated image classification is a critical safeguard
for clinical deployment. Detected failure cases can be referred to human
assessment, ensuring patient safety in computer-aided clinical decision making.
Despite its paramount importance, there is insufficient evidence about the
ability of state-of-the-art confidence scoring methods to detect test-time
failures of classification models in the context of medical imaging. This paper
provides a reality check, establishing the performance of in-domain
misclassification detection methods, benchmarking 9 widely used confidence
scores on 6 medical imaging datasets with different imaging modalities, in
multiclass and binary classification settings. Our experiments show that the
problem of failure detection is far from being solved. We found that none of
the benchmarked advanced methods proposed in the computer vision and machine
learning literature can consistently outperform a simple softmax baseline,
demonstrating that improved out-of-distribution detection or model calibration
do not necessarily translate to improved in-domain misclassification detection.
Our developed testbed facilitates future work in this important area
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 16:50:48 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Oct 2022 08:42:52 GMT"
}
] | 1,666,656,000,000 | [
[
"Bernhardt",
"Melanie",
""
],
[
"Ribeiro",
"Fabio De Sousa",
""
],
[
"Glocker",
"Ben",
""
]
] |
2205.14229 | Jonathan Laurent | Jonathan Laurent and Andr\'e Platzer | Learning to Find Proofs and Theorems by Learning to Refine Search
Strategies: The Case of Loop Invariant Synthesis | null | Advances in Neural Information Processing Systems, volume 35
(2022) 4843--4856 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We propose a new approach to automated theorem proving where an
AlphaZero-style agent is self-training to refine a generic high-level expert
strategy expressed as a nondeterministic program. An analogous teacher agent is
self-training to generate tasks of suitable relevance and difficulty for the
learner. This allows leveraging minimal amounts of domain knowledge to tackle
problems for which training data is unavailable or hard to synthesize. As a
specific illustration, we consider loop invariant synthesis for imperative
programs and use neural networks to refine both the teacher and solver
strategies.
| [
{
"version": "v1",
"created": "Fri, 27 May 2022 20:48:40 GMT"
},
{
"version": "v2",
"created": "Sat, 6 Aug 2022 20:49:55 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Oct 2022 13:54:00 GMT"
}
] | 1,694,476,800,000 | [
[
"Laurent",
"Jonathan",
""
],
[
"Platzer",
"André",
""
]
] |
2205.14327 | Navdeep Kumar | Navdeep Kumar, Kfir Levy, Kaixin Wang, Shie Mannor | Efficient Policy Iteration for Robust Markov Decision Processes via
Regularization | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Robust Markov decision processes (MDPs) provide a general framework to model
decision problems where the system dynamics are changing or only partially
known. Efficient methods for some \texttt{sa}-rectangular robust MDPs exist,
using its equivalence with reward regularized MDPs, generalizable to online
settings. In comparison to \texttt{sa}-rectangular robust MDPs,
\texttt{s}-rectangular robust MDPs are less restrictive but much more difficult
to deal with. Interestingly, recent works have established the equivalence
between \texttt{s}-rectangular robust MDPs and policy regularized MDPs. But we
don't have a clear understanding to exploit this equivalence, to do policy
improvement steps to get the optimal value function or policy. We don't have a
clear understanding of greedy/optimal policy except it can be stochastic. There
exist no methods that can naturally be generalized to model-free settings. We
show a clear and explicit equivalence between \texttt{s}-rectangular $L_p$
robust MDPs and policy regularized MDPs that resemble very much policy entropy
regularized MDPs widely used in practice. Further, we dig into the policy
improvement step and concretely derive optimal robust Bellman operators for
\texttt{s}-rectangular $L_p$ robust MDPs. We find that the greedy/optimal
policies in \texttt{s}-rectangular $L_p$ robust MDPs are threshold policies
that play top $k$ actions whose $Q$ value is greater than some threshold
(value), proportional to the $(p-1)$th power of its advantage. In addition, we
show time complexity of (\texttt{sa} and \texttt{s}-rectangular) $L_p$ robust
MDPs is the same as non-robust MDPs up to some log factors. Our work greatly
extends the existing understanding of \texttt{s}-rectangular robust MDPs and
naturally generalizable to online settings.
| [
{
"version": "v1",
"created": "Sat, 28 May 2022 04:05:20 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Oct 2022 11:03:29 GMT"
}
] | 1,665,014,400,000 | [
[
"Kumar",
"Navdeep",
""
],
[
"Levy",
"Kfir",
""
],
[
"Wang",
"Kaixin",
""
],
[
"Mannor",
"Shie",
""
]
] |
2205.14753 | Nguyen Dang | Nguyen Dang, \"Ozg\"ur Akg\"un, Joan Espasa, Ian Miguel, Peter
Nightingale | A Framework for Generating Informative Benchmark Instances | 15 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Benchmarking is an important tool for assessing the relative performance of
alternative solving approaches. However, the utility of benchmarking is limited
by the quantity and quality of the available problem instances. Modern
constraint programming languages typically allow the specification of a
class-level model that is parameterised over instance data. This separation
presents an opportunity for automated approaches to generate instance data that
define instances that are graded (solvable at a certain difficulty level for a
solver) or can discriminate between two solving approaches. In this paper, we
introduce a framework that combines these two properties to generate a large
number of benchmark instances, purposely generated for effective and
informative benchmarking. We use five problems that were used in the MiniZinc
competition to demonstrate the usage of our framework. In addition to producing
a ranking among solvers, our framework gives a broader understanding of the
behaviour of each solver for the whole instance space; for example by finding
subsets of instances where the solver performance significantly varies from its
average performance.
| [
{
"version": "v1",
"created": "Sun, 29 May 2022 19:56:08 GMT"
}
] | 1,653,955,200,000 | [
[
"Dang",
"Nguyen",
""
],
[
"Akgün",
"Özgür",
""
],
[
"Espasa",
"Joan",
""
],
[
"Miguel",
"Ian",
""
],
[
"Nightingale",
"Peter",
""
]
] |
2205.15126 | Linjie Xu | Linjie Xu, Jorge Hurtado-Grueso, Dominic Jeurissen, Diego Perez
Liebana, Alexander Dockhorn | Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game
Playing | 8 pages, 3 figures; Published on IEEE Conference on Games 2022 | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Strategy video games challenge AI agents with their combinatorial search
space caused by complex game elements. State abstraction is a popular technique
that reduces the state space complexity. However, current state abstraction
methods for games depend on domain knowledge, making their application to new
games expensive. State abstraction methods that require no domain knowledge are
studied extensively in the planning domain. However, no evidence shows they
scale well with the complexity of strategy games. In this paper, we propose
Elastic MCTS, an algorithm that uses state abstraction to play strategy games.
In Elastic MCTS, the nodes of the tree are clustered dynamically, first grouped
together progressively by state abstraction, and then separated when an
iteration threshold is reached. The elastic changes benefit from efficient
searching brought by state abstraction but avoid the negative influence of
using state abstraction for the whole search. To evaluate our method, we make
use of the general strategy games platform Stratega to generate scenarios of
varying complexity. Results show that Elastic MCTS outperforms MCTS baselines
with a large margin, while reducing the tree size by a factor of $10$. Code can
be found at: https://github.com/egg-west/Stratega
| [
{
"version": "v1",
"created": "Mon, 30 May 2022 14:18:45 GMT"
}
] | 1,653,955,200,000 | [
[
"Xu",
"Linjie",
""
],
[
"Hurtado-Grueso",
"Jorge",
""
],
[
"Jeurissen",
"Dominic",
""
],
[
"Liebana",
"Diego Perez",
""
],
[
"Dockhorn",
"Alexander",
""
]
] |
2205.15141 | Ryuta Arisaka | Ryuta Arisaka, Ryoma Nakai, Yusuke Kawamoto, Takayuki Ito | Theme Aspect Argumentation Model for Handling Fallacies | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | From daily discussions to marketing ads to political statements, information
manipulation is rife. It is increasingly more important that we have the right
set of tools to defend ourselves from manipulative rhetoric, or fallacies.
Suitable techniques to automatically identify fallacies are being investigated
in natural language processing research. However, a fallacy in one context may
not be a fallacy in another context, so there is also a need to explain how and
why it has come to be judged a fallacy. For the explainable fallacy
identification, we present a novel approach to characterising fallacies through
formal constraints, as a viable alternative to more traditional fallacy
classifications by informal criteria. To achieve this objective, we introduce a
novel context-aware argumentation model, the theme aspect argumentation model,
which can do both: the modelling of a given argumentation as it is expressed
(rhetorical modelling); and a deeper semantic analysis of the rhetorical
argumentation model. By identifying fallacies with formal constraints, it
becomes possible to tell whether a fallacy lurks in the modelled rhetoric with
a formal rigour. We present core formal constraints for the theme aspect
argumentation model and then more formal constraints that improve its fallacy
identification capability. We show and prove the consequences of these formal
constraints. We then analyse the computational complexities of deciding the
satisfiability of the constraints.
| [
{
"version": "v1",
"created": "Mon, 30 May 2022 14:34:09 GMT"
},
{
"version": "v2",
"created": "Wed, 25 Oct 2023 09:49:55 GMT"
}
] | 1,698,278,400,000 | [
[
"Arisaka",
"Ryuta",
""
],
[
"Nakai",
"Ryoma",
""
],
[
"Kawamoto",
"Yusuke",
""
],
[
"Ito",
"Takayuki",
""
]
] |
2205.15414 | Nguyen Dang | Nguyen Dang | A portfolio-based analysis method for competition results | 10 pages | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Competitions such as the MiniZinc Challenges or the SAT competitions have
been very useful sources for comparing performance of different solving
approaches and for advancing the state-of-the-arts of the fields. Traditional
competition setting often focuses on producing a ranking between solvers based
on their average performance across a wide range of benchmark problems and
instances. While this is a sensible way to assess the relative performance of
solvers, such ranking does not necessarily reflect the full potential of a
solver, especially when we want to utilise a portfolio of solvers instead of a
single one for solving a new problem. In this paper, I will describe a
portfolio-based analysis method which can give complementary insights into the
performance of participating solvers in a competition. The method is
demonstrated on the results of the MiniZinc Challenges and new insights gained
from the portfolio viewpoint are presented.
| [
{
"version": "v1",
"created": "Mon, 30 May 2022 20:20:45 GMT"
}
] | 1,654,041,600,000 | [
[
"Dang",
"Nguyen",
""
]
] |
2205.15714 | Maximilian Felde | Maximilian Felde and Gerd Stumme | Attribute Exploration with Multiple Contradicting Partial Experts | 22 pages (14 pages + 8 pages appendix) | null | 10.1007/978-3-031-16663-1_5 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Attribute exploration is a method from Formal Concept Analysis (FCA) that
helps a domain expert discover structural dependencies in knowledge domains
which can be represented as formal contexts (cross tables of objects and
attributes). In this paper we present an extension of attribute exploration
that allows for a group of domain experts and explores their shared views. Each
expert has their own view of the domain and the views of multiple experts may
contain contradicting information.
| [
{
"version": "v1",
"created": "Tue, 31 May 2022 12:00:55 GMT"
}
] | 1,663,718,400,000 | [
[
"Felde",
"Maximilian",
""
],
[
"Stumme",
"Gerd",
""
]
] |
2206.00595 | Timothy Parker | Umberto Grandi, Emiliano Lorini, Timothy Parker, Rachid Alami | Logic-Based Ethical Planning | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this paper we propose a framework for ethical decision making in the
context of planning, with intended application to robotics. We put forward a
compact but highly expressive language for ethical planning that combines
linear temporal logic with lexicographic preference modelling. This original
combination allows us to assess plans both with respect to an agent's values
and their desires, introducing the novel concept of the morality level of an
agent and moving towards multigoal, multivalue planning. We initiate the study
of computational complexity of planning tasks in our setting, and we discuss
potential applications to robotics.
| [
{
"version": "v1",
"created": "Wed, 1 Jun 2022 16:07:53 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Jun 2022 08:19:41 GMT"
}
] | 1,654,214,400,000 | [
[
"Grandi",
"Umberto",
""
],
[
"Lorini",
"Emiliano",
""
],
[
"Parker",
"Timothy",
""
],
[
"Alami",
"Rachid",
""
]
] |
2206.01044 | Bowen Xu | Bowen Xu, Quansheng Ren | Artificial Open World for Evaluating AGI: a Conceptual Design | null | null | 10.1007/978-3-031-19907-3_43 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | How to evaluate Artificial General Intelligence (AGI) is a critical problem
that is discussed and unsolved for a long period. In the research of narrow AI,
this seems not a severe problem, since researchers in that field focus on some
specific problems as well as one or some aspects of cognition, and the criteria
for evaluation are explicitly defined. By contrast, an AGI agent should solve
problems that are never-encountered by both agents and developers. However,
once a developer tests and debugs the agent with a problem, the
never-encountered problem becomes the encountered problem, as a result, the
problem is solved by the developers to some extent, exploiting their
experience, rather than the agents. This conflict, as we call the trap of
developers' experience, leads to that this kind of problems is probably hard to
become an acknowledged criterion. In this paper, we propose an evaluation
method named Artificial Open World, aiming to jump out of the trap. The
intuition is that most of the experience in the actual world should not be
necessary to be applied to the artificial world, and the world should be open
in some sense, such that developers are unable to perceive the world and solve
problems by themselves before testing, though after that they are allowed to
check all the data. The world is generated in a similar way as the actual
world, and a general form of problems is proposed. A metric is proposed aiming
to quantify the progress of research. This paper describes the conceptual
design of the Artificial Open World, though the formalization and the
implementation are left to the future.
| [
{
"version": "v1",
"created": "Thu, 2 Jun 2022 13:43:52 GMT"
}
] | 1,692,921,600,000 | [
[
"Xu",
"Bowen",
""
],
[
"Ren",
"Quansheng",
""
]
] |
2206.01240 | Marko Palangeti\'c | Marko Palangeti\'c, Chris Cornelis, Salvatore Greco, Roman
S{\l}owi\'nski | Fuzzy granular approximation classifier | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In this article, a new Fuzzy Granular Approximation Classifier (FGAC) is
introduced. The classifier is based on the previously introduced concept of the
granular approximation and its multi-class classification case. The classifier
is instance-based and its biggest advantage is its local transparency i.e., the
ability to explain every individual prediction it makes. We first develop the
FGAC for the binary classification case and the multi-class classification case
and we discuss its variation that includes the Ordered Weighted Average (OWA)
operators. Those variations of the FGAC are then empirically compared with
other locally transparent ML methods. At the end, we discuss the transparency
of the FGAC and its advantage over other locally transparent methods. We
conclude that while the FGAC has similar predictive performance to other
locally transparent ML models, its transparency can be superior in certain
cases.
| [
{
"version": "v1",
"created": "Thu, 2 Jun 2022 18:28:13 GMT"
}
] | 1,654,473,600,000 | [
[
"Palangetić",
"Marko",
""
],
[
"Cornelis",
"Chris",
""
],
[
"Greco",
"Salvatore",
""
],
[
"Słowiński",
"Roman",
""
]
] |
2206.01815 | Gabriele Sartor | Gabriele Sartor, Davide Zollo, Marta Cialdea Mayer, Angelo Oddi,
Riccardo Rasconi and Vieri Giuliano Santucci | Option Discovery for Autonomous Generation of Symbolic Knowledge | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | In this work we present an empirical study where we demonstrate the
possibility of developing an artificial agent that is capable to autonomously
explore an experimental scenario. During the exploration, the agent is able to
discover and learn interesting options allowing to interact with the
environment without any pre-assigned goal, then abstract and re-use the
acquired knowledge to solve possible tasks assigned ex-post. We test the system
in the so-called Treasure Game domain described in the recent literature and we
empirically demonstrate that the discovered options can be abstracted in an
probabilistic symbolic planning model (using the PPDDL language), which allowed
the agent to generate symbolic plans to achieve extrinsic goals.
| [
{
"version": "v1",
"created": "Fri, 3 Jun 2022 20:46:34 GMT"
}
] | 1,654,560,000,000 | [
[
"Sartor",
"Gabriele",
""
],
[
"Zollo",
"Davide",
""
],
[
"Mayer",
"Marta Cialdea",
""
],
[
"Oddi",
"Angelo",
""
],
[
"Rasconi",
"Riccardo",
""
],
[
"Santucci",
"Vieri Giuliano",
""
]
] |
2206.01822 | Damien Pellier | D. Pellier and H. Fiorino and M. Grand and A. Albore and R.
Bailon-Ruiz | HDDL 2.1: Towards Defining an HTN Formalism with Time | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Real world applications of planning, like in industry and robotics, require
modelling rich and diverse scenarios. Their resolution usually requires
coordinated and concurrent action executions. In several cases, such planning
problems are naturally decomposed in a hierarchical way and expressed by a
Hierarchical Task Network (HTN) formalism. The PDDL language used to specify
planning domains has evolved to cover the different planning paradigms.
However, formulating real and complex scenarios where numerical and temporal
constraints concur in defining a solution is still a challenge. Our proposition
aims at filling the gap between existing planning languages and operational
needs. To do so, we propose to extend HDDL taking inspiration from PDDL 2.1 and
ANML to express temporal and numerical expressions. This paper opens
discussions on the semantics and the syntax needed to extend HDDL, and
illustrate these needs with the modelling of an Earth Observing Satellite
planning problem.
| [
{
"version": "v1",
"created": "Fri, 3 Jun 2022 21:22:19 GMT"
}
] | 1,654,560,000,000 | [
[
"Pellier",
"D.",
""
],
[
"Fiorino",
"H.",
""
],
[
"Grand",
"M.",
""
],
[
"Albore",
"A.",
""
],
[
"Bailon-Ruiz",
"R.",
""
]
] |
2206.01954 | Shivani Bathla | Shivani Bathla and Vinita Vasudevan | MPE inference using an Incremental Build-Infer-Approximate Paradigm | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Exact inference of the most probable explanation (MPE) in Bayesian networks
is known to be NP-complete. In this paper, we propose an algorithm for
approximate MPE inference that is based on the incremental
build-infer-approximate (IBIA) framework. We use this framework to obtain an
ordered set of partitions of the Bayesian network and the corresponding
max-calibrated clique trees. We show that the maximum belief in the last
partition gives an estimate of the probability of the MPE assignment. We
propose an iterative algorithm for decoding, in which the subset of variables
for which an assignment is obtained is guaranteed to increase in every
iteration. There are no issues of convergence, and we do not perform a search
for solutions. Even though it is a single shot algorithm, we obtain valid
assignments in 100 out of the 117 benchmarks used for testing. The accuracy of
our solution is comparable to a branch and bound search in majority of the
benchmarks, with competitive run times.
| [
{
"version": "v1",
"created": "Sat, 4 Jun 2022 09:37:44 GMT"
}
] | 1,654,560,000,000 | [
[
"Bathla",
"Shivani",
""
],
[
"Vasudevan",
"Vinita",
""
]
] |
2206.02019 | Wangcheng Xu | Wangcheng Xu, Snejana Shegheva and Ashok Goel | Symmetry as a Representation of Intuitive Geometry? | CogSci 2022 Camera ready version | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recognition of geometrical patterns seems to be an important aspect of human
intelligence. Geometric pattern recognition is used in many intelligence tests,
including Dehaene's odd-one-out test of Core Geometry (CG)) based on intuitive
geometrical concepts (Dehaene et al., 2006). Earlier work has developed a
symmetry-based cognitive model of Dehaene's test and demonstrated performance
comparable to that of humans. In this work, we further investigate the role of
symmetry in geometrical intuition and build a cognitive model for the
2-Alternative Forced Choice (2-AFC) variation of the CG test (Marupudi & Varma
2021). In contrast to Dehaene's test, 2-AFC leaves almost no space for
cognitive models based on generalization over multiple examples. Our
symmetry-based model achieves an accuracy comparable to the human average on
the 2-AFC test and appears to capture an essential part of intuitive geometry.
| [
{
"version": "v1",
"created": "Sat, 4 Jun 2022 16:15:35 GMT"
}
] | 1,654,560,000,000 | [
[
"Xu",
"Wangcheng",
""
],
[
"Shegheva",
"Snejana",
""
],
[
"Goel",
"Ashok",
""
]
] |
2206.02144 | Joshua Hunte | Joshua Hunte, Martin Neil, Norman Fenton | Product safety idioms: a method for building causal Bayesian networks
for product safety and risk assessment | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Idioms are small, reusable Bayesian network (BN) fragments that represent
generic types of uncertain reasoning. This paper shows how idioms can be used
to build causal BNs for product safety and risk assessment that use a
combination of data and knowledge. We show that the specific product safety
idioms that we introduce are sufficient to build full BN models to evaluate
safety and risk for a wide range of products. The resulting models can be used
by safety regulators and product manufacturers even when there are limited (or
no) product testing data.
| [
{
"version": "v1",
"created": "Sun, 5 Jun 2022 10:16:03 GMT"
},
{
"version": "v2",
"created": "Thu, 9 Jun 2022 18:04:35 GMT"
}
] | 1,655,078,400,000 | [
[
"Hunte",
"Joshua",
""
],
[
"Neil",
"Martin",
""
],
[
"Fenton",
"Norman",
""
]
] |
2206.02216 | Erik Skalnes | Erik Skalnes | Sequential Counterfactual Decision-Making Under Confounded Reward | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | We investigate the limitations of random trials when the cause of interest is
confounded with the effect by formalizing a counterfactual policy-space where
the agent's natural predilection is input to a soft-intervention.
| [
{
"version": "v1",
"created": "Sun, 5 Jun 2022 16:44:42 GMT"
}
] | 1,654,560,000,000 | [
[
"Skalnes",
"Erik",
""
]
] |
2206.03124 | Micha\"el Thomazo | David Carral, Lucas Larroque, Marie-Laure Mugnier and Micha\"el
Thomazo | Normalisations of Existential Rules: Not so Innocuous! | Published at 19th International Conference on Principles of Knowledge
Representation and Reasoning, KR 2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Existential rules are an expressive knowledge representation language mainly
developed to query data. In the literature, they are often supposed to be in
some normal form that simplifies technical developments. For instance, a common
assumption is that rule heads are atomic, i.e., restricted to a single atom.
Such assumptions are considered to be made without loss of generality as long
as all sets of rules can be normalised while preserving entailment. However, an
important question is whether the properties that ensure the decidability of
reasoning are preserved as well. We provide a systematic study of the impact of
these procedures on the different chase variants with respect to chase
(non-)termination and FO-rewritability. This also leads us to study open
problems related to chase termination of independent interest.
| [
{
"version": "v1",
"created": "Tue, 7 Jun 2022 09:01:56 GMT"
}
] | 1,654,646,400,000 | [
[
"Carral",
"David",
""
],
[
"Larroque",
"Lucas",
""
],
[
"Mugnier",
"Marie-Laure",
""
],
[
"Thomazo",
"Michaël",
""
]
] |
2206.03356 | Eyal Weiss | Eyal Weiss and Gal A. Kaminka | Position Paper: Online Modeling for Offline Planning | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | The definition and representation of planning problems is at the heart of AI
planning research. A key part is the representation of action models. Decades
of advances improving declarative action model representations resulted in
numerous theoretical advances, and capable, working, domain-independent
planners. However, despite the maturity of the field, AI planning technology is
still rarely used outside the research community, suggesting that current
representations fail to capture real-world requirements, such as utilizing
complex mathematical functions and models learned from data. We argue that this
is because the modeling process is assumed to have taken place and completed
prior to the planning process, i.e., offline modeling for offline planning.
There are several challenges inherent to this approach, including: limited
expressiveness of declarative modeling languages; early commitment to modeling
choices and computation, that preclude using the most appropriate resolution
for each action model -- which can only be known during planning; and
difficulty in reliably using non-declarative, learned, models.
We therefore suggest to change the AI planning process, such that is carries
out online modeling in offline planning, i.e., the use of action models that
are computed or even generated as part of the planning process, as they are
accessed. This generalizes the existing approach (offline modeling). The
proposed definition admits novel planning processes, and we suggest one
concrete implementation, demonstrating the approach. We sketch initial results
that were obtained as part of a first attempt to follow this approach by
planning with action cost estimators. We conclude by discussing open
challenges.
| [
{
"version": "v1",
"created": "Tue, 7 Jun 2022 14:48:08 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2022 08:05:08 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Jun 2022 12:49:48 GMT"
}
] | 1,655,856,000,000 | [
[
"Weiss",
"Eyal",
""
],
[
"Kaminka",
"Gal A.",
""
]
] |
2206.03487 | Evgenii Vityaev | E.E. Vityaev, A.G. Kolonin, A.V. Kurpatov A.A. Molchanov | Formalization of the principles of brain Programming (Brain Principles
Programming) | 28 pages, in Russian, 4 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In the monograph "Strong artificial intelligence. On the Approaches to
Superintelligence" contains an overview of general artificial intelligence
(AGI). As an anthropomorphic research area, it includes Brain Principles
Programming (BPP) -- the formalization of universal mechanisms (principles) of
the brain work with information, which are implemented at all levels of the
organization of nervous tissue. This monograph contains a formalization of
these principles in terms of category theory. However, this formalization is
not enough to develop algorithms for working with information. In this paper,
for the description and modeling of BPP, it is proposed to apply mathematical
models and algorithms developed earlier, which modeling cognitive functions and
base on well-known physiological, psychological and other natural science
theories. The paper uses mathematical models and algorithms of the following
theories: P.K.Anokhin Theory of Functional Brain Systems, Eleanor Rosch
prototypical categorization theory, Bob Rehder theory of causal models and
"natural" classification. As a result, a formalization of BPP is obtained and
computer experiments demonstrating the operation of algorithms are presented.
| [
{
"version": "v1",
"created": "Fri, 13 May 2022 13:16:34 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2022 13:45:08 GMT"
},
{
"version": "v3",
"created": "Wed, 15 Jun 2022 02:26:12 GMT"
}
] | 1,655,337,600,000 | [
[
"Vityaev",
"E. E.",
""
],
[
"Kolonin",
"A. G.",
""
],
[
"Molchanov",
"A. V. Kurpatov A. A.",
""
]
] |
2206.03965 | Dennis Soemers | Elliot Doe and Mark H. M. Winands and Dennis J. N. J. Soemers and
Cameron Browne | Combining Monte-Carlo Tree Search with Proof-Number Search | Accepted at IEEE CoG 2022. Copyright of final version held by IEEE | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Proof-Number Search (PNS) and Monte-Carlo Tree Search (MCTS) have been
successfully applied for decision making in a range of games. This paper
proposes a new approach called PN-MCTS that combines these two tree-search
methods by incorporating the concept of proof and disproof numbers into the UCT
formula of MCTS. Experimental results demonstrate that PN-MCTS outperforms
basic MCTS in several games including Lines of Action, MiniShogi,
Knightthrough, and Awari, achieving win rates up to 94.0%.
| [
{
"version": "v1",
"created": "Wed, 8 Jun 2022 15:28:42 GMT"
}
] | 1,654,732,800,000 | [
[
"Doe",
"Elliot",
""
],
[
"Winands",
"Mark H. M.",
""
],
[
"Soemers",
"Dennis J. N. J.",
""
],
[
"Browne",
"Cameron",
""
]
] |
2206.04460 | Julian Tritscher | Julian Tritscher, Fabian Gwinner, Daniel Schl\"or, Anna Krause,
Andreas Hotho | Open ERP System Data For Occupational Fraud Detection | null | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Recent estimates report that companies lose 5% of their revenue to
occupational fraud. Since most medium-sized and large companies employ
Enterprise Resource Planning (ERP) systems to track vast amounts of information
regarding their business process, researchers have in the past shown interest
in automatically detecting fraud through ERP system data. Current research in
this area, however, is hindered by the fact that ERP system data is not
publicly available for the development and comparison of fraud detection
methods. We therefore endeavour to generate public ERP system data that
includes both normal business operation and fraud. We propose a strategy for
generating ERP system data through a serious game, model a variety of fraud
scenarios in cooperation with auditing experts, and generate data from a
simulated make-to-stock production company with multiple research participants.
We aggregate the generated data into ready to used datasets for fraud detection
in ERP systems, and supply both the raw and aggregated data to the general
public to allow for open development and comparison of fraud detection
approaches on ERP system data.
| [
{
"version": "v1",
"created": "Thu, 9 Jun 2022 12:38:29 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Jun 2022 13:04:56 GMT"
},
{
"version": "v3",
"created": "Wed, 13 Jul 2022 07:51:02 GMT"
}
] | 1,657,756,800,000 | [
[
"Tritscher",
"Julian",
""
],
[
"Gwinner",
"Fabian",
""
],
[
"Schlör",
"Daniel",
""
],
[
"Krause",
"Anna",
""
],
[
"Hotho",
"Andreas",
""
]
] |
2206.04724 | Till Mossakowski | Till Mossakowski | Modular design patterns for neural-symbolic integration: refinement and
combination | null | 16th International Workshop on Neural-Symbolic Learning and
Reasoning (NeSy), volume 3212, series CEUR Workshop proceedings, pages
192-201, 2022 | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | We formalise some aspects of the neural-symbol design patterns of van Bekkum
et al., such that we can formally define notions of refinement of patterns, as
well as modular combination of larger patterns from smaller building blocks.
These formal notions are being implemented in the heterogeneous tool set
(Hets), such that patterns and refinements can be checked for well-formedness,
and combinations can be computed.
| [
{
"version": "v1",
"created": "Thu, 9 Jun 2022 18:41:15 GMT"
},
{
"version": "v2",
"created": "Tue, 27 Sep 2022 16:13:50 GMT"
}
] | 1,664,323,200,000 | [
[
"Mossakowski",
"Till",
""
]
] |
2206.04909 | Jiafei Duan | Jieyi Ye, Jiafei Duan, Samson Yu, Bihan Wen, Cheston Tan | ABCDE: An Agent-Based Cognitive Development Environment | Accepted to CVPRW 2022,Embodied AI Workshop (Extended Abstract) | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Children's cognitive abilities are sometimes cited as AI benchmarks. How can
the most common 1,000 concepts (89\% of everyday use) be learnt in a
naturalistic children's setting? Cognitive development in children is about
quality, and new concepts can be conveyed via simple examples. Our approach of
knowledge scaffolding uses simple objects and actions to convey concepts, like
how children are taught. We introduce ABCDE, an interactive 3D environment
modeled after a typical playroom for children. It comes with 300+ unique 3D
object assets (mostly toys), and a large action space for child and parent
agents to interact with objects and each other. ABCDE is the first environment
aimed at mimicking a naturalistic setting for cognitive development in
children; no other environment focuses on high-level concept learning through
learner-teacher interactions. The simulator can be found at
https://pypi.org/project/ABCDESim/1.0.0/
| [
{
"version": "v1",
"created": "Fri, 10 Jun 2022 07:23:26 GMT"
}
] | 1,655,078,400,000 | [
[
"Ye",
"Jieyi",
""
],
[
"Duan",
"Jiafei",
""
],
[
"Yu",
"Samson",
""
],
[
"Wen",
"Bihan",
""
],
[
"Tan",
"Cheston",
""
]
] |
2206.05273 | Seng-Beng Ho | Seng-Beng Ho | A General Framework for the Representation of Function and Affordance: A
Cognitive, Causal, and Grounded Approach, and a Step Toward AGI | 66 pages, 49 figures | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | In AI research, so far, the attention paid to the characterization and
representation of function and affordance has been sporadic and sparse, even
though this aspect features prominently in an intelligent system's functioning.
In the sporadic and sparse, though commendable efforts so far devoted to the
characterization and understanding of function and affordance, there has also
been no general framework that could unify all the different use domains and
situations related to the representation and application of functional
concepts. This paper develops just such a general framework, with an approach
that emphasizes the fact that the representations involved must be explicitly
cognitive and conceptual, and they must also contain causal characterizations
of the events and processes involved, as well as employ conceptual constructs
that are grounded in the referents to which they refer, in order to achieve
maximal generality. The basic general framework is described, along with a set
of basic guiding principles with regards to the representation of
functionality. To properly and adequately characterize and represent
functionality, a descriptive representation language is needed. This language
is defined and developed, and many examples of its use are described. The
general framework is developed based on an extension of the general language
meaning representational framework called conceptual dependency. To support the
general characterization and representation of functionality, the basic
conceptual dependency framework is enhanced with representational devices
called structure anchor and conceptual dependency elaboration, together with
the definition of a set of ground level concepts. These novel representational
constructs are defined, developed, and described. A general framework dealing
with functionality would represent a major step toward achieving Artificial
General Intelligence.
| [
{
"version": "v1",
"created": "Thu, 2 Jun 2022 08:25:55 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2022 07:51:05 GMT"
},
{
"version": "v3",
"created": "Wed, 17 Aug 2022 04:17:30 GMT"
}
] | 1,660,780,800,000 | [
[
"Ho",
"Seng-Beng",
""
]
] |
2206.05370 | Robert Helmeczi | Robert K. Helmeczi and Can Kavaklioglu and Mucahit Cevik and Davood
Pirayesh Neghab | A multi-objective constrained POMDP model for breast cancer screening | 37 pages, 5 figures | null | 10.1007/s12351-023-00774-w | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Breast cancer is a common and deadly disease, but it is often curable when
diagnosed early. While most countries have large-scale screening programs,
there is no consensus on a single globally accepted guideline for breast cancer
screening. The complex nature of the disease; the limited availability of
screening methods such as mammography, magnetic resonance imaging (MRI), and
ultrasound; and public health policies all factor into the development of
screening policies. Resource availability concerns necessitate the design of
policies which conform to a budget, a problem which can be modelled as a
constrained partially observable Markov decision process (CPOMDP). In this
study, we propose a multi-objective CPOMDP model for breast cancer screening
which allows for supplemental screening methods to accompany mammography. The
model has two objectives: maximize the quality-adjusted life years (QALYs) and
minimize lifetime breast cancer mortality risk (LBCMR). We identify the Pareto
frontier of optimal solutions for average and high-risk patients at different
budget levels, which can be used by decision-makers to set policies in
practice. We find that the policies obtained by using a weighted objective are
able to generate well-balanced QALYs and LBCMR values. In contrast, the
single-objective models generally sacrifice a substantial amount in terms of
QALYs/LBCMR for a minimal gain in LBCMR/QALYs. Additionally, our results show
that, with the baseline cost values for supplemental screenings as well as the
additional disutility that they incur, they are rarely recommended in CPOMDP
policies, especially in a budget-constrained setting. A sensitivity analysis
reveals the thresholds on cost and disutility values at which supplemental
screenings become advantageous to prescribe.
| [
{
"version": "v1",
"created": "Fri, 10 Jun 2022 22:43:49 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Jan 2023 17:56:26 GMT"
}
] | 1,687,824,000,000 | [
[
"Helmeczi",
"Robert K.",
""
],
[
"Kavaklioglu",
"Can",
""
],
[
"Cevik",
"Mucahit",
""
],
[
"Neghab",
"Davood Pirayesh",
""
]
] |
2206.05418 | Jianfeng Zhan | Yatao Li, Jianfeng Zhan | SAIBench: Benchmarking AI for Science | Published in BenchCouncil Transactions on Benchmarks, Standards and
Evaluations (TBench) | null | 10.1016/j.tbench.2022.100063 | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | Scientific research communities are embracing AI-based solutions to target
tractable scientific tasks and improve research workflows. However, the
development and evaluation of such solutions are scattered across multiple
disciplines. We formalize the problem of scientific AI benchmarking, and
propose a system called SAIBench in the hope of unifying the efforts and
enabling low-friction on-boarding of new disciplines. The system approaches
this goal with SAIL, a domain-specific language to decouple research problems,
AI models, ranking criteria, and software/hardware configuration into reusable
modules. We show that this approach is flexible and can adapt to problems, AI
models, and evaluation methods defined in different perspectives. The project
homepage is https://www.computercouncil.org/SAIBench
| [
{
"version": "v1",
"created": "Sat, 11 Jun 2022 04:19:51 GMT"
}
] | 1,655,164,800,000 | [
[
"Li",
"Yatao",
""
],
[
"Zhan",
"Jianfeng",
""
]
] |
2206.05421 | Joseph Ramsey | Wai-Yin Lam, Bryan Andrews, Joseph Ramsey | Greedy Relaxations of the Sparsest Permutation Algorithm | 36 pages, 16 figures, 4 tables, 2 algorithms, accepted, UAI
(Uncertainty in Artificial Intelligence) 2022 | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | There has been an increasing interest in methods that exploit permutation
reasoning to search for directed acyclic causal models, including the "Ordering
Search" of Teyssier and Kohler and GSP of Solus, Wang and Uhler. We extend the
methods of the latter by a permutation-based operation, tuck, and develop a
class of algorithms, namely GRaSP, that are efficient and pointwise consistent
under increasingly weaker assumptions than faithfulness. The most relaxed form
of GRaSP outperforms many state-of-the-art causal search algorithms in
simulation, allowing efficient and accurate search even for dense graphs and
graphs with more than 100 variables.
| [
{
"version": "v1",
"created": "Sat, 11 Jun 2022 05:00:36 GMT"
}
] | 1,655,164,800,000 | [
[
"Lam",
"Wai-Yin",
""
],
[
"Andrews",
"Bryan",
""
],
[
"Ramsey",
"Joseph",
""
]
] |
2206.05532 | Gyunam Park | Gyunam Park, Janik-Vasily Benzin, Wil M. P. van der Aalst | Detecting Context-Aware Deviations in Process Executions | null | LNBIP 458 (2022) 190-206 | 10.1007/978-3-031-16171-1_12 | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | A deviation detection aims to detect deviating process instances, e.g.,
patients in the healthcare process and products in the manufacturing process. A
business process of an organization is executed in various contextual
situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of
semiconductor chip shortage in the case of automobile companies. Thus,
context-aware deviation detection is essential to provide relevant insights.
However, existing work 1) does not provide a systematic way of incorporating
various contexts, 2) is tailored to a specific approach without using an
extensive pool of existing deviation detection techniques, and 3) does not
distinguish positive and negative contexts that justify and refute deviation,
respectively. In this work, we provide a framework to bridge the aforementioned
gaps. We have implemented the proposed framework as a web service that can be
extended to various contexts and deviation detection methods. We have evaluated
the effectiveness of the proposed framework by conducting experiments using 255
different contextual scenarios.
| [
{
"version": "v1",
"created": "Sat, 11 Jun 2022 13:45:04 GMT"
}
] | 1,667,260,800,000 | [
[
"Park",
"Gyunam",
""
],
[
"Benzin",
"Janik-Vasily",
""
],
[
"van der Aalst",
"Wil M. P.",
""
]
] |
2206.05922 | Hao Tang | Hao Tang and Kevin Ellis | From Perception to Programs: Regularize, Overparameterize, and Amortize | ICML 2023 | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-sa/4.0/ | Toward combining inductive reasoning with perception abilities, we develop
techniques for neurosymbolic program synthesis where perceptual input is first
parsed by neural nets into a low-dimensional interpretable representation,
which is then processed by a synthesized program. We explore several techniques
for relaxing the problem and jointly learning all modules end-to-end with
gradient descent: multitask learning; amortized inference;
overparameterization; and a differentiable strategy for penalizing lengthy
programs. Collectedly this toolbox improves the stability of gradient-guided
program search, and suggests ways of learning both how to perceive input as
discrete abstractions, and how to symbolically process those abstractions as
programs.
| [
{
"version": "v1",
"created": "Mon, 13 Jun 2022 06:27:11 GMT"
},
{
"version": "v2",
"created": "Wed, 31 May 2023 19:10:41 GMT"
}
] | 1,685,664,000,000 | [
[
"Tang",
"Hao",
""
],
[
"Ellis",
"Kevin",
""
]
] |
2206.06202 | Quinten Van Baelen | Quinten Van Baelen, Peter Karsmakers | Constraint Guided Gradient Descent: Guided Training with Inequality
Constraints | 9 pages, 1 figure, 1 table Comments: corrected typo in author list | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | Deep learning is typically performed by learning a neural network solely from
data in the form of input-output pairs ignoring available domain knowledge. In
this work, the Constraint Guided Gradient Descent (CGGD) framework is proposed
that enables the injection of domain knowledge into the training procedure. The
domain knowledge is assumed to be described as a conjunction of hard inequality
constraints which appears to be a natural choice for several applications.
Compared to other neuro-symbolic approaches, the proposed method converges to a
model that satisfies any inequality constraint on the training data and does
not require to first transform the constraints into some ad-hoc term that is
added to the learning (optimisation) objective. Under certain conditions, it is
shown that CGGD can converges to a model that satisfies the constraints on the
training set, while prior work does not necessarily converge to such a model.
It is empirically shown on two independent and small data sets that CGGD makes
training less dependent on the initialisation of the network and improves the
constraint satisfiability on all data.
| [
{
"version": "v1",
"created": "Mon, 13 Jun 2022 14:33:33 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Jun 2022 06:01:03 GMT"
}
] | 1,655,251,200,000 | [
[
"Van Baelen",
"Quinten",
""
],
[
"Karsmakers",
"Peter",
""
]
] |
2206.06213 | Dario Izzo | Marcus M\"artens and Dario Izzo | Symbolic Regression for Space Applications: Differentiable Cartesian
Genetic Programming Powered by Multi-objective Memetic Algorithms | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Interpretable regression models are important for many application domains,
as they allow experts to understand relations between variables from sparse
data. Symbolic regression addresses this issue by searching the space of all
possible free form equations that can be constructed from elementary algebraic
functions. While explicit mathematical functions can be rediscovered this way,
the determination of unknown numerical constants during search has been an
often neglected issue. We propose a new multi-objective memetic algorithm that
exploits a differentiable Cartesian Genetic Programming encoding to learn
constants during evolutionary loops. We show that this approach is competitive
or outperforms machine learned black box regression models or hand-engineered
fits for two applications from space: the Mars express thermal power estimation
and the determination of the age of stars by gyrochronology.
| [
{
"version": "v1",
"created": "Mon, 13 Jun 2022 14:44:15 GMT"
}
] | 1,655,164,800,000 | [
[
"Märtens",
"Marcus",
""
],
[
"Izzo",
"Dario",
""
]
] |
2206.06440 | Yuliya Lierler | Yuliya Lierler | An Abstract View on Optimizations in Propositional Frameworks | null | null | null | null | cs.AI | http://creativecommons.org/licenses/by/4.0/ | Search-optimization problems are plentiful in scientific and engineering
domains. Artificial intelligence has long contributed to the development of
search algorithms and declarative programming languages geared toward solving
and modeling search-optimization problems. Automated reasoning and knowledge
representation are the subfields of AI that are particularly vested in these
developments. Many popular automated reasoning paradigms provide users with
languages supporting optimization statements: answer set programming or MaxSAT
on minone, to name a few. These paradigms vary significantly in their languages
and in the ways they express quality conditions on computed solutions. Here we
propose a unifying framework of so-called weight systems that eliminates
syntactic distinctions between paradigms and allows us to see essential
similarities and differences between optimization statements provided by
paradigms. This unifying outlook has significant simplifying and explanatory
potential in the studies of optimization and modularity in automated reasoning
and knowledge representation. It also supplies researchers with a convenient
tool for proving the formal properties of distinct frameworks; bridging these
frameworks; and facilitating the development of translational solvers.
| [
{
"version": "v1",
"created": "Mon, 13 Jun 2022 19:44:01 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Mar 2023 16:03:12 GMT"
},
{
"version": "v3",
"created": "Mon, 20 Mar 2023 22:23:20 GMT"
}
] | 1,679,443,200,000 | [
[
"Lierler",
"Yuliya",
""
]
] |
2206.06530 | Christian Muise | Ethan Callanan, Rebecca De Venezia, Victoria Armstrong, Alison
Paredes, Tathagata Chakraborti, Christian Muise | MACQ: A Holistic View of Model Acquisition Techniques | 8 pages, 7 figures, KEPS Workshop Submission | null | null | null | cs.AI | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ | For over three decades, the planning community has explored countless methods
for data-driven model acquisition. These range in sophistication (e.g., simple
set operations to full-blown reformulations), methodology (e.g., logic-based
vs. planing-based), and assumptions (e.g., fully vs. partially observable).
With no fewer than 43 publications in the space, it can be overwhelming to
understand what approach could or should be applied in a new setting. We
present a holistic characterization of the action model acquisition space and
further introduce a unifying framework for automated action model acquisition.
We have re-implemented some of the landmark approaches in the area, and our
characterization of all the techniques offers deep insight into the research
opportunities that remain; i.e., those settings where no technique is capable
of solving.
| [
{
"version": "v1",
"created": "Tue, 14 Jun 2022 00:18:12 GMT"
}
] | 1,655,251,200,000 | [
[
"Callanan",
"Ethan",
""
],
[
"De Venezia",
"Rebecca",
""
],
[
"Armstrong",
"Victoria",
""
],
[
"Paredes",
"Alison",
""
],
[
"Chakraborti",
"Tathagata",
""
],
[
"Muise",
"Christian",
""
]
] |
2206.06618 | Harshad Khadilkar | Harshad Khadilkar | Solving the capacitated vehicle routing problem with timing windows
using rollouts and MAX-SAT | 6 pages, 2 figures | null | null | null | cs.AI | http://creativecommons.org/licenses/by-nc-nd/4.0/ | The vehicle routing problem is a well known class of NP-hard combinatorial
optimisation problems in literature. Traditional solution methods involve
either carefully designed heuristics, or time-consuming metaheuristics. Recent
work in reinforcement learning has been a promising alternative approach, but
has found it difficult to compete with traditional methods in terms of solution
quality. This paper proposes a hybrid approach that combines reinforcement
learning, policy rollouts, and a satisfiability solver to enable a tunable
tradeoff between computation times and solution quality. Results on a popular
public data set show that the algorithm is able to produce solutions closer to
optimal levels than existing learning based approaches, and with shorter
computation times than meta-heuristics. The approach requires minimal design
effort and is able to solve unseen problems of arbitrary scale without
additional training. Furthermore, the methodology is generalisable to other
combinatorial optimisation problems.
| [
{
"version": "v1",
"created": "Tue, 14 Jun 2022 06:27:09 GMT"
}
] | 1,655,251,200,000 | [
[
"Khadilkar",
"Harshad",
""
]
] |
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