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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", "" ] ]