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检索条件"任意字段=15th International Conference on Logic for Programming Artificial Intelligence and Reasoning"

共 1549 条记录，以下是161-170 订阅

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Stream reasoning approach for Anticipating Human Activities in Ambient intelligence environments

Stream Reasoning approach for Anticipating Human Activities ...

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international conference on Tools for artificial intelligence (ICTAI)

作者： K. Moulouel M. Arbane A. Chibani G. Khodabandelou Y. Amirat Univ Paris Est Creteil LISSI Vitry Vitry-sur-Seine France

the advent of the internet of things (IoT) and artificial intelligence (AI) technologies enable continuous data generation, also known as data streams. Considering data streams is required for ambient intelligence (AmI) systems for context-aware assistance services. this paper proposes a hybrid approach combining deep learning models and probabilistic commonsense reasoning over data streams to anticipate human activities. the reasoning is performed using the event calculus formulated in answer set programming (ECASP); the latter allows for abductive and temporal reasoning, which enables an eXplainable AI (XAI) approach. An activity context ontology is exploited for the reasoning axiomatisation. Several experiments were conducted to demonstrate the effectiveness of the proposed approach in terms of accuracy and computation time.

关键词： Deep learning Computational modeling Ontologies programming Probabilistic logic Data models Ambient intelligence

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Answer-Set programming for Lexicographical Makespan Optimisation in Parallel Machine Scheduling 18

Answer-Set Programming for Lexicographical Makespan Optimisa...

引用

18th international conference on Principles of Knowledge Representation and reasoning, KR 2021

作者： Eiter, thomas Geibinger, Tobias Musliu, Nysret Oetsch, Johannes Skočovský, Peter Stepanova, Daria Institute for Logic and Computation TU Wien Favoritenstraße 9-11 ViennaA-1040 Austria CD-Lab Artis TU Wien Robert Bosch Campus 1 Renningen D-71272 Germany Bosch Center for AI Robert Bosch Campus 1 Renningen D-71272 Germany

ISBN: (纸本)9781956792997

We deal with a challenging scheduling problem on parallel-machines with sequence-dependent setup times and release dates from a real-world application of semiconductor workshop production. there, jobs can only be processed by dedicated machines, thus few machines can determine the makespan almost regardless of how jobs are scheduled on the remaining ones. this causes problems when machines fail and jobs need to be rescheduled. Instead of optimising only the makespan, we put the individual machine spans in non-ascending order and lexicographically minimise the resulting tuples. this achieves that all machines complete as early as possible and increases the robustness of the schedule. We study the application of Answer-Set programming (ASP) to solve this problem. While ASP eases modelling, the combination of timing constraints and the considered objective function challenges current solving technology. the former issue is addressed by using an extension of ASP by difference logic. For the latter, we devise different algorithms that use multi-shot solving. To tackle industrial-sized instances, we study different approximations and heuristics. Our experimental results show that ASP is indeed a promising KRR paradigm for this problem and is competitive with state-of-the-art CP and MIP solvers. Copyright © 2021, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Job shop scheduling

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Determining Action Reversibility in STRIPS Using Answer Set programming with Quantifiers 24th

Determining Action Reversibility in STRIPS Using Answer Set...

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24th international conference on Practical Aspects of Declarative Languages, PADL 2022

作者： Faber, Wolfgang Morak, Michael Chrpa, Lukáš University of Klagenfurt Klagenfurt Austria Czech Technical University in Prague Prague Czech Republic

ISBN: (纸本)9783030944780

In the field of automated planning, an action is called reversible when other actions can be applied in order to revert the effects of this action and return to the original state. In recent years, there has been renewed interest in this topic, which led to novel results in the widely known STRIPS formalism and the PDDL planning language. In this paper, we aim to solve the computational problem of deciding action reversibility in a practical setting, applying recent advances in the field of logic programming. In particular, a quantified extension of Answer Set programming (ASP) named ASP with Quantifiers (ASP(Q)) has been proposed by Amendola, Ricca, and Truszczynski, which allows for stacking logic programs by quantifying over answer sets of the previous layer. this language is well-suited to express encodings for the action reversibility problem, since this problem naturally contains a quantifier alternation. In addition, a prototype solver for ASP(Q) is currently developed. We make use of the ASP(Q) language to offer an encoding for action reversibility, and then report on preliminary benchmark results on how well this encoding performs compared to classical ASP. © 2022, Springer Nature Switzerland AG.

关键词： Answer set programming Automated planning reasoning about action and change

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Existential Abstraction on Argumentation Frameworks via Clustering 18

Existential Abstraction on Argumentation Frameworks via Clus...

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18th international conference on Principles of Knowledge Representation and reasoning, KR 2021

作者： Saribatur, Zeynep G. Wallner, Johannes P. Institute of Logic and Computation TU Wien Austria Institute of Software Technology Graz University of Technology Austria

ISBN: (纸本)9781956792997

Argumentation in artificial intelligence (AI) builds on formal approaches to reasoning argumentatively. Common to many such approaches is to use argumentation frameworks (AFs) as reasoning engines, with AFs being composed of arguments and attacks between arguments, which are instantiated from knowledge bases in a principle-based manner. While representing what can be argued for in an AF provides a conceptually clean way, this process can face challenges arising from generating a large number of arguments, which can act as a barrier to explainability. Inspired by successful approaches to model checking where the state explosion is mitigated by applying existential abstraction, we study an adaption of existential abstraction in form of clustering arguments in an AF to address an associated"argument explosion". In this paper, we provide a foundational investigation of this form of existential abstraction by defining semantics of the resulting clustered AFs, which balance two inherent aspects of existential abstractions: abstracting from concrete AFs and not permitting too much spuriousness (i.e., conclusions that hold on the abstraction but not on the original AF). Moreover, we show properties of clustered AFs, including complexity results, discuss use of clusterings for explaining results of reasoning tasks, and employ the recently introduced methodology of abstraction in answer set programming (ASP) for obtaining and reasoning over clustered AFs. Copyright © 2021, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Abstracting

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Approximate Inference for Neural Probabilistic logic programming 18

Approximate Inference for Neural Probabilistic Logic Program...

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18th international conference on Principles of Knowledge Representation and reasoning, KR 2021

作者： Manhaeve, Robin Marra, Giuseppe De Raedt, Luc KU Leuven Dept. of Computer Science Leuven.AI Belgium Örebro University Sweden

ISBN: (纸本)9781956792997

DeepProbLog is a neural-symbolic framework that integrates probabilistic logic programming and neural networks. It is realized by providing an interface between the probabilistic logic and the neural networks. Inference in probabilistic neural symbolic methods is hard, since it combines logical theorem proving with probabilistic inference and neural network evaluation. In this work, we make the inference more efficient by extending an approximate inference algorithm from the field of statistical-relational AI. Instead of considering all possible proofs for a certain query, the system searches for the best proof. However, training a DeepProbLog model using approximate inference introduces additional challenges, as the best proof is unknown at the start of training which can lead to convergence towards a local optimum. To be able to apply DeepProbLog on larger tasks, we propose: 1) a method for approximate inference using an A*-like search, called DPLA* 2) an exploration strategy for proving in a neural-symbolic setting, and 3) a parametric heuristic to guide the proof search. We empirically evaluate the performance and scalability of the new approach, and also compare the resulting approach to other neural-symbolic systems. the experiments show that DPLA* achieves a speed up of up to 2-3 orders of magnitude in some cases. Copyright © 2021, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Probabilistic logics

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DEL-based Epistemic Planning for Human-Robot Collaboration: theory and Implementation 18

DEL-based Epistemic Planning for Human-Robot Collaboration: ...

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18th international conference on Principles of Knowledge Representation and reasoning, KR 2021

作者： Bolander, thomas Dissing, Lasse Herrmann, Nicolai Technical University of Denmark Denmark

ISBN: (纸本)9781956792997

Epistemic planning based on Dynamic Epistemic logic (DEL) allows agents to reason and plan from the perspective of other agents. the framework of DEL-based epistemic planning thereby has the potential to represent significant aspects of theory of Mind in autonomous robots, and to provide a foundation for human-robot collaboration in which coordination is achieved implicitly through perspective shifts. In this paper, we build on previous work in epistemic planning with implicit coordination. We introduce a new notion of indistinguishability between epistemic states based on bisimulation, and provide a novel partition refinement algorithm for computing unique representatives of sets of indistinguishable states. We provide an algorithm for computing implicitly coordinated plans using these new constructs, embed it in a perceive-plan-act agent loop, and implement it on a robot. the planning algorithm is benchmarked against an existing epistemic planning algorithm, and the robotic implementation is demonstrated on human-robot collaboration scenarios requiring implicit coordination. Copyright © 2021, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Robot programming

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An Evaluation of Meta-reasoning over OWL 2 QL 5th

An Evaluation of Meta-reasoning over OWL 2 QL

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5th international Joint conference on Rules and reasoning (RuleML+RR)

作者： Qureshi, Haya Majid Faber, Wolfgang Univ Klagenfurt Klagenfurt Austria

ISBN: (纸本)9783030911676;9783030911669

there has been increasing interest in enriching ontologies with meta-modeling and meta-querying for the past few years. Unfortunately, the Direct Semantics for OWL2 and SPARQL does not support meta-constructs in a satisfactory way: While meta-axioms (involving identifiers used both as a class and an individual) can be syntactically expressed using punning, different occurrences of the same identifier will be treated as different entities. For example, GoldenEagle used as an instance of EndangeredSpecies and also as a class containing individuals, will be treated as two separate, unrelated entities. Metaqueries (for example, asking for classes that also occur as individuals) are not allowed at all in SPARQL under the Direct Semantics Entailment Regime. To overcome this, a new semantic flavour for SPARQL, called Meta-modeling Semantics Entailment Regime (MSER), has been introduced. In previous work, Cima et al. have proposed a reduction from OWL 2 QL (a light-weight profile of OWL 2) and associated metaqueries in SPARQL to query answering over Datalog rules. In this paper, we experiment with various logic programming tools that support Datalog querying to determine their suitability as back-ends to MSER query answering. these tools stem from different logic programming paradigms (Prolog, pure Datalog, Answer Set programming). Our work shows that the Datalog approach to MSER querying is practical also for sizeable ontologies.

关键词： Meta-reasoning SPARQL Datalog

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Functorial semantics as a unifying perspective on logic programming 9

Functorial semantics as a unifying perspective on logic prog...

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9th conference on Algebra and Coalgebra in Computer Science, CALCO 2021

作者： Gu, Tao Zanasi, Fabio University College London United Kingdom

ISBN: (纸本)9783959772129

logic programming and its variations are widely used for formal reasoning in various areas of Computer Science, most notably artificial intelligence. In this paper we develop a systematic and unifying perspective for (ground) classical, probabilistic, weighted logic programs, based on categorical algebra. Our departure point is a formal distinction between the syntax and the semantics of programs, now regarded as separate categories. then, we are able to characterise the various variants of logic program as different models for the same syntax category, i.e. structure-preserving functors in the spirit of Lawvere’s functorial semantics. As a first consequence of our approach, we showcase a series of semantic constructs for logic programming pictorially as certain string diagrams in the syntax category. Secondly, we describe the correspondence between probabilistic logic programs and Bayesian networks in terms of the associated models. Our analysis reveals that the correspondence can be phrased in purely syntactical terms, without resorting to the probabilistic domain of interpretation. © Tao Gu and Fabio Zanasi.

关键词： logic programming

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Towards a Unified View on logics for Uncertainty 15th

Towards a Unified View on Logics for Uncertainty

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15th international conference on Scalable Uncertainty Management, SUM 2022

作者： Corsi, Esther Anna Flaminio, Tommaso Hosni, Hykel Department of Philosophy University of Milan Via Festa del Perdono 7 Milano20122 Italy Campus UAB Bellaterra08193 Spain

ISBN: (纸本)9783031188428

In the present paper, we propose a general logical approach for reasoning about probability functions, belief functions, lower probabilities and the corresponding duals. the logical setting we consider combines the modal logic S5, Lukasiewicz logic and an additional modality P that applied to boolean formulas formalises probability functions. the modality P together with an S5 modal provides a language rich enough to characterise probability, belief and lower probability theories. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fuzzy logic

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Faith and fate: limits of transformers on compositionality 23

Faith and fate: limits of transformers on compositionality

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Nouha Dziri Ximing Lu Melanie Sclar Xiang Lorraine Li Liwei Jiang Bill Yuchen Lin Peter West Chandra Bhagavatula Ronan Le Bras Jena D. Hwang Soumya Sanyal Sean Welleck Xiang Ren Allyson Ettinger Zaid Harchaoui Yejin Choi Allen Institute for Artificial Intelligence Allen Institute for Artificial Intelligence and University of Washington University of Washington University of Southern California Allen Institute for Artificial Intelligence and University of Southern California Allen Institute for Artificial Intelligence and University of Chicago

Transformer large language models (LLMs) have sparked admiration for their exceptional performance on tasks that demand intricate multi-step reasoning. Yet, these models simultaneously show failures on surprisingly trivial problems. this begs the question: Are these errors incidental, or do they signal more substantial limitations? In an attempt to demystify transformer LLMs, we investigate the limits of these models across three representative compositional tasks—multi-digit multiplication, logic grid puzzles, and a classic dynamic programming problem. these tasks require breaking problems down into sub-steps and synthesizing these steps into a precise answer. We formulate compositional tasks as computation graphs to systematically quantify the level of complexity, and break down reasoning steps into intermediate sub-procedures. Our empirical findings suggest that transformer LLMs solve compositional tasks by reducing multi-step compositional reasoning into linearized subgraph matching, without necessarily developing systematic problem-solving skills. To round off our empirical study, we provide theoretical arguments on abstract multi-step reasoning problems that highlight how autoregressive generations' performance can rapidly decay with increased task complexity.

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