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

共 1492 条记录，以下是81-90 订阅

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Gradient-Based Supported Model Computation in Vector Spaces 16th

Gradient-Based Supported Model Computation in Vector Spaces

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16th international conference on logic programming and Non-Monotonic reasoning (LPNMR)

作者： Takemura, Akihiro Inoue, Katsumi Grad Univ Adv Studies Dept Informat SOKENDAI Tokyo Japan Natl Inst Informat Tokyo Japan

ISBN: (纸本)9783031157073;9783031157066

We propose a method for computing supported models of normal logic programs in vector spaces using gradient information. First, the program is translated into a definite program and embedded into a matrix representing the program. We introduce a loss function based on the implementation of the immediate consequence operator T-P by matrix-vector multiplication with a suitable thresholding function, and we incorporate regularization terms into the loss function to avoid undesirable results. the proposed thresholding operation is an almost everywhere differentiable alternative to the non-linear thresholding operation. We report the results of several experiments where our method shows promising performance when used with adaptive gradient update.

关键词： logic programming Supported Model Computation Differentiable logic programming

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Learning to Reason Assisted by Automated reasoning 15th

Learning to Reason Assisted by Automated Reasoning

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15th international conference on Intelligent Computer Mathematics (CICM) part of the Computational logic Autumn Summit (CLAS)

作者： Windsteiger, Wolfgang Johannes Kepler Univ Linz JKU Res Inst Symbol Computat RISC Altenbergerstr 69 A-4040 Linz Austria

ISBN: (纸本)9783031166815;9783031166808

We report on using logic software in a novel course-format for an undergraduate logic course for students in computer science or artificial intelligence. Although being designed as the students' basic introduction to the field of logic, the course features a novel structure and it adds some modern content, such as SAT and SMT solving, to the traditional and established topics, such as propositional logic and first order predicate logic. the novel course design is characterized by, among others, the integration of existing logic software into the teaching of logic. In this paper we focus on the module on first-order predicate logic and the use of the theorema system as a proof-tutor for the students. We report on statistical evaluation of data collected over two consecutive years of teaching this course. On the one hand, we asked for feedback of students on how helpful they felt the software support was. On the other hand, we evaluated their results in the exams during the course and their development over the entire teaching period. the performance in exams is then correlated with students' own perception of the helpfulness of software.

关键词： theorema Automated theorem proving Teaching logic

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Leveraging Neurosymbolic AI for Slice Discovery 18th

Leveraging Neurosymbolic AI for Slice Discovery

引用

18th international conference on Neural-Symbolic Learning and reasoning (NeSy)

作者： Collevati, Michele Eiter, thomas Higuera, Nelson Tech Univ Wien Inst Log & Computat Favoritenstr 9-11 A-1040 Vienna Austria

ISBN: (纸本)9783031711664;9783031711671

While remarkable recent developments in deep neural networks have significantly contributed to advancing the state-of-the-art in Computer Vision (CV), several studies have also shown their limitations and defects. In particular, CV models often make systematic errors on important subsets of data called slices, which are groups of data sharing a set of attributes. the slice discovery problem involves detecting semantically meaningful slices on which the model performs poorly, called rare slices. We propose a modular Neurosymbolic AI approach whose distinct advantage is the extraction of human-readable logical rules that describe rare slices, and thus enhances explainability of CV models. To this end, we present a methodology to induce rare slice occurrences in a model. Experiments on datasets from our data generator leveraging on Super-CLEVR show that the approach can correctly identify rare slices and produce logical rules describing them. the rules can be fruitfully used to generate new training data to mend model behavior or may be integrated into the model to enhance its inference capabilities. (the code for reproducing our experiments is available as an online repository: https://***/kbs/nesy- ai/ilp4sd).

关键词： Neurosymbolic AI Slice Discovery Inductive logic programming

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A Practical Account into Counting Dung's Extensions by Dynamic programming 16th

A Practical Account into Counting Dung's Extensions by Dynam...

引用

16th international conference on logic programming and Non-Monotonic reasoning (LPNMR)

作者： Dewoprabowo, Ridhwan Fichte, Johannes Klaus Gorczyca, Piotr Jerzy Hecher, Markus Tech Univ Dresden Dresden Germany TU Wien Vienna Austria

ISBN: (纸本)9783031157073;9783031157066

A Abstract argumentation and Dung's framework are popular for modeling and evaluating arguments in artificial intelligence. We consider various counting problems in abstract argumentation under practical aspects. We revisit algorithms and establish a framework that employs dynamic programming on tree decompositions for counting extensions of abstract argumentation frameworks under admissible, stable, and complete semantics. We provide an empirical evaluation and investigate conditions under which our approach is useful.

关键词： Dynamic programming

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A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection 16th

A Machine Learning System to Improve the Performance of ASP ...

引用

16th international conference on logic programming and Non-Monotonic reasoning (LPNMR)

作者： Liu, Liu Truszczynski, Mirek Lierler, Yuliya Univ Kentucky Lexington KY 40506 USA Univ Nebraska Omaha NE 68182 USA

ISBN: (纸本)9783031157073;9783031157066

Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving.

关键词： answer set programming encoding selection machine learning

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Modal logic S5 in Answer Set programming with Lazy Creation of Worlds 16th

Modal Logic S5 in Answer Set Programming with Lazy Creation ...

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16th international conference on logic programming and Non-Monotonic reasoning (LPNMR)

作者： Alviano, Mario Batsakis, Sotiris Baryannis, George Univ Calabria Arcavacata Di Rende Italy Tech Univ Crete Iraklion Greece Univ Huddersfield Sch Comp & Engn Huddersfield W Yorkshire England

ISBN: (纸本)9783031157073;9783031157066

Modal logic S5 is used extensively for representing knowledge that includes statements about necessity and possibility, owing to its simplicity in handling chained modal operators. Significant research effort has been devoted in developing efficient reasoning mechanisms over complex S5 formulas, resulting in various solvers taking advantage of the boolean satisfiability problem (SAT). Among them, the most performant solver implements a heuristic for identifying worlds that can be merged, hence reducing the size of SAT instances to be checked. Recently, Answer Set programming (ASP) has also been considered, and different ASP encodings were proposed and tested, reaching state-of-the-art performance. In particular, a heuristic for identifying the propositional atoms that are relevant in every world resulted in a performance gain in previous experiments. this work addresses the open question of whether the aforementioned two heuristics can be combined, as well as possibly enabling lazy instantiation of the resulting encodings, and what their potential impact is on the performance of the ASP-based solver. Experiments show that lazy creation of worlds provides some further performance gain to the ASP-based solver on the tested instances.

关键词： Modal logic S5 Answer Set programming

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Explaining Optimal Trajectories 7th

Explaining Optimal Trajectories

引用

7th international Joint conference on Rules and reasoning (RuleML+RR)

作者： Rouveirol, Celine Aoual, Malik Kazi Soldano, Henry Ventos, Veronique Nukkai Paris France Univ Sorbonne Paris Nord CNRS UMR 7030 Inst GalileeLIPN Villetaneuse France ISYEB Museum Natl Hist Nat CNRS UMR 7205 Paris France

ISBN: (纸本)9783031450716;9783031450723

We propose a definition of common explanation for the label shared by a group of observations described as first order interpretations, and provide algorithms to enumerate minimal common explanations. this was motivated by explaining how performing some action, for instance a card played during a card game play, results in winning a maximum total reward at the end of the trajectory. As there are various ways to reach this reward, each associated to a group of trajectories, we propose to first build groups of trajectories and then build minimal common explanations for each group. the whole method is illustrated on a simplified Bridge game.

关键词： Abductive Explanation Inductive logic programming Markov Decision Process

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Synthetic Datasets and Evaluation Tools for Inductive Neural reasoning 1

引用

30th international conference on Inductive logic programming (ILP) held as part of the 1st international Joint conference on Learning and reasoning (IJCLR)

作者： Cornelio, Cristina thost, Veronika Samsung AI Cambridge England IBM Res MIT IBM Watson AI Lab Ruschlikon Switzerland

ISBN: (数字)9783030974541

ISBN: (纸本)9783030974541;9783030974534

logical rules are a popular knowledge representation language in many domains. Recently, neural networks have been proposed to support the complex rule induction process. However, we argue that existing datasets and evaluation approaches are lacking in various dimensions;for example, different kinds of rules or dependencies between rules are neglected. Moreover, for the development of neural approaches, we need large amounts of data to learn from and adequate, approximate evaluation measures. In this paper, we provide a tool for generating diverse datasets and for evaluating neural rule learning systems, including novel performance metrics.

关键词： Learning systems

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Analyzing Termination for Prev-Aware Fragments of Communicating Datalog Programs 7th

Analyzing Termination for Prev-Aware Fragments of Communicat...

引用

7th international Joint conference on Rules and reasoning (RuleML+RR)

作者： Di Cosmo, Francesco Free Univ Bozen Bolzano Bolzano Italy

ISBN: (纸本)9783031450716;9783031450723

Communicating Datalog Programs (CDPs) are a distributed computing model grounded on logic programming: networks of nodes perform Datalog-like computations, leveraging on information coming from incoming messages and databases received from external services. In previous works, the decidability and complexity border of verification for different variants of CDPs was charted. In general, the problem is undecidable, but model-checking of CTL formulas specialized to the data-centric and distributed setting is decidable for CDPs where all data-sources, except the external inputs, are bounded. An intuitive explanation is that "a bounded state is unable to fully take advantage of an unbounded input", a formal justification is missing. However, we note that traditional CDPs have a limited capability of handling external inputs, i.e., they cannot directly compare two successive inputs or messages. thus, an alternative explanation is that an unbounded data-source does per se not cause undecidability, as long as the CDP cannot compare two successive instances.

关键词： logic programming

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Representing Abstract Dialectical Frameworks with Binary Decision Diagrams 16th

Representing Abstract Dialectical Frameworks with Binary Dec...

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16th international conference on logic programming and Non-Monotonic reasoning (LPNMR)

作者： Ellmauthaler, Stefan Gaggl, Sarah Alice Rusovac, Dominik Wallner, Johannes P. Tech Univ Dresden Cfaed Knowledge Based Syst Grp Dresden Germany Tech Univ Dresden Log Programming & Argumentat Grp Dresden Germany Graz Univ Technol Inst Software Technol Graz Austria

ISBN: (纸本)9783031157073;9783031157066

A Abstract dialectical frameworks (ADFs) are a well-studied generalisation of the prominent argumentation frameworks due to Phan Minh Dung. In this paper we propose to use reduced ordered binary decision diagrams (RoBDDs) as a suitable representation of the acceptance conditions of arguments within ADFs. We first show that computational complexity of reasoning on ADFs represented by RoBDDs is milder than in the general case, with a drop of one level in the polynomial hierarchy. Furthermore, we present a framework to systematically define heuristics for search space exploitation, based on easily retrievable properties of RoBDDs and the recently proposed approach of weighted faceted navigation for answer set programming. Finally, we present preliminary experiments of an implementation of our approach showing promise both when compared to state-of-the-art solvers and when developing heuristics for reasoning.

关键词： Abstract dialectical frameworks Binary decision diagrams

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