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

共 1552 条记录，以下是131-140 订阅

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logic of Differentiable logics: Towards a Uniform Semantics of DL 24th

Logic of Differentiable Logics: Towards a Uniform Semantics ...

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24th international conference on logic for programming, artificial intelligence and reasoning, LPAR 2023

作者： Ślusarz, Natalia Komendantskaya, Ekaterina Daggitt, Matthew L. Stewart, Robert Stark, Kathrin Heriot-Watt University Edinburgh United Kingdom

Differentiable logics (DL) have recently been proposed as a method of training neural networks to satisfy logical specifications. A DL consists of a syntax in which specifications are stated and an interpretation function that translates expressions in the syntax into loss functions. these loss functions can then be used during training with standard gradient descent algorithms. the variety of existing DLs and the differing levels of formality with which they are treated makes a systematic comparative study of their properties and implementations difficult. this paper remedies this problem by suggesting a meta-language for defining DLs that we call the logic of Differentiable logics, or LDL. Syntactically, it generalises the syntax of existing DLs to FOL, and for the first time introduces the formalism for reasoning about vectors and learners. Semantically, it introduces a general interpretation function that can be instantiated to define loss functions arising from different existing DLs. We use LDL to establish several theoretical properties of existing DLs and to conduct their empirical study in neural network verification. Keywords: Differentiable logic, Fuzzy logic, Probabilistic logic, Machine Learning, Training with Constraints, Types. © 2023, EasyChair. All rights reserved.

关键词： Fuzzy logic

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NMR 2022 - Proceedings of the 20th international Workshop on Non-Monotonic reasoning, Part of the Federated logic conference, FLoC 2022

NMR 2022 - Proceedings of the 20th International Workshop on...

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20th international Workshop on Non-Monotonic reasoning, NMR 2022

the proceedings contain 18 papers. the topics discussed include: towards legally and ethically correct online HTN planning for data transfer;repairing ontologies via kernel pseudo-contraction;trust graphs for belief revision: framework and implementation;bipolar argumentation frameworks with explicit conclusions: connecting argumentation and logic programming;conditional syntax splitting, lexicographic entailment and the drowning effect;truth-tracking with non-expert information sources;argumentation frameworks induced by assumption-based argumentation: relating size and complexity;and a situation-calculus model of knowledge and belief based on thinking about justifications.

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Automatic Complexity Analysis of Integer Programs via Triangular Weakly Non-Linear Loops 11th

Automatic Complexity Analysis of Integer Programs via Triang...

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11th international Joint conference on Automated reasoning (IJCAR) part of the Federated logic conference (FLoC)

作者： Lommen, Nils Meyer, Fabian Giesl, Juergen Rhein Westfal TH Aachen LuFG Informat 2 Aachen Germany

ISBN: (纸本)9783031107696;9783031107689

there exist several results on deciding termination and computing runtime bounds for triangular weakly non-linear loops (twn-loops). We show how to use results on such subclasses of programs where complexity bounds are computable within incomplete approaches for complexity analysis of full integer programs. To this end, we present a novel modular approach which computes local runtime bounds for subprograms which can be transformed into twn-loops. these local runtime bounds are then lifted to global runtime bounds for the whole program. the power of our approach is shown by our implementation in the tool KoAT which analyzes complexity of programs where all other state-of-the-art tools fail.

关键词： Integer programming

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Non-associative, Non-commutative Multi-modal Linear logic 11th

Non-associative, Non-commutative Multi-modal Linear Logic

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11th international Joint conference on Automated reasoning (IJCAR) part of the Federated logic conference (FLoC)

作者： Blaisdell, Eben Kanovich, Max Kuznetsov, Stepan L. Pimentel, Elaine Scedrov, Andre Univ Penn Dept Math Philadelphia PA 19104 USA UCL Dept Comp Sci London England Steklov Math Inst RAS Moscow Russia HSE Univ Fac Comp Sci Moscow Russia

ISBN: (纸本)9783031107696;9783031107689

Adding multi-modalities (called subexponentials) to linear logic enhances its power as a logical framework, which has been extensively used in the specification of e.g. proof systems, programming languages and bigraphs. Initially, subexponentials allowed for classical, linear, affine or relevant behaviors. Recently, this framework was enhanced so to allow for commutativity as well. In this work, we close the cycle by considering associativity. We show that the resulting system (acLL(Sigma)) admits the (multi)cut rule, and we prove two undecidability results for fragments/variations of acLL(Sigma).

关键词： Linear algebra

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A Comparison of ASP-Based and SAT-Based Algorithms for the Contension Inconsistency Measure 15th

A Comparison of ASP-Based and SAT-Based Algorithms for th...

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15th international conference on Scalable Uncertainty Management, SUM 2022

作者： Kuhlmann, Isabelle Gessler, Anna Laszlo, Vivien thimm, Matthias Artificial Intelligence Group University of Hagen Hagen Germany

ISBN: (纸本)9783031188428

We propose an algorithm based on satisfiability problem (SAT) solving for determining the contension inconsistency degree in propositional knowledge bases. In addition, we present a revised version of an algorithm based on answer set programming (ASP), which serves the same purpose. In an experimental analysis, we compare the two algorithms to each other, as well as to a naive baseline method. Our results demonstrate that both the SAT and the ASP approach expectedly outperform the baseline algorithm. Further, the revised ASP method is not only superior to the SAT approach, but also to its predecessors from the literature. Hence, it poses a new state of the art. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： logic programming

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ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser 19

ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser

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19th international conference on Principles of Knowledge Representation and reasoning, KR 2022

作者： Eiter, thomas Geibinger, Tobias Higuera, Nelson Musliu, Nysret Oetsch, Johannes Stepanova, Daria Institute for Logic and Computation TU Wien Favoritenstraße 9-11 Vienna1040 Austria CD-Lab Artis TU Wien Austria Bosch Center for AI Robert Bosch Campus 1 Renningen71272 Germany

ISBN: (纸本)9781956792010

We present the system ALASPO which implements Adaptive Large-neighbourhood search for Answer Set programming (ASP) Optimisation. Large-neighbourhood search (LNS) is a meta-heuristic where parts of a solution are destroyed and reconstructed in an attempt to improve an overall objective. ALASPO currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon for difference and full integer constraints, and multi-shot solving for an efficient implementation of the LNS loop. Neighbourhoods can be defined in code or declaratively as part of the ASP encoding. While the method underlying ALASPO has been described in previous work, ALASPO also incorporates portfolios for the LNS operators along with self-adaptive selection strategies as a technical novelty. this improves usability considerably at no loss of solution quality, but on the contrary often yields benefits. To demonstrate this, we evaluate ALASPO on different optimisation benchmarks. © 19th international conference on Principles of Knowledge Representation and reasoning, KR 2022. All rights reserved.

关键词： Optimization

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IPS 2023, RCRA 2023, and SPIRIT 2023 Workshops - Proceedings of the Italian Workshop on Planning and Scheduling, RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and SPIRIT Workshop on Strategies, Prediction, Interaction, and reasoning in Italy, co-located with 22nd international conference of the Italian Association for artificial intelligence, AIxIA 2023

IPS 2023, RCRA 2023, and SPIRIT 2023 Workshops - Proceedings...

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11th Italian Workshop on Planning and Scheduling, 30th RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and 2023 SPIRIT Workshop on Strategies, Prediction, Interaction, and reasoning in Italy, IPS-RCRA-SPIRIT 2023 Workshops

the proceedings contain 21 papers. the topics discussed include: planning safe collaborative behaviors through risk-aware heuristic search;tabular model learning in Monte Carlo tree search;goal recognition with deep learning and embedded representation of state traces;investigating domain-oriented approaches to optimization in timeline-based planning;heuristic planning for hybrid dynamical systems with constraint logic programming;characterizing nexus of similarity between entities;a simple proof-theoretic characterization of stable models;experimenting an approach to neuro-symbolic RL;planning as theorem proving with heuristics;learning augmented online learning algorithms - the adversarial bandit with knapsacks framework;and optimal rates for online Bayesian persuasion.

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Projection as a way of thinking to find factors in factor space 8

Projection as a way of thinking to find factors in factor sp...

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8th international conference on Information Technology and Quantitative Management (ITQM) - Developing Global Digital Economy after COVID-19

作者： Kong, Qi-wei He, Jing Zhang, Zhi-wang Zheng, Hui Wang, Pei-zhuang Nanjing Univ Finance & Econ Inst Informat Engn Nanjing Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Hawthorn Vic Australia Liaoning Tech Univ Inst Intelligence Engn & Math Fuxin Liaoning Peoples R China

With the development of artificial intelligence, statistical learning methods are widely used. Among all artificial intelligence problems, obtaining excellent training models through massive data mining and huge computing power is currently a hot topic. Although such statistical learning methods have shown great potential in academic and industrial applications, the causal logic of machine learning still has poor interpretability. In the factor space theory proposed in 1982, Professor Peizhuang Wang first proposed causal reasoning to find the relationship between causation and effect and created factor space to illustrate causal relationships. Factors play a vital role in the factor space, which can be applied in various scenarios. the projection method provides a way of thinking and perspective for finding factors by projecting the vectors in the n-dimensional real vector space R-n into subspace, and this method has great value in knowledge representation and causal reasoning. (C) 2021 the Authors. Published by Elsevier B.V.

关键词： Factor space artificial intelligence linear programming Hat projection Causal reasoning

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16th international conference on logic programming and Nonmonotonic reasoning, LPNMR 2022

16th International Conference on Logic Programming and Nonmo...

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16th international conference on logic programming and Nonmonotonic reasoning, LPNMR 2022

ISBN: (纸本)9783031157066

the proceedings contain 39 papers. the special focus in this conference is on logic programming and Nonmonotonic reasoning. the topics include: Deep Learning for the Generation of Heuristics in Answer Set programming: A Case Study of Graph Coloring;a Qualitative Temporal Extension of Here-and-there logic;representing Abstract Dialectical Frameworks with Binary Decision Diagrams;Arguing Correctness of ASP Programs with Aggregates;Efficient Computation of Answer Sets via SAT Modulo Acyclicity and Vertex Elimination;IASCAR: Incremental Answer Set Counting by Anytime Refinement;reasoning About Actions with EL Ontologies and Temporal Answer Sets for DLTL;inference to the Stable Explanations;modal logic S5 in Answer Set programming with Lazy Creation of Worlds;Semantics for Conditional Literals via the SM Operator;state Transition in Multi-agent Epistemic Domains Using Answer Set programming;towards Provenance in Heterogeneous Knowledge Bases;Computing Smallest MUSes of Quantified Boolean Formulas;Pinpointing Axioms in Ontologies via ASP;interlinking logic Programs and Argumentation Frameworks;gradient-Based Supported Model Computation in Vector Spaces;towards Causality-Based Conflict Resolution in Answer Set Programs;xASP: An Explanation Generation System for Answer Set programming;Solving Problems in the Polynomial Hierarchy with ASP(Q);Enumeration of Minimal Models and MUSes in WASP;a Practical Account into Counting Dung’s Extensions by Dynamic programming;Clingraph: ASP-Based Visualization;A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection;QMaxSATpb: A Certified MaxSAT Solver;knowledge-Based Support for Adhesive Selection;ASP for Flexible Payroll Management;Analysis of Cyclic Fault Propagation via ASP;learning to Rank the Distinctiveness of Behaviour in Serial Offending;optimising Business Process Discovery Using Answer Set programming;DeduDeep: An Extensible Framework for Combining Deep Learning and ASP-Based Models;statis

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5th international Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th international Workshop on Numerical Software Verification, NSV 2022

5th International Workshop on Software Verification and Form...

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5th international Workshop on Software Verification and Formal Methods for ML-Enables Autonomous Systems, FoMLAS 2022 and 15th international Workshop on Numerical Software Verification, NSV 2022

ISBN: (纸本)9783031212215

the proceedings contain 11 papers. the special focus in this conference is on Software Verification and Formal Methods for ML-Enables Autonomous Systems. the topics include: Neural Network Precision Tuning Using Stochastic Arithmetic;MLTL Multi-type (MLTLM): A logic for reasoning About Signals of Different Types;a Cascade of Checkers for Run-time Certification of Local Robustness;CEG4N: Counter-Example Guided Neural Network Quantization Refinement;minimal Multi-Layer Modifications of Deep Neural Networks;differentiable logics for Neural Network Training and Verification;neural Networks in Imandra: Matrix Representation as a Verification Choice;self-correcting Neural Networks for Safe Classification;formal Specification for Learning-Enabled Autonomous Systems;verified Numerical Methods for Ordinary Differential Equations.

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