Logic Programming and Nonmonotonic Reasoning

16th International Conference, LPNMR 2022, Genova, Italy, September 5–9, 2022, Proceedings

AvGeorg Gottlob,Daniela Inclezan

Häftad, Engelska, 2022

Del 13416 i serien Lecture Notes in Computer Science

834 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt över 249 kr.

Beskrivning

This book constitutes the refereed proceedings of the 16th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2022, held in Genova, Italy, in September 2022. The 34 full papers and 5 short papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Technical Contributions; Systems; Applications.Statistical Statements in Probabilistic Logic Programming” and “Efficient Computation of Answer Sets via SAT Modulo Acyclicity and Vertex Elimination” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.Chapters “Statistical Statements in Probabilistic Logic Programming” and “Efficient Computation of Answer Sets via SAT Modulo Acyclicity and Vertex Elimination” are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

Produktinformation

Utgivningsdatum:2022-08-27
Mått:155 x 235 x 30 mm
Vikt:814 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:512
Förlag:Springer International Publishing AG
ISBN:9783031157066

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Programmeringsböcker inom Data och IT

Innehållsförteckning

Technical Contributions.- Syntactic ASP Forgetting with Forks.- Modal Logic S5 in Answer Set Programming with Lazy Creation of Worlds.- Enumeration of minimal models and MUSes in WASP.- Statistical Statements in Probabilistic Logic Programming.- A Comparative Study of Three Neural-Symbolic Approaches to Inductive Logic Programming.- A Definition of Sceptical Semantics in the Constellations Approach.- SHACL: A Description Logic in Disguise.- Tunas - Fishing for diverse Answer Sets: a Multi-Shot Trade up Strategy.- Emotional Reasoning in an Action Language for Emotion-Aware Planning.- Metric Temporal Answer Set Programming over Timed Traces.- Epistemic Logic Programs: a study of some properties.- 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.- 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.- Systems.- Solving Problems in the Polynomial Hierarchy with ASP(Q).- 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.- QMaxSATpb: A Certified MaxSAT Solver.- Applications.- 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.