Solving graded/probabilistic modal logic via linear inequalities (system description)

William Snell*, Dirk Pattinson, Florian Widmann

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

We present the experience gained from implementing a new decision procedure for both graded and probabilistic modal logic. While our approach uses standard tableaux for propositional connectives, modal rules are given by linear constraints on the arguments of operators. The implementation uses binary decision diagrams for propositional connectives and a linear programming library for the modal rules. We compare our implementation, for graded modal logic, with other tools, showing average performance. Due to lack of other implementations, no comparison is provided for probabilistic modal logic, the main new feature of our implementation.

Original languageEnglish
Title of host publicationLogic for Programming, Artificial Intelligence, and Reasoning - 18th International Conference, LPAR-18, Proceedings
Pages383-390
Number of pages8
DOIs
Publication statusPublished - 2012
Externally publishedYes
Event18th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning, LPAR-18 - Merida, Venezuela, Bolivarian Republic of
Duration: 11 Mar 201215 Mar 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7180 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Logic for Programming, Artificial Intelligence, and Reasoning, LPAR-18
Country/TerritoryVenezuela, Bolivarian Republic of
CityMerida
Period11/03/1215/03/12

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