Paper Info
Title
A statistical model checker for situation calculus based multi-agent models
Abstract
In this paper we introduce a new approach for multi-agent simulation and statistical model checking that combines the well-established situation calculus with a first order version of bounded linear time logic (BLTL). This creates a fully integrated solution for specifying system behavior and requirements within the same logical framework. We realized the approach in an extensible tool that combines the benefits of constraint logic programming with the versatility of Python and its ecosystem. First experiments show that the approach is applicable to a wide range of problems and that altogether a more flexible modeling-verification workflow is achieved than in most existing solutions.
Year
DOI
Venue
2014
10.5555/2615731.2616065
AAMAS
Keywords
Field
DocType
bounded linear time logic,integrated solution,extensible tool,order version,flexible modeling-verification workflow,existing solution,logical framework,multi-agent simulation,constraint logic programming,multi-agent model,statistical model checker,new approach
Situation calculus,Computer science,Theoretical computer science,Multi-agent system,Statistical model,Constraint logic programming,Time complexity,Workflow,Logical framework,Python (programming language)
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
1
Name
Order
Citations
PageRank
Christian Kroiss1303.00