Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems

Model Checking Probabilistic Epistemic Logic for Probabilistic Multiagent Systems

Chen Fu, Andrea Turrini, Xiaowei Huang, Lei Song, Yuan Feng, Lijun Zhang

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4757-4763. https://doi.org/10.24963/ijcai.2018/661

In this work we study the model checking problem for probabilistic multiagent systems with respect to the probabilistic epistemic logic PETL, which can specify both temporal and epistemic properties. We show that under the realistic assumption of uniform schedulers, i.e., the choice of every agent depends only on its observation history, PETL model checking is undecidable. By restricting the class of schedulers to be memoryless schedulers, we show that the problem becomes decidable. More importantly, we design a novel algorithm which reduces the model checking problem into a mixed integer non-linear programming problem, which can then be solved by using an SMT solver. The algorithm has been implemented in an existing model checker and experiments are conducted on examples from the IPPC competitions.
Keywords:
Planning and Scheduling: Planning with Incomplete information
Agent-based and Multi-agent Systems: Formal Verification, Validation and Synthesis
Knowledge Representation and Reasoning: Knowledge Representation and Reasoning for Game Playing