Reasoning about knowledge
Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework
Autonomous Agents and Multi-Agent Systems
Dispersion games: general definitions and some specific learning results
Eighteenth national conference on Artificial intelligence
Symbolic Model Checking the Knowledge of the Dining Cryptographers
CSFW '04 Proceedings of the 17th IEEE workshop on Computer Security Foundations
PAT: Towards Flexible Verification under Fairness
CAV '09 Proceedings of the 21st International Conference on Computer Aided Verification
Fair Model Checking with Process Counter Abstraction
FM '09 Proceedings of the 2nd World Congress on Formal Methods
Model checking hierarchical probabilistic systems
ICFEM'10 Proceedings of the 12th international conference on Formal engineering methods and software engineering
Symbolic model checking of probabilistic knowledge
Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
Verifying team formation protocols with probabilistic model checking
CLIMA'11 Proceedings of the 12th international conference on Computational logic in multi-agent systems
PRISM: a tool for automatic verification of probabilistic systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
MCMAS: a model checker for multi-agent systems
TACAS'06 Proceedings of the 12th international conference on Tools and Algorithms for the Construction and Analysis of Systems
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Multi-agent systems, which are composed of autonomous agents, have been successfully employed as a modeling paradigm in many scenarios. However, it is challenging to guarantee the correctness of their behaviors due to the complex nature of the autonomous agents, especially when they have stochastic characteristics. In this work, we propose to apply probabilistic model checking to analyze multi-agent systems. A modeling language called PMA is defined to specify such kind of systems, and LTL property and logic of knowledge combined with probabilistic requirements are supported to analyze system behaviors. Initial evaluation indicates the effectiveness of our current progress; meanwhile some challenges and possible solutions are discussed as our ongoing work.