Handbook of theoretical computer science (vol. B)
Artificial Intelligence
The complexity of probabilistic verification
Journal of the ACM (JACM)
ACM Transactions on Computational Logic (TOCL)
Model checking multi-agent systems with MABLE
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Tractable multiagent planning for epistemic goals
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Heterogeneous Agent Systems
It Usually Works: The Temporal Logic of Stochastic Systems
Proceedings of the 7th International Conference on Computer Aided Verification
Model checking for probability and time: from theory to practice
LICS '03 Proceedings of the 18th Annual IEEE Symposium on Logic in Computer Science
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
On feasible cases of checking multi-agent systems behavior
Theoretical Computer Science - Logic and complexity in computer science
Automatic verification of probabilistic concurrent finite state programs
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Verification of epistemic properties in probabilistic multi-agent systems
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Model checking epistemic and probabilistic properties of multi-agent systems
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Runtime verification of multi-agent systems interaction quality
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
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Probabilistic systems of interacting intelligent agents are considered. They have two sources of uncertainty: uncertainty of communication channels and uncertainty of actions. We show how such systems can be polynomially transformed to finite state Markov chains. This allows one to transfer known results on verifying temporal properties of the finite state Markov chains to the probabilistic multi-agent systems of the considered type.