Reasoning about knowledge
Model checking
PRISM: Probabilistic Symbolic Model Checker
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
Verifying epistemic properties of multi-agent systems via bounded model checking
Fundamenta Informaticae - Concurrency specification and programming
Symbolic model checking for temporal-epistemic logics
ACM SIGACT News
Principles of Model Checking (Representation and Mind Series)
Principles of Model Checking (Representation and Mind Series)
A temporal logic for Markov chains
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Automatic verification of knowledge and time with NuSMV
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
An Introduction to MultiAgent Systems
An Introduction to MultiAgent Systems
Verification of epistemic properties in probabilistic multi-agent systems
MATES'09 Proceedings of the 7th German conference on Multiagent system technologies
Temporal verification of probabilistic multi-agent systems
Pillars of computer science
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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Model checking, a formal automatic verification method, has been widely used in multi-agent systems to verify specifications that contain qualitative properties (e.g safety and liveliness) and quantitative properties. Decision making processes based on inherent knowledge are necessary for agents to act appropriately, particularly in uncertain settings. In order to check epistemic (i.e knowledge) and measurable properties in multi-agent systems, we propose a new logic PCTLK, which uses probabilistic, epistemic, and temporal modal operators. We exploit Discrete-Time Markov Chains (DTMC), in which we are able to represent measurable properties with probability, to model uncertainty in multi-agent systems. We extend the formalism of interpreted systems by adding probabilistic features to suit DTMC models and to present the model checking algorithm for our logic. At the end of this paper, we simulate our algorithm using an example of online shopping.