Logics of time and computation
Logics of time and computation
Temporal logics in AI: semantical and ontological considerations
Artificial Intelligence
Handbook of theoretical computer science (vol. B)
Model checking vs. theorem proving: a manifesto
Artificial intelligence and mathematical theory of computation
ACM Transactions on Programming Languages and Systems (TOPLAS)
Distributed artificial intelligence
Temporal logic (vol. 1): mathematical foundations and computational aspects
Temporal logic (vol. 1): mathematical foundations and computational aspects
First steps in modal logic
Reasoning about knowledge
Autonomous, model-based diagnosis agents
Autonomous, model-based diagnosis agents
Introduction to Robotics
Epistemic Logic for AI and Computer Science
Epistemic Logic for AI and Computer Science
Strategies in Model-based Diagnosis
Journal of Automated Reasoning
Design and Synthesis of Synchronization Skeletons Using Branching-Time Temporal Logic
Logic of Programs, Workshop
Iterated belief change in multi-agent systems
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Interpreted systems and Kripke models for multiagent systems from a categorical perspective
Theoretical Computer Science
A logic for knowledge, correctness, and real time
CLIMA'04 Proceedings of the 5th international conference on Computational Logic in Multi-Agent Systems
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Intelligent agents must update their knowledge base as they acquire new information about their environment. The modal logic S5n has been designed for representing knowledge bases in societies of agents. Halpern and Vardi have proposed the notion of refinement of S5n Kripke models in order to solve multi-agent problems in which knowledge evolves. We argue that there are some problems with their proposal and attempt to solve them by moving from Kripke models to their corresponding trees. We define refinement of a tree with a formula, show some properties of the notion, and illustrate with the muddy children puzzle. We show how some diagnosis problems in engineering can be modelled as knowledge-based multi-agent systems, and hence how our approach can address them.