Intention is choice with commitment
Artificial Intelligence
Reasoning about knowledge
AgentSpeak(L): BDI agents speak out in a logical computable language
MAAMAW '96 Proceedings of the 7th European workshop on Modelling autonomous agents in a multi-agent world : agents breaking away: agents breaking away
IEEE Transactions on Software Engineering - Special issue on formal methods in software practice
First-Order Dynamic Logic
An Operational Semantics for a PRS-Like Agent Architecture
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Model Checking Knowledge and Time in Systems with Perfect Recall (Extended Abstract)
Proceedings of the 19th Conference on Foundations of Software Technology and Theoretical Computer Science
Modelling PRS-Like Agents' Mental States
PRICAI '02 Proceedings of the 7th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Verifying epistemic properties of multi-agent systems via bounded model checking
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
A model-theoretic approach to the verification of situated reasoning systems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Reactive reasoning and planning
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
An analysis of three puzzles in the logic of intention
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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The key problem in applying verification techniques such as model checking to agent architectures is to show how to map systematically from an agent program to a model structure that not only includes the possible behaviours of the agent in its environment, but which also captures appropriate mental notions, such as belief, desire and intention, that may be used by the designer to reason about the agent. In this paper, we present an algorithm providing a mapping from agent programs under a simplified PRS-type agent architecture to a reachability graph structure extended to include representations of beliefs, goals and intentions, and illustrate the translation with a simple “waypoint following” agent. We conclude with a brief discussion of the differences between the internal (operational) notion of intention used in the architecture and the formal (external) notion of intention used in the modelling.