Proceedings:
Proceedings of the AAAI Conference on Artificial Intelligence, 12
Volume
Issue:
Qualitative and Model-Based Reasoning
Track:
Model-Based Reasoning
Downloads:
Abstract:
The standard approach in AI to knowledge representation is to represent an agent’s knowledge symbolically as a collection of formulas, which we can view as a knowledge base. An agent is then said to know a fact if it is provable from the formulas in his knowledge base. Halpern and Vardi advocated a model-theoretic approach to knowledge representation. In this approach, the key step is representing the agent’s knowledge using an appropriate semantic model. Here, we model knowledge bases operationally as multi-agent systems. Our results show that this approach offers significant advantages.
AAAI
Qualitative and Model-Based Reasoning