An Operational Semantics for Knowledge Bases

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.

Authors

• Ronald Fagin
• Yoram Moses

Keywords

No keywords are indexed for this paper.