An operational semantics for knowledge bases

The standard approach in AI to knowledge representation isto 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 advocateda model-theoretic approach to knowledge representation. In this approach, the key step is representing the agent's knowl-edge using an appropriate semantic model. Here, we model knowledge bases operationally as multi-agent systems. Our results show that this approach offers significant advantages.