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 aknowledge 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.

• Type: Conference
• Year: 1994
• Where: AAAI
• Authors: Ronald Fagin, Joseph Y. Halpern, Yoram Moses, Moshe Y. Vardi
• Keywords: AAAI 1994, Agent's Knowledge, Intelligent Agents, Knowledge Bases, Knowledge Representation