Reasoning about knowledge to understand distributed AI systems

The use of reasoning about agent knowledge to understand computation in distributed artificial intelligence (DAI) systems is explored. In particular, the negotiation model and the blackboard model are examined, using a temporal, epistemic logic to characterize knowledge and its evolution among interacting agents in these systems. For negotiation, knowledge that agents require to commit consistently to the outcome is determined; also derived are communication requirements for attaining that knowledge in several computational settings. For the blackboard model, the kinds of knowledge statements that can be built up via communication during a distributed computation, independent of the application domain, is discussed. It is suggested that reasoning about knowledge can help one to understand the role of communication in achieving coherence and coordination. The utility of reasoning about knowledge in DAI systems is considered