Asynchronous knowledge with hidden actions in the situation calculus

We present a powerful new account of multi-agent knowledge in the situation calculus and an automated reasoning procedure for knowledge queries. Existing accounts of epistemic reasoning in the situation calculus require that whenever an action occurs, all agents know that an action has occurred. This demands a level of synchronicity that is unreasonable in many multi-agent domains. In asynchronous domains, each agent’s knowledge must instead account for arbitrarily-long sequences of hidden actions. By using a persistence condition meta-operator to augment traditional regression techniques, we show how agents can reason about their own knowledge using only their internal history of observations, rather than requiring a full history of the world. The result is a more robust and flexible account of knowledge in the situation calculus suitable for asynchronous, partially-observable multi-agent domains.

• Type: Journal
• Year: 2015
• Where: AI
• Authors: Ryan F. Kelly, Adrian R. Pearce
• Keywords: AI 2015, Artificial Intelligence