Continuous Explanation Generation in a Multi-Agent Domain

Abstract : An agent operating in a dynamic, multi-agent environment with partial observability should continuously generate and maintain an explanation of its observations that describes what is occurring around it. We update our existing formal model of occurrence-based explanations to describe ambiguous explanations and the actions of other agents. We also introduce a new version of DiscoverHistory, an algorithm that continuously maintains such explanations as new observations are received. In our empirical study this version of DiscoverHistory outperformed a competitor in terms of efficiency while maintaining correctness (i.e., precision and recall).