Continuous Explanation Generation in a Multi-Agent Domain
Journal article preprint
NAVAL RESEARCH LAB WASHINGTON DC
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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.
- Statistics and Probability
- Human Factors Engineering and Man Machine Systems