Comparing Action-Query Strategies in Semi-Autonomous Agents

Authors

  • Robert Cohn University of Michigan, Ann Arbor
  • Edmund Durfee University of Michigan, Ann Arbor
  • Satinder Singh University of Michigan

Abstract

We consider settings in which a semi-autonomous agent has uncertain knowledge about its environment, but can ask what action the human operator would prefer taking in the current or in a potential future state. Asking queries can improve behavior, but if queries come at a cost (e.g., due to limited operator attention), the value of each query should be maximized. We compare two strategies for selecting action queries: 1) based on myopically maximizing expected gain in long-term value, and 2) based on myopically minimizing uncertainty in the agent's policy representation. We show empirically that the first strategy tends to select more valuable queries, and that a hybrid method can outperform either method alone in settings with limited computation.

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Published

2011-08-04

How to Cite

Cohn, R., Durfee, E., & Singh, S. (2011). Comparing Action-Query Strategies in Semi-Autonomous Agents. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1102-1107. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7992