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Autonomous Agents and Multi-Agent Systems

, Volume 33, Issue 5, pp 628–644 | Cite as

Summarizing agent strategies

  • Ofra AmirEmail author
  • Finale Doshi-Velez
  • David Sarne
Article
  • 40 Downloads

Abstract

Intelligent agents and AI-based systems are becoming increasingly prevalent. They support people in different ways, such as providing users with advice, working with them to achieve goals or acting on users’ behalf. One key capability missing in such systems is the ability to present their users with an effective summary of their strategy and expected behaviors under different conditions and scenarios. This capability, which we see as complementary to those currently under development in the context of “interpretable machine learning” and “explainable AI”, is critical in various settings. In particular, it is likely to play a key role when a user needs to collaborate with an agent, when having to choose between different available agents to act on her behalf, or when requested to determine the level of autonomy to be granted to an agent or approve its strategy. In this paper, we pose the challenge of developing capabilities for strategy summarization, which is not addressed by current theories and methods in the field. We propose a conceptual framework for strategy summarization, which we envision as a collaborative process that involves both agents and people. Last, we suggest possible testbeds that could be used to evaluate progress in research on strategy summarization.

Keywords

Strategy summarization Human–agent interaction Explainable AI 

Notes

Acknowledgements

The research was partially supported by a J.P. Morgan faculty research award and by the Israel Science Foundation (Grant No. 1162/17).

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Authors and Affiliations

  1. 1.Faculty of Industrial Engineering and ManagementTechnion - Israel Institute of TechnologyHaifaIsrael
  2. 2.John Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeUSA
  3. 3.Department of Computer ScienceBar-Ilan UniversityRamat GanIsrael

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