Summarizing agent strategies

摘要

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.