Research on advice-taking in artificial intelligence is motivated by the promise of knowledge-based systems that can accept high-level, human-like instruction [11]. Examining the activity of human advice-taking is a way of determining the key computational problems that a fully automated advice taker must solve. In this paper, we identify three features of human advice-taking that pose computational problems, and address them in the context of brainstormer, a planning system that takes advice in the domain of terrorist crisis management.
