"Using Grice's Maxim of Quantity to Select the Content of Plan Descriptions"

6/3/1999: [time not recorded]
[location not recorded]

Abstract: Complex activities, by definition, contain a large amount of detail. When people describe these activities, they very naturally emphasize information they feel is important and leave out information they feel isn't essential. This is an example of obeying what the philosopher Grice calls the Conversational Maxim of Quantity: say no more and no less than what's needed in the given context. The plans produced by AI planning systems are typically quite complex, even for fairly straightforward activities. In order for natural interfaces for describing these types of plans to be designed, a principled way for determining what content to retain and what content to remove must be developed. In this talk, I'll describe the Cooperative Plan Identification (CPI) architecture, an architecture for producing textual descriptions of AI plans. The CPI architecture uses computational interpretations of Grice's Maxim of Quantity to search the space of plan descriptions, selecting descriptions that are at once concise and effective. I'll also describe an empirical evaluation of the CPI architecture in which human subjects carried out instructions produced by the algorithm in a text-based virtual world. The evaluation provides strong evidence that plan descriptions produced by the CPI architecture were more effective than those produced by several competing algorithms.

About Michael Young: Michael Young is an assistant professor in the Department of Computer Science at North Carolina State University. His current work focuses on human and computer collaboration, particularly in virtual worlds. Michael's research deals with formal models of planning and plan recognition, natural language discourse generation, and the development and use of computational models of narrative to describe the structure of human and computer interaction. Before joining the Computer Science faculty at NC State, Michael was a post-doctoral fellow in the Robotics Institute at Carnegie Mellon University, where his research centered on the roles of intelligent systems in contexts where teams of humans and computers collaborate together.

Last updated: Mon Jun 19 17:44:06 2006