Plan Recognition as Candidate Space Search

Abstract

Effective human computer interaction is enhanced by a machine's ability to make educated guesses about the intention of its user. In our research, we have developed a novel plan recognition algorithm — based on plan space search planners — to recognize plans given a limited set of observed actions. Our focus in this research is towards accurately picking possible plans and not towards disambiguation or building plan libraries and therefore we complement other advances in this field, namely probability based recognition and other plan library based recognition systems. Along with the ability to recognize overall goal of an agent our algorithm also allows us to make local predictions, a feature absent in most of the other system.