Planning while learning operators
Xuemei Wang · 1996
This paper describes issues that arise when integrating a planner with a system that learns planning operators incrementally, and our approaches to address these issues. During learning, domain knowledge can be incomplete and incorrect in different ways; therefore the planner must be able to use incomplete domain knowledge. This presents the following challenges for planning: How should the planner effectively generate plans using incomplete and incorrect domain knowledge? How should the planner repair plans upon execution failures? How should planning, learning, and execution be integrated? This paper describes how we address these challenges in the framework of an integrated system, called OBSERVER, that learns planning operators automatically and incrementally. In OBSERVER, operators are learned by observing expert agents and by practicing in a learning-by-doing paradigm. We present empirical results to demonstrate the validity of our approach in a process planning domain. These results show that practicing using our algorithms for planning with incomplete information and plan repair contributes significantly to the learning process. Planning While Learning Operators Acquiring and maintaining domain knowledge is a key bottleneck in fielding planning systems (Chien et al. 1995). Our approach (Wang 1995) to address this issue is to learn planning operators automatically and incrementaUy by observing expert agents and to refine the operators by practicing with them in a learning-by-doing paradigm (Anzai Simon 1979). Unfortunately, while learning, the operators can be incomplete and incorrect. The planning system must