Planning and control in artificial intelligence: A unifying perspective

The problem of selecting actions in environments that are dynamic and not c ompletely predictable or observable is a central problem in intelligent beh avior. In AI, this translates into the problem of designing controllers tha t can map sequences of observations into actions so that certain goals are achieved. Three main approaches have been used in AI for designing such con trollers: the programming approach, where the controller is programmed by h and in a suitable high-level procedural language, the planning approach, wh ere the control is automatically derived from a suitable description of act ions and goals, and the learning approach, where the control is derived fro m a collection of experiences. The three approaches exhibit successes and l imitations. The focus of this paper is on the planning approach. More speci fically, we present an approach to planning based on various state models t hat handle various types of action dynamics (deterministic and probabilisti c) and sensor feedback (null, partial, and complete). The approach combines high-level representations languages for describing actions, sensors, and goals, mathematical models of sequential decisions for making precise the v arious planning tasks and their solutions, and heuristic search algorithms for computing those solutions. The approach is supported by a computational tool we have developed that accepts high-level descriptions of actions, se nsors, and goals and produces suitable controllers. We also present empiric al results and discuss open challenges.