Perspectives on Artificial Intelligence Planning

Abstract

Planning is a key area in Artificial Intelligence. In its general form, planning is concerned with the automatic synthesis of action strategies (plans) from a description of actions, sensors, and goals. Planning thus contrasts with two other approaches to intelligent behavior: the programming approach, where action strategies are defined by hand, and the learning approach, where action strategies are inferred from experience. Different assumptions about the nature of actions, sensors, and costs lead to various forms of planning: planning with complete information and deterministic actions (classical planning), planning with non-deterministic actions and sensing, planning with temporal and concurrent actions, etc. Most work so far has been devoted to classical planning, where significant changes have taken place in the last few years. On the methodological side, the area has become more empirical, on the technical side, approaches based on heuristic or constrained-based search have become common. In this paper, I try to provide a coherent picture of Planning in AI, making emphasis on the mathematical models that underlie various forms of planning and the ideas that have been found most useful computationally.