Control of Search in AI Planning

In the field of artificial intelligence, ”planning” is defined as designing the behavior of some entity that acts, either an individual, a group, or an organization. The output is some kind of blueprint for behavior, which we call a plan. There are a wide variety of planning problems, differentiated by the types of their inputs and outputs. Typically, planning problems get more and more difficult as more flexible inputs are allowed and fewer constraints on the output are required. As this flexibility increases, the space of possibilities needing to be explored by the planning algorithm grows extremely quickly (usually exponentially). The goal of this workshop was to provide a specific focus on controlling this search. This problem is the essence of the planning problem, and it arises regardless of which specific planning methodology is used (nonlinear planning, deductive planning, hierarchical planning, etc.) in all of these controlling an exponentially growing search space is a central problem. In all planning formalizations, it is critical that some sort of knowledge (heuristic or otherwise) is used to make reasonable decisions at any of the many choice points which arise in planning. Such choice points can concern: