EMPLOYING DOMAIN KNOWLEDGE TO IMPROVE AI PLANNING EFFICIENCY

One of the most important problems of traditional A.I. planning methods such as nonlinear planning is the control of the planning process itself. A non-linear planner confronts many choice points in different steps of the planning process (i.e., selection of the next goal to work on, selection of an action to achieve the goal, and selection of the right order to resolve a conflict), and ideally, it should choose the best option in each case.The partial ordered planner (POP) introduced by Weld in 1994, assumes a magical function called Choose to select the best option in each planning step. There have been some previous efforts for the realization of this function; however, most of these efforts ignore the valuable information that can be extracted from the problem s domain. This paper introduces several general heuristics for extracting useful information contained in problem domains by an automatic preprocessing. These heuristics have been incorporated into a planner calledH POP, and testedon a numberof different domains.