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The common wisdom that goal orderings can be used to improve planning performance is nearly as old as planning itself. During the last decades of research several approaches emerged that computed goal orderings for different planning paradigms, mostly in the area of state-space planning. For partial-order, plan-space planners goal orderings have not been investigated in much detail. Mechanisms developed for statespace planning are not directly applicable because partial-order planners do not have a current (world) state. Further, it is not completely clear how plan-space planners should make use of goal orderings. This paper describes an approach to extract goal orderings to be used by the plan-space planner CAPlan. The extraction of goal orderings is based on the analysis of an extended version of operator graphs which previously have been found useful for the analysis of interactions and recursion of plan-space planners.
Diese Arbeit beschäftigt sich mit einer Möglichkeit zur Effizienzverbesserung, wobei das SNLP-basierte Planungssystem CAPlan verwendet wird. Dabei werden neue, zu lösende Probleme einer Vorverarbeitung unterzogen. Dort werden bestimmte Eigenschaften ermittelt, ohne jedoch das Problem zu lösen. Anschliessend wird dem Planungssystem das neue Problem mit dem Zusatzwissen in Form der analysierten Eigenschaften übergeben. Das Planungssystem verwendet das Wissen, um effizienter eine Lösung zu finden.