MAC Montag, 12.11.2012, 17:00 Uhr c.t., WE5/4.003
Michael Siebers: (Goal-dependent) Conditional Annulling of Actions in Automated Planning
Most modern automated planners use some kind of heuristic search to generate a suitable plan. In each search node (i.e. planning state) the successor nodes are given by the results of applying all applicable operators to the given state. So there are two major factors influencing the planning speed: the heuristic used (in calculation time and quality) and the number of applicable operators. We tackle the latter. We introduce means to expand the application preconditions of given operator definitions. Those expansions can incorporate conditions on the current state as well as conditions on the goal state (which isn't possible using the default planning problem definition language). Furthermore we show an approach to automatically learn such augmentations from previous planning experience.