Kolloquium Dienstag, 04.02.14 12:00 Uhr, WE5/5.013 

Christian Reißner (MA AI): Researching Heuristic Functions to Detect and Avoid Dead Ends in Action Planning    

One approach to reduce the runtime in learning is to find dead ends by planning and use this information to avoid them efficiently. The heuristic search is a well performing planning approach. The target of this paper is the modification of the heuristic search. I will present methods to detect dead ends and run them in different planning domains. A comparison of the results decides the best method to detect dead ends in planning. I will show that the Wisp method delivers the best results and that abstraction heuristics are the best for detecting dead ends.