Escaping heuristic depressions in real-time heuristic search
نویسندگان
چکیده
Heuristic depressions are local minima of heuristic functions. While visiting one them, real-time (RT) search algorithms like LRTA∗ will update the heuristic value for most of their states several times before escaping, resulting in costly solutions. Existing RT search algorithm tackle this problem by doing more search and/or lookahead but do not guide search towards leaving depressions. We present eLSS-LRTA∗, a new RT search algorithm based on LSS-LRTA∗ that actively guides search towards exiting regions with heuristic depressions. We show that our algorithm produces better-quality solutions than LSS-LRTA∗ for equal values of lookahead in standard RT benchmarks.
منابع مشابه
Real-Time Heuristic Search with Depression Avoidance
Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time search algorithms easily become trapped in those regions since the heuristic values of states in them may need to be updated multiple times, which...
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