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Escaping Depressions in LRTS Based on Incremental Refinement of Encoded Quad-Trees
2017
Mathematical Problems in Engineering
In the context of robot navigation, game AI, and so on, real-time search is extensively used to undertake motion planning. Though it satisfies the requirement of quick response to users' commands and environmental changes, learning real-time search (LRTS) suffers from the heuristic depressions where agents behave irrationally. There have introduced several effective solutions, such as state abstractions. This paper combines LRTS and encoded quad-tree abstraction which represent the search space
doi:10.1155/2017/1850678
fatcat:4ak5ztp6kjh5nkdfzdalputlbm