Generalized Adaptive A*

• Authors: Xiaoxun Sun;Sven Koenig;William Yeoh
• Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
• Year: 2008

Agents often have to solve series of similar search problems. Adaptive A* is a recent incremental heuristic search algorithm that solves series of similar search problems faster than A* because it updates the h-values using information from previous searches. It basically transforms consistent h-values into more informed consistent h-values. This allows it to find shortest paths in state spaces where the action costs can increase over time since consistent h-values remain consistent after action cost increases. However, it is not guaranteed to find shortest paths in state spaces where the action costs can decrease over time because consistent h-values do not necessarily remain consistent after action cost decreases. Thus, the h-values need to get corrected after action cost decreases. In this paper, we show how to do that, resulting in Generalized Adaptive A* (GAA*) that finds shortest paths in state spaces where the action costs can increase or decrease over time. Our experiments demonstrate that Generalized Adaptive A* outperforms breadth-first search, A* and D* Lite for moving-target search, where D* Lite is an alternative state-of-the-art incremental heuristic search algorithm that finds shortest paths in state spaces where the action costs can increase or decrease over time.