%0 Conference Paper %1 conf/aamas06/Koenig %A Koenig, Sven %A Likhachev, Maxim %B AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems %C New York, NY, USA %D 2006 %I ACM Press %K aamas06 agents planning search %P 281--288 %R http://doi.acm.org/10.1145/1160633.1160682 %T Real-time adaptive A* %U http://portal.acm.org/citation.cfm?id=1160633.1160682&coll=ACM&dl=ACM&type=series&idx=1160633&part=Proceedings&WantType=Proceedings&title=International%20Conference%20on%20Autonomous%20Agents&CFID=25754815&CFTOKEN=92537800 %X Characters in real-time computer games need to move smoothly and thus need to search in real time. In this paper, we describe a simple but powerful way of speeding up repeated A* searches with the same goal states, namely by updating the heuristics between A* searches. We then use this technique to develop a novel real-time heuristic search method, called Real-Time Adaptive A*, which is able to choose its local search spaces in a fine-grained way. It updates the values of all states in its local search spaces and can do so very quickly. Our experimental results for characters in real-time computer games that need to move to given goal coordinates in unknown terrain demonstrate that this property allows Real-Time Adaptive A* to follow trajectories of smaller cost for given time limits per search episode than a recently proposed real-time heuristic search method 5 that is more difficult to implement. %@ 1-59593-303-4

@inproceedings{conf/aamas06/Koenig, abstract = {Characters in real-time computer games need to move smoothly and thus need to search in real time. In this paper, we describe a simple but powerful way of speeding up repeated A* searches with the same goal states, namely by updating the heuristics between A* searches. We then use this technique to develop a novel real-time heuristic search method, called Real-Time Adaptive A*, which is able to choose its local search spaces in a fine-grained way. It updates the values of all states in its local search spaces and can do so very quickly. Our experimental results for characters in real-time computer games that need to move to given goal coordinates in unknown terrain demonstrate that this property allows Real-Time Adaptive A* to follow trajectories of smaller cost for given time limits per search episode than a recently proposed real-time heuristic search method [5] that is more difficult to implement.}, added-at = {2007-08-03T14:46:24.000+0200}, address = {New York, NY, USA}, author = {Koenig, Sven and Likhachev, Maxim}, biburl = {https://www.bibsonomy.org/bibtex/2a7454bed56ddf53a023c577b0a8af36d/mpfingst}, booktitle = {AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems}, description = {: AAMAS '06, Real-time adaptive A*}, doi = {http://doi.acm.org/10.1145/1160633.1160682}, interhash = {414146643113e07dabfca0471b9d0b21}, intrahash = {a7454bed56ddf53a023c577b0a8af36d}, isbn = {1-59593-303-4}, keywords = {aamas06 agents planning search}, location = {Hakodate, Japan}, pages = {281--288}, publisher = {ACM Press}, timestamp = {2007-08-03T14:46:24.000+0200}, title = {Real-time adaptive A*}, url = {http://portal.acm.org/citation.cfm?id=1160633.1160682&coll=ACM&dl=ACM&type=series&idx=1160633&part=Proceedings&WantType=Proceedings&title=International%20Conference%20on%20Autonomous%20Agents&CFID=25754815&CFTOKEN=92537800}, year = 2006 }