TY - GEN
T1 - Cost sensitive reachability heuristics for handling state uncertainty
AU - Bryce, Daniel
AU - Kambhampati, Subbarao
PY - 2005
Y1 - 2005
N2 - While POMDPs provide a general platform for non-deterministic conditional planning under a variety of quality metrics they have limited scalability. On the other hand, non-deterministic conditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present a novel generalization of planning graph based heuristics that helps conditional planners both scale and generate high quality plans when using actions with non-uniform costs. We make empirical comparisons with two state of the art planners to show the benefit of our techniques.
AB - While POMDPs provide a general platform for non-deterministic conditional planning under a variety of quality metrics they have limited scalability. On the other hand, non-deterministic conditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present a novel generalization of planning graph based heuristics that helps conditional planners both scale and generate high quality plans when using actions with non-uniform costs. We make empirical comparisons with two state of the art planners to show the benefit of our techniques.
UR - http://www.scopus.com/inward/record.url?scp=34247386058&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34247386058&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:34247386058
SN - 0974903914
T3 - Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
SP - 60
EP - 68
BT - Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
PB - AUAI Press
T2 - 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005
Y2 - 26 July 2005 through 29 July 2005
ER -