TY - JOUR
T1 - Anytime heuristic search for partial satisfaction planning
AU - Benton, J.
AU - Do, Minh
AU - Kambhampati, Subbarao
N1 - Funding Information:
This article is in part edited and extended from Do and Kambhampati [15]; Benton, Kambhampati and Do [4]; and van den Briel, Sanchez, Do and Kambhampati [44]. We would like to thank Romeo Sanchez for help with his code and the experiments, and Menkes van den Briel and Rong Zhou for their discussions and helpful comments on this paper. We also greatly appreciate the assistance of William Cushing for discussions and help with experiments. Additionally, we express our gratitude to David Smith and Daniel Bryce for their suggestions and discussions in the initial stages of this work. And of course thanks go to Sylvie Thiébaux and the anonymous reviewers, who gave valuable comments that helped us to improve the article. This research is supported in part by the ONR grants N000140610058 and N0001407-1-1049 (MURI subcontract from Indiana University), a Lockheed Martin subcontract TT0687680 to ASU as part of the DARPA Integrated Learning program, and the NSF grant IIS-308139.
PY - 2009/4
Y1 - 2009/4
N2 - We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource cost (e.g., time, fuel cost) needed to execute each action. The objective is to find the plan that maximizes the trade-off between the total achieved utility and the total incurred cost; we call this problem PSP Net Benefit. Previous approaches to solving this problem heuristically convert PSP Net Benefit into STRIPS planning with action cost by pre-selecting a subset of goals. In contrast, we provide a novel anytime search algorithm that handles soft goals directly. Our new search algorithm has an anytime property that keeps returning better quality solutions until the termination criteria are met. We have implemented this search algorithm, along with relaxed plan heuristics adapted to PSP Net Benefit problems, in a forward state-space planner called SapaPS. An adaptation of SapaPS, called YochanPS, received a "distinguished performance" award in the "simple preferences" track of the 5th International Planning Competition.
AB - We present a heuristic search approach to solve partial satisfaction planning (PSP) problems. In these problems, goals are modeled as soft constraints with utility values, and actions have costs. Goal utility represents the value of each goal to the user and action cost represents the total resource cost (e.g., time, fuel cost) needed to execute each action. The objective is to find the plan that maximizes the trade-off between the total achieved utility and the total incurred cost; we call this problem PSP Net Benefit. Previous approaches to solving this problem heuristically convert PSP Net Benefit into STRIPS planning with action cost by pre-selecting a subset of goals. In contrast, we provide a novel anytime search algorithm that handles soft goals directly. Our new search algorithm has an anytime property that keeps returning better quality solutions until the termination criteria are met. We have implemented this search algorithm, along with relaxed plan heuristics adapted to PSP Net Benefit problems, in a forward state-space planner called SapaPS. An adaptation of SapaPS, called YochanPS, received a "distinguished performance" award in the "simple preferences" track of the 5th International Planning Competition.
KW - Heuristics
KW - Partial satisfaction
KW - Planning
KW - Search
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U2 - 10.1016/j.artint.2008.11.010
DO - 10.1016/j.artint.2008.11.010
M3 - Article
AN - SCOPUS:60649092091
SN - 0004-3702
VL - 173
SP - 562
EP - 592
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 5-6
ER -