TY - JOUR
T1 - Robust planning with incomplete domain models
AU - Nguyen, Tuan
AU - Sreedharan, Sarath
AU - Kambhampati, Subbarao
N1 - Funding Information:
This research is supported in part by the ARO grant W911NF-13-1-0023, and the ONR grants N00014-13-1-0176, N0014-13-1-0519 and N00014-15-1-2027. We gratefully acknowledge the helpful discussions with Minh Do, David Smith (NASA), Hankz Hankui Zhuo (Sun Yat-Sen University) and Will Cushing.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/4/1
Y1 - 2017/4/1
N2 - Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In this paper, we study planning problems with incomplete domain models where the annotations specify possible preconditions and effects of actions. We show that the problem of assessing the quality of a plan, or its plan robustness, is #P-complete, establishing its equivalence with the weighted model counting problems. We present two approaches to synthesizing robust plans. While the method based on the compilation to conformant probabilistic planning is much intuitive, its performance appears to be limited to only small problem instances. Our second approach based on stochastic heuristic search works well for much larger problems. It aims to use the robustness measure directly for estimating heuristic distance, which is then used to guide the search. Our planning system, PISA, outperforms a state-of-the-art planner handling incomplete domain models in most of the tested domains, both in terms of plan quality and planning time. Finally, we also present an extension of PISA called CPISA that is able to exploit the available of past successful plan traces to both improve the robustness of the synthesized plans and reduce the domain modeling burden.
AB - Most current planners assume complete domain models and focus on generating correct plans. Unfortunately, domain modeling is a laborious and error-prone task, thus real world agents have to plan with incomplete domain models. While domain experts cannot guarantee completeness, often they are able to circumscribe the incompleteness of the model by providing annotations as to which parts of the domain model may be incomplete. In this paper, we study planning problems with incomplete domain models where the annotations specify possible preconditions and effects of actions. We show that the problem of assessing the quality of a plan, or its plan robustness, is #P-complete, establishing its equivalence with the weighted model counting problems. We present two approaches to synthesizing robust plans. While the method based on the compilation to conformant probabilistic planning is much intuitive, its performance appears to be limited to only small problem instances. Our second approach based on stochastic heuristic search works well for much larger problems. It aims to use the robustness measure directly for estimating heuristic distance, which is then used to guide the search. Our planning system, PISA, outperforms a state-of-the-art planner handling incomplete domain models in most of the tested domains, both in terms of plan quality and planning time. Finally, we also present an extension of PISA called CPISA that is able to exploit the available of past successful plan traces to both improve the robustness of the synthesized plans and reduce the domain modeling burden.
KW - Incomplete domain models
KW - Robust planning
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U2 - 10.1016/j.artint.2016.12.003
DO - 10.1016/j.artint.2016.12.003
M3 - Article
AN - SCOPUS:85012303389
SN - 0004-3702
VL - 245
SP - 134
EP - 161
JO - Artificial Intelligence
JF - Artificial Intelligence
ER -