TY - JOUR
T1 - Model-lite planning
T2 - Case-based vs. model-based approaches
AU - Zhuo, Hankz Hankui
AU - Kambhampati, Subbarao
N1 - Funding Information:
We thank Dr. Tuan Nguyen for the discussion on comparing case-based and model-based approaches. Hankz Hankui Zhuo thanks the National Key Research and Development Program of China (2016YFB0201900), National Natural Science Foundation of China (U1611262), Pearl River Science and Technology New Star of Guangzhou, and Guangdong Province Key Laboratory of Big Data Analysis and Processing for the support of this research. Kambhampati's research is supported in part by the ONR grants N00014-16-1-2892, N00014-13-1-0176, N00014-13-1-0519 and N00014-15-1-2027.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider the problem of generating robust and accurate plans, when the agent only has access to incomplete domain models, supplanted by a set of successful plan cases. We will develop two classes of approaches – one case-based and the other model-based. ML-CBP is a case-based approach that leverages the incomplete model and the plan cases to solve a new problem directly by affecting case-level transfer. RIM is a model-based approach that uses the incomplete model and the plan cases to first learn a more complete model. This model contains both primitive actions as well as macro-operators that are derived from the plan cases. The learned model is then used in conjunction with an off-the-shelf planner to solve new problems. We present a comprehensive evaluation of the two approaches, both to characterize their relative tradeoffs, and to quantify their advances over existing approaches.
AB - There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider the problem of generating robust and accurate plans, when the agent only has access to incomplete domain models, supplanted by a set of successful plan cases. We will develop two classes of approaches – one case-based and the other model-based. ML-CBP is a case-based approach that leverages the incomplete model and the plan cases to solve a new problem directly by affecting case-level transfer. RIM is a model-based approach that uses the incomplete model and the plan cases to first learn a more complete model. This model contains both primitive actions as well as macro-operators that are derived from the plan cases. The learned model is then used in conjunction with an off-the-shelf planner to solve new problems. We present a comprehensive evaluation of the two approaches, both to characterize their relative tradeoffs, and to quantify their advances over existing approaches.
KW - AI planning
KW - Action model learning
KW - Case-based planning
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U2 - 10.1016/j.artint.2017.01.004
DO - 10.1016/j.artint.2017.01.004
M3 - Article
AN - SCOPUS:85012066318
SN - 0004-3702
VL - 246
SP - 1
EP - 21
JO - Artificial Intelligence
JF - Artificial Intelligence
ER -