TY - JOUR
T1 - On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning
AU - Kambhampati, Subbarao
N1 - Funding Information:
The ideas described here developed over the course of my interactions with Suresh Katukam, Gopi Bulusu and Yong Qu. I thank them for them for their insights. I also thank Suresh Katukam and Terry Zimmerman and Roberto Bayardo for their critical comments on a previous draft, and Steve Minton for his encouragemento n this line of work. A preliminary version of this paper was presented at AAAI-96 [27]. This researchi s supportedi n part by NSF young investigator award (NYI) IRI-9457634 an ARPA/Rome Laboratory planning initiative grants F30602-93-C-0039 and F30602-95-C-0247, an ARPA AASERT grant DAAH04-96-l-023 1, and an AFRL grant F20602-9%l - 0182.
PY - 1998/10
Y1 - 1998/10
N2 - The ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have developed independently in the constraint satisfaction, planning, machine learning and problem solving communities. The variety of approaches developed for IB and EBL in the various communities have hither-to been incomparable. In this paper, I formalize and unify these ideas under the task-independent framework of refinement search, which can model the search strategies used in both planning and constraint satisfaction problems (CSPs). I show that both IB and EBL depend upon the common theory of explanation analysis - which involves explaining search failures, and regressing them to higher levels of the search tree. My comprehensive analysis shows that most of the differences between the CSP and planning approaches to EBL and IB revolve around different solutions to: (a) how the failure explanations are computed; (b) how they are contextualized (contextualization involves deciding whether or not to keep the flaw description and the description of the violated problem constraints); and (c) how the storage of explanations is managed. The differences themselves can be understood in terms of the differences between planning and CSP problems as instantiations of refinement search. This unified understanding is expected to support a greater cross-fertilization of ideas among CSP, planning and EBL communities.
AB - The ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have developed independently in the constraint satisfaction, planning, machine learning and problem solving communities. The variety of approaches developed for IB and EBL in the various communities have hither-to been incomparable. In this paper, I formalize and unify these ideas under the task-independent framework of refinement search, which can model the search strategies used in both planning and constraint satisfaction problems (CSPs). I show that both IB and EBL depend upon the common theory of explanation analysis - which involves explaining search failures, and regressing them to higher levels of the search tree. My comprehensive analysis shows that most of the differences between the CSP and planning approaches to EBL and IB revolve around different solutions to: (a) how the failure explanations are computed; (b) how they are contextualized (contextualization involves deciding whether or not to keep the flaw description and the description of the violated problem constraints); and (c) how the storage of explanations is managed. The differences themselves can be understood in terms of the differences between planning and CSP problems as instantiations of refinement search. This unified understanding is expected to support a greater cross-fertilization of ideas among CSP, planning and EBL communities.
KW - Constraint satisfaction
KW - Dependency directed backtracking
KW - Dynamic backtracking
KW - Explanation-based learning
KW - Flaw resolution
KW - Nogood learning
KW - Planning
KW - Propagation
KW - Regression
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U2 - 10.1016/s0004-3702(98)00087-3
DO - 10.1016/s0004-3702(98)00087-3
M3 - Article
AN - SCOPUS:0032179503
SN - 0004-3702
VL - 105
SP - 161
EP - 208
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1-2
ER -