TY - JOUR
T1 - Failure driven dynamic search control for partial order planners
T2 - An explanation based approach
AU - Kambhampati, Subbarao
AU - Katukam, Suresh
AU - Qu, Yong
N1 - Funding Information:
‘The I301 a lsoe nsuret\h at no interveninsgtep can supported by the causal link. This is however not required for completeness I26 1.
PY - 1996/12
Y1 - 1996/12
N2 - Given the intractability of domain independent planning, the ability to control the search of a planner is vitally important. One way of doing this involves learning from search failures. This paper describes SNLP + EBL, the first implementation of an explanation based search control rule learning framework for a partial order (plan-space) planner. We will start by describing the basic learning framework of SNLP + EBL. We will then concentrate on SNLP + EBL's ability to learn from failures, and describe the results of empirical studies which demonstrate the effectiveness of the search control rules SNLP + EBL learns using our method. We then demonstrate the generality of our learning methodology by extending it to UCPOP (Penberthy and Weld, 1992), a descendant of SNLP that allows for more expressive domain theories. The resulting system, UCPOP + EBL, is used to analyze and understand the factors influencing the effectiveness of EBL. Specifically, we analyze the effect of (i) expressive action representations, (ii) domain specific failure theories and (iii) sophisticated backtracking strategies on the utility of EBL. Through empirical studies, we demonstrate that expressive action representations allow for more explicit domain representations which in turn increase the ability of EBL to learn from analytical failures, and obviate the need for domain specific failure theories. We also explore the strong affinity between dependency directed backtracking and EBL in planning.
AB - Given the intractability of domain independent planning, the ability to control the search of a planner is vitally important. One way of doing this involves learning from search failures. This paper describes SNLP + EBL, the first implementation of an explanation based search control rule learning framework for a partial order (plan-space) planner. We will start by describing the basic learning framework of SNLP + EBL. We will then concentrate on SNLP + EBL's ability to learn from failures, and describe the results of empirical studies which demonstrate the effectiveness of the search control rules SNLP + EBL learns using our method. We then demonstrate the generality of our learning methodology by extending it to UCPOP (Penberthy and Weld, 1992), a descendant of SNLP that allows for more expressive domain theories. The resulting system, UCPOP + EBL, is used to analyze and understand the factors influencing the effectiveness of EBL. Specifically, we analyze the effect of (i) expressive action representations, (ii) domain specific failure theories and (iii) sophisticated backtracking strategies on the utility of EBL. Through empirical studies, we demonstrate that expressive action representations allow for more explicit domain representations which in turn increase the ability of EBL to learn from analytical failures, and obviate the need for domain specific failure theories. We also explore the strong affinity between dependency directed backtracking and EBL in planning.
KW - Explanation based learning
KW - Failure driven learning
KW - Partial order planning
KW - Search control
UR - http://www.scopus.com/inward/record.url?scp=0030416991&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0030416991&partnerID=8YFLogxK
U2 - 10.1016/s0004-3702(96)00005-7
DO - 10.1016/s0004-3702(96)00005-7
M3 - Article
AN - SCOPUS:0030416991
VL - 88
SP - 253
EP - 315
JO - Artificial Intelligence
JF - Artificial Intelligence
SN - 0004-3702
IS - 1-2
ER -