TY - JOUR
T1 - Synthesizing customized planners from specifications
AU - Srivastava, Biplav
AU - Kambhampati, Subbarao
PY - 1998
Y1 - 1998
N2 - Existing plan synthesis approaches in artificial intelligence fall into two categories - domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very efficient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very efficient in the domain for which they are designed. One enticing alternative to these approaches is to automatically synthesize domain independent planners given the knowledge about the domain and the theory of planning. In this paper, we investigate the feasibility of using existing automated software synthesis tools to support such synthesis. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used to derive a domain-customized planner through a semi-automatic combination of a declarative theory of planning, and the declarative control knowledge specific to a given domain, to semi-automatically combine them to derive domain-customized planners. We discuss what it means to write a declarative theory of planning and control knowledge for KIDS, and illustrate our approach by generating a class of domain-specific planners using state space refinements. Our experiments show that the synthesized planners can outperform classical refinement planners (implemented as instantiations of UCP, Kambhampati & Srivastava, 1995), using the same control knowledge. We will contrast the costs and benefits of the synthesis approach with conventional methods for customizing domain independent planners.
AB - Existing plan synthesis approaches in artificial intelligence fall into two categories - domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very efficient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very efficient in the domain for which they are designed. One enticing alternative to these approaches is to automatically synthesize domain independent planners given the knowledge about the domain and the theory of planning. In this paper, we investigate the feasibility of using existing automated software synthesis tools to support such synthesis. Specifically, we describe an architecture called CLAY in which the Kestrel Interactive Development System (KIDS) is used to derive a domain-customized planner through a semi-automatic combination of a declarative theory of planning, and the declarative control knowledge specific to a given domain, to semi-automatically combine them to derive domain-customized planners. We discuss what it means to write a declarative theory of planning and control knowledge for KIDS, and illustrate our approach by generating a class of domain-specific planners using state space refinements. Our experiments show that the synthesized planners can outperform classical refinement planners (implemented as instantiations of UCP, Kambhampati & Srivastava, 1995), using the same control knowledge. We will contrast the costs and benefits of the synthesis approach with conventional methods for customizing domain independent planners.
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U2 - 10.1613/jair.428
DO - 10.1613/jair.428
M3 - Article
AN - SCOPUS:0343153354
SN - 1076-9757
VL - 8
SP - 93
EP - 128
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -