Quantitative results concerning the utility of explanation-based learning
Artificial Intelligence
KIDS: A Semiautomatic Program Development System
IEEE Transactions on Software Engineering
Partial-order planning: evaluating possible efficiency gains
Artificial Intelligence
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
A constraint-based approach to high-school timetabling problems: a case study
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
A comparative analysis of partial order planning and task reduction planning
ACM SIGART Bulletin
Artificial Intelligence - Special volume on planning and scheduling
Universal classical planner: an algorithm for unifying state-space and plan-space planning
New directions in AI planning
Using temporal logic to control search in a forward chaining planner
New directions in AI planning
Failure driven dynamic search control for partial order planners: an explanation based approach
Artificial Intelligence
Understanding and Extending Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
A structured approach for synthesizing planners from specifications
ASE '97 Proceedings of the 12th international conference on Automated software engineering (formerly: KBSE)
Synthesis of schedulers for planned shutdowns of power plants
KBSE '96 Proceedings of The 11th Knowledge-Based Software Engineering Conference
Prodigy Planning Algorithm
Learning effective search control knowledge: an explanation-based approach
Learning effective search control knowledge: an explanation-based approach
Challenges in bridging plan synthesis paradigms
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
STRPLAN: A Distributed Planner for Object-Centred Application Domains
Applied Intelligence
Synthesis of efficient constraint-satisfaction programs
The Knowledge Engineering Review
An automated approach to generating efficient constraint solvers
Proceedings of the 34th International Conference on Software Engineering
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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.