Domain-independent planning: representation and plan generation
Artificial Intelligence
Macro-operators: a weak method for learning
Artificial Intelligence - Lecture notes in computer science 178
Exploiting constraints in design synthesis
Exploiting constraints in design synthesis
Reasoning about action I: a possible worlds approach
Artificial Intelligence
Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
Nonmonotonic reasoning in the framework of situation calculus
Artificial Intelligence - Special issue on knowledge representation
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Admissible criteria for loop control in planning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Learning abstraction hierarchies for problem solving
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Is there any need for domain-dependent control information?
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
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This paper extends existing work on the use of default information to describe planning hierarchies in two ways. First, we present a completeness result showing that all hierarchical planners can be described using defaults. Second, we show that if the planning hierarchy is situation-dependent, the default description is likely to have substantial computational advantages over a more conventional approach.