Domain-independent planning: representation and plan generation
Artificial Intelligence
Abstraction in planning
Automatically generating abstractions for problem solving
Automatically generating abstractions for problem solving
Abstraction in problem solving and learning
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
An efficient robot planner which generates its own procedures
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
Towards a practical theory of reformulation for reasoning about physical systems
Artificial Intelligence - Special volume on reformulation
The complexity of action redundancy
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Using secondary structure information to perform multiple alignment
Transactions on Computational Systems Biology III
An approach to safe continuous planning
PRIMA'04 Proceedings of the 7th Pacific Rim international conference on Intelligent Agents and Multi-Agent Systems
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The purposes of this paper are threefold. The first is to provide a crisp formalization of ABSTRIPS-style abstraction, since the lack of such formalizations has made it difficult to ascertain the uses and value of this type of abstraction in previous research. Second, we define the refinement relationship between solutions at different levels of the abstraction hierarchy. Such definitions are crucial to developing efficient search strategies with this type of hierarchical planning. And third, we provide a restriction on the abstraction mapping that provides a criterion for generating useful abstractions.