The Metric-FF Planning System: Translating ``Ignoring Delete Lists'' to Numeric State Variables

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Translating “Ignoring Delete Lists” to Numeric State Variables The Metric-FF Planning System: Translating “Ignoring Delete Lists” to Numeric State Variables

Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). One of the currently most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We ...

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The Metric-FF Planning System: Translating "Ignoring Delete Lists" to Numeric State Variables

Planning with numeric state variables has been a challenge for many years, and was a part of the 3rd International Planning Competition (IPC-3). Currently one of the most popular and successful algorithmic techniques in STRIPS planning is to guide search by a heuristic function, where the heuristic is based on relaxing the planning task by ignoring the delete lists of the available actions. We ...

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The ignoring delete lists relaxation is of paramount importance for both satisficing and optimal planning. In earlier work (Hoffmann 2005), it was observed that the optimal relaxation heuristic h has amazing qualities in many classical planning benchmarks, in particular pertaining to the complete absence of local minima. The proofs of this are hand-made, raising the question whether such proofs...

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Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning Benchmarks

Between 1998 and 2004, the planning community has seen vast progress in terms of the sizes of benchmark examples that domain-independent planners can tackle successfully. The key technique behind this progress is the use of heuristic functions based on relaxing the planning task at hand, where the relaxation is to assume that all delete lists are empty. The unprecedented success of such methods...

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ژورنال

عنوان ژورنال: Journal of Artificial Intelligence Research

سال: 2003

ISSN: 1076-9757

DOI: 10.1613/jair.1144