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nissim-et-al-ijcai2011.pdf
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Summary

Researchers address the issue of deriving admissible heuristics automatically in optimal planning, focusing on Merge-and-Shrink abstraction (M&S), which can compute perfect heuristics in polynomial time in some benchmark domains. M&S allows for fine-grained choices in defining abstractions, but little is known about making these choices. The study uses the notion of bisimulation to guide the process, showing that aggregating only bisimilar states yields a perfect heuristic. However, bisimulations can be exponentially large, so the researchers apply label reduction, which can reduce bisimulation size exponentially without losing information. The resulting algorithm computes perfect heuristics in polynomial time in several benchmark domains. Empirical results show that approximating variants of this algorithm improve the state of the art in M&S heuristics, with a simple hybrid of two variants being competitive with the leading heuristic LM-cut.

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Description

Researchers address the issue of deriving admissible heuristics automatically in optimal planning, focusing on Merge-and-Shrink abstraction (M&S), which can...

Technical Information

  • File Format: PDF
  • File Size: 291 KB
  • Pages: 8
  • Language: EN
  • Total Downloads: 266
  • Last Updated: 1 day ago

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