ML Anthology

Using Payoff-Similarity to Speed up Search

Timothy Furtak, Michael Buro
IJCAI 2011 pp. 534-539
doi:10.5591/978-1-57735-516-8/IJCAI11-097 /ijcai/2011/furtak2011ijcai-using/

Abstract

Transposition tables are a powerful tool in search domains for avoiding duplicate effort and for guiding node expansions. Traditionally, however, they have only been applicable when the current state is exactly the same as a previously explored state. We consider a generalized transposition table, whereby a similarity metric that exploits local structure is used to compare the current state with a neighbourhood of previously seen states. We illustrate this concept and forward pruning based on function approximation in the domain of Skat, and show that we can achieve speedups of 16+ over standard methods.

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Cite

Text

Furtak and Buro. "Using Payoff-Similarity to Speed up Search." International Joint Conference on Artificial Intelligence, 2011. doi:10.5591/978-1-57735-516-8/IJCAI11-097

Markdown

[Furtak and Buro. "Using Payoff-Similarity to Speed up Search." International Joint Conference on Artificial Intelligence, 2011.](https://mlanthology.org/ijcai/2011/furtak2011ijcai-using/) doi:10.5591/978-1-57735-516-8/IJCAI11-097

BibTeX

@inproceedings{furtak2011ijcai-using,
  title     = {{Using Payoff-Similarity to Speed up Search}},
  author    = {Furtak, Timothy and Buro, Michael},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2011},
  pages     = {534-539},
  doi       = {10.5591/978-1-57735-516-8/IJCAI11-097},
  url       = {https://mlanthology.org/ijcai/2011/furtak2011ijcai-using/}
}