---
title: "Using Payoff-Similarity to Speed Up Search"
authors:
  - "Timothy Furtak"
  - "Michael Buro"
doi: "10.5591/978-1-57735-516-8/IJCAI11-097"
url: "https://ijcai.org/proceedings/2011/97"
pdf: "https://ijcai.org/proceedings/2011/0097.pdf"
venue: "Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence"
year: 2011
pages: "534-538"
track: ""
keywords:
---

# Using Payoff-Similarity to Speed Up Search

Timothy Furtak, Michael Buro

*Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence*, pages 534-538. DOI: [10.5591/978-1-57735-516-8/IJCAI11-097](https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-097)

## 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.