Expected Work Search: Combining Win Rate and Proof Size Estimation

TL;DR

Expected Work Search (EWS) is a novel game solving algorithm that combines win rate and proof size estimation to efficiently solve complex board games like Go and Hex, achieving new milestones.

Contribution

EWS introduces a new approach by integrating win rate and proof size estimates, optimizing expected work to improve game solving efficiency.

Findings

Solved empty 5x5 Go with superko ruleset

Solved empty 8x8 Hex in under 4 minutes

Outperforms traditional algorithms on Go and Hex

Abstract

We propose Expected Work Search (EWS), a new game solving algorithm. EWS combines win rate estimation, as used in Monte Carlo Tree Search, with proof size estimation, as used in Proof Number Search. The search efficiency of EWS stems from minimizing a novel notion of Expected Work, which predicts the expected computation required to solve a position. EWS outperforms traditional solving algorithms on the games of Go and Hex. For Go, we present the first solution to the empty 5x5 board with the commonly used positional superko ruleset. For Hex, our algorithm solves the empty 8x8 board in under 4 minutes. Experiments show that EWS succeeds both with and without extensive domain-specific knowledge.