2024/05/09 by Owen Randall, Randall, Owen, Martin Müller +5 · 1 citation
Business, Management and Accounting · Computer Science · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Games #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Software Engineering Research
paper · pdf · doi:10.48550/arxiv.2405.05594
openalex publication_date 2024/05/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.