Optimizing $\alpha\mu$

TL;DR

This paper enhances the $ ext{ extalpha} ext{ extmu}$ search algorithm for imperfect information games like Bridge by introducing optimizations that reduce redundant computations and improve speed, with potential applicability to other similar games.

Contribution

It proposes general optimizations for $ ext{ extalpha} ext{ extmu}$, including Pareto front-based cuts and redundancy tracking, to improve efficiency in imperfect information turn-based games.

Findings

Optimizations significantly speed up $ ext{ extalpha} ext{ extmu}$ search.

Redundancy tracking reduces unnecessary evaluations.

Parallelization benefits observed in double dummy searches.

Abstract

$\alpha\mu$ is a search algorithm which repairs two defaults of Perfect Information Monte Carlo search: strategy fusion and non locality. In this paper we optimize $\alpha\mu$ for the game of Bridge, avoiding useless computations. The proposed optimizations are general and apply to other imperfect information turn-based games. We define multiple optimizations involving Pareto fronts, and show that these optimizations speed up the search. Some of these optimizations are cuts that stop the search at a node, while others keep track of which possible worlds have become redundant, avoiding unnecessary, costly evaluations. We also measure the benefits of parallelizing the double dummy searches at the leaves of the $\alpha\mu$ search tree.