Emergent Mind

Best-First and Depth-First Minimax Search in Practice

(1505.01603)
Published May 7, 2015 in cs.AI

Abstract

Most practitioners use a variant of the Alpha-Beta algorithm, a simple depth-first pro- cedure, for searching minimax trees. SSS, with its best-first search strategy, reportedly offers the potential for more efficient search. However, the complex formulation of the al- gorithm and its alleged excessive memory requirements preclude its use in practice. For two decades, the search efficiency of "smart" best-first SSS has cast doubt on the effectiveness of "dumb" depth-first Alpha-Beta. This paper presents a simple framework for calling Alpha-Beta that allows us to create a variety of algorithms, including SSS* and DUAL. In effect, we formulate a best-first algorithm using depth-first search. Expressed in this framework SSS is just a special case of Alpha-Beta, solving all of the perceived drawbacks of the algorithm. In practice, Alpha-Beta variants typically evaluate less nodes than SSS. A new instance of this framework, MTD(f), out-performs SSS and NegaScout, the Alpha-Beta variant of choice by practitioners.

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