Fine-Grained Decision-Theoretic Search Control

TL;DR

This paper advances decision-theoretic search control by analyzing the value of evaluating individual successors, leading to improved algorithms and empirical results in Othello.

Contribution

It extends the analysis of search control to include individual successor evaluations, providing a new formula and an improved MGSS* algorithm.

Findings

Developed a formula for expected node value based on evaluated successors.

Enhanced the MGSS* algorithm with empirical improvements.

Demonstrated effectiveness in the game of Othello.

Abstract

Decision-theoretic control of search has previously used as its basic unit. of computation the generation and evaluation of a complete set of successors. Although this simplifies analysis, it results in some lost opportunities for pruning and satisficing. This paper therefore extends the analysis of the value of computation to cover individual successor evaluations. The analytic techniques used may prove useful for control of reasoning in more general settings. A formula is developed for the expected value of a node, k of whose n successors have been evaluated. This formula is used to estimate the value of expanding further successors, using a general formula for the value of a computation in game-playing developed in earlier work. We exhibit an improved version of the MGSS* algorithm, giving empirical results for the game of Othello.