Perfect Information Monte Carlo with Postponing Reasoning

TL;DR

This paper introduces EPIMC, an online algorithm that improves imperfect information game search by postponing perfect information resolution, thereby reducing strategy fusion issues and enhancing performance in complex games.

Contribution

EPIMC extends PIMC by delaying perfect information resolution, addressing strategy fusion challenges, and demonstrating empirical performance improvements across various games.

Findings

EPIMC outperforms PIMC in games with high strategy fusion.

Postponing resolution reduces strategy fusion effects.

Theoretical insights into determinization-based algorithms are provided.

Abstract

Imperfect information games, such as Bridge and Skat, present challenges due to state-space explosion and hidden information, posing formidable obstacles for search algorithms. Determinization-based algorithms offer a resolution by sampling hidden information and solving the game in a perfect information setting, facilitating rapid and effective action estimation. However, transitioning to perfect information introduces challenges, notably one called strategy fusion.This research introduces `Extended Perfect Information Monte Carlo' (EPIMC), an online algorithm inspired by the state-of-the-art determinization-based approach Perfect Information Monte Carlo (PIMC). EPIMC enhances the capabilities of PIMC by postponing the perfect information resolution, reducing alleviating issues related to strategy fusion. However, the decision to postpone the leaf evaluator introduces novel considerations, such as the interplay between prior levels of reasoning and the newly deferred resolution. In our empirical analysis, we investigate the performance of EPIMC across a range of games, with a particular focus on those characterized by varying degrees of strategy fusion. Our results demonstrate notable performance enhancements, particularly in games where strategy fusion significantly impacts gameplay. Furthermore, our research contributes to the theoretical foundation of determinization-based algorithms addressing challenges associated with strategy fusion.%, thereby enhancing our understanding of these algorithms within the context of imperfect information game scenarios.