Finding optimal strategies in sequential games with the novel selection monad

TL;DR

This paper introduces a novel selection monad for finding optimal strategies in sequential games, demonstrating its application through Haskell-based case studies including Connect Four, Sudoku, and simplified Chess, along with performance analysis.

Contribution

It develops a library utilizing the selection monad to implement game AIs and explores its effectiveness through practical case studies and performance evaluation.

Findings

Successful implementation of game AIs using the selection monad

Performance bottlenecks identified for future optimization

Demonstrated elegance and practicality of the selection monad in game AI development

Abstract

The recently discovered monad, Tx = Selection (x -> r) -> r, provides an elegant way to finnd optimal strategies in sequential games. During this thesis, a library was developed which provides a set of useful functions using the selection monad to compute optimal games and AIs for sequential games. In order to explore the selection monads ability to support these AI implementations, three example case studies were developed using Haskell: The two-player game Connect Four, a Sudoku solver and a simplified version of Chess. These case studies show how to elegantly implement a game AI. Furthermore, a performance analysis of these case studies was done, identifying the major points where performance can be increased.