Artificial intelligence for Bidding Hex

TL;DR

This paper introduces a Monte Carlo algorithm for Bidding Hex that leverages recent theoretical insights to efficiently identify near-optimal moves and bids, advancing game-playing AI in bidding scenarios.

Contribution

It applies recent theoretical results to develop a Monte Carlo method specifically for Bidding Hex, bridging random-turn game solutions with bidding strategies.

Findings

Effective near-optimal move identification in Bidding Hex

Demonstrates the algorithm's efficiency and accuracy

Connects theoretical game solutions with practical bidding strategies

Abstract

We present a Monte Carlo algorithm for efficiently finding near optimal moves and bids in the game of Bidding Hex. The algorithm is based on the recent solution of Random-Turn Hex by Peres, Schramm, Sheffield, and Wilson together with Richman's work connecting random-turn games to bidding games.