Helping AI to Play Hearthstone: AAIA'17 Data Mining Challenge

TL;DR

This paper summarizes the AAIA'17 Data Mining Challenge focused on developing AI models to play Hearthstone, highlighting machine learning approaches for predicting game outcomes and guiding intelligent agent design.

Contribution

It introduces the challenge, discusses various machine learning methods for modeling game states, and evaluates promising solutions for enhancing Hearthstone AI agents.

Findings

Predictive models can estimate players' winning chances.

Certain machine learning approaches outperform baseline methods.

Selected solutions show potential for integration with Monte Carlo Tree Search.

Abstract

This paper summarizes the AAIA'17 Data Mining Challenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and demonstrate how predictive models for the assessment of player's winning chances can be utilized in a construction of an intelligent agent for playing Hearthstone. Finally, we show a few selected machine learning approaches for modeling state and action values in Hearthstone. We provide evaluation for a few promising solutions that may be used to create more advanced types of agents, especially in conjunction with Monte Carlo Tree Search algorithms.