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

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.Comment: Federated Conference on Computer Science and Information Systems, Prague (FedCSIS-2017) (Prague, Czech Republic

Knowledge Pit - A data challenge platform

Knowledge Pit (https://knowledgepit.fedcsis.org) is a web platform created to facilitate organization of data mining competitions. Its main aim is to stimulate collaborative research for solving practical problems related to real-life applications of predictive analysis and decision support systems. What makes Knowledge Pit different from other data challenge platforms is the fact that it is a non-commercial project focusing on a collaboration with international conferences. It promotes the idea of open research and encourages young researchers to involve in projects related to data science. The platform can also be used as a elearning tool to support data mining courses and for defining interesting student projects. In this paper we discuss the architecture of Knowledge Pit and highlight its main functionalities. We also overview some of the already finished data challenges that were organized using our web platform.</p