Summarizing Strategy Card Game AI Competition

This paper concludes five years of AI competitions based on Legends of Code and Magic (LOCM), a small Collectible Card Game (CCG), designed with the goal of supporting research and algorithm development. The game was used in a number of events, including Community Contests on the CodinGame platform, and Strategy Card Game AI Competition at the IEEE Congress on Evolutionary Computation and IEEE Conference on Games. LOCM has been used in a number of publications related to areas such as game tree search algorithms, neural networks, evaluation functions, and CCG deckbuilding. We present the rules of the game, the history of organized competitions, and a listing of the participant and their approaches, as well as some general advice on organizing AI competitions for the research community. Although the COG 2022 edition was announced to be the last one, the game remains available and can be played using an online leaderboard arena

Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms

We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our Hearthstone agent's decision making. We show that even simple neural networks can be trained and successfully used for the evaluation of game states. Moreover, we demonstrate that by providing a guidance to the game state search heuristic, it is possible to substantially improve the win rate, and at the same time reduce the required computations.Comment: Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games (CIG'18); pages 445-452; ISBN: 978-1-5386-4358-

Looking for Archetypes: Applying Game Data Mining to Hearthstone Decks

Digital Collectible Cards Games such as Hearthstone have become a very proli c test-bed for Arti cial Intelligence algorithms. The main researches have focused on the implementation of autonomous agents (bots) able to effectively play the game. However, this environment is also very attractive for the use of Data Mining (DM) and Machine Learning (ML) techniques, for analysing and extracting useful knowledge from game data. The objective of this work is to apply existing Game Mining techniques in order to study more than 600,000 real decks (groups of cards) created by players with many di erent skill levels. Data visualisation and analysis tools have been applied, namely, Graph representations and Clustering techniques. Then, an expert player has conducted a deep analysis of the results yielded by these methods, aiming to identify the use of standard - and well-known - archetypes de ned by the players. The used methods will also make it possible for the expert to discover hidden relationships between cards that could lead to nding better combinations of them, enhancing players' decks or, otherwise, identify unbalanced cards that could lead to a disappointing game experience. Moreover, although this work is mostly focused on data analysis and visualization, the obtained results can be applied to improve Hearthstone Bots' behaviour, e.g. predicting opponent's actions after identifying a speci c archetype in his/her deck.Spanish Government PID2020-113462RB-I00 PID2020-115570 GB-C22Junta de Andalucia B-TIC-402-UGR18 P18-RT-4830 A-TIC-608-UGR2

MAPiS 2019 - First MAP-i Seminar: proceedings

This book contains a selection of Informatics papers accepted for presentation and discussion at “MAPiS 2019 - First MAP-i Seminar”, held in Aveiro, Portugal, January 31, 2019. MAPiS is the first conference organized by the MAP-i first year students, in the context of the Seminar course. The MAP-i Doctoral Programme in Computer Science is a joint Doctoral Programme in Computer Science of the University of Minho, the University of Aveiro and the University of Porto. This programme aims to form highly-qualified professionals, fostering their capacity and knowledge to the research area. This Conference was organized by the first grade students attending the Seminar Course. The aim of the course was to introduce concepts which are complementary to scientific and technological education, but fundamental to both completing a PhD successfully and entailing a career on scientific research. The students had contact with the typical procedures and difficulties of organizing and participate in such a complex event. These students were in charge of the organization and management of all the aspects of the event, such as the accommodation of participants or revision of the papers. The works presented in the Conference and the papers submitted were also developed by these students, fomenting their enthusiasm regarding the investigation in the Informatics area. (...)publishe