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

Balancing Wargames through Predicting Unit Point Costs

In tactical wargames, such as Warhammer 40K, two or more players control asymmetrical armies that include multiple units of different types and strengths. In these type of games, unit are assigned point costs, which are used to ensure that all players will control armies of similar strength. Players are provided with a total budget of points they can spend to purchase units that will be part of their army lists. Calculating the point value of individual units is a tedious manual process, which often requires long play-testing sessions and iterations of adjustments. In this paper, we propose an automated way of predicting these point costs using a linear regression approach. We use a multi-unit, turn-based, non-balanced game that has three asymmetric armies. We use Monte Carlo Tree Search agents to simulate the players, using different heuristics to emulate playing strategies. We present six different variants of our unit-point prediction algorithm, and we show how our best variant is able to almost reduce the unbalanced nature of the game by half

Rinascimento: Playing Splendor-Like Games with Event-Value Functions

In the realm of games research, artificial general intelligence algorithms often use score as the main reward signal for learning or playing actions. However, this has shown its limitations in scenarios where the rewards are very rare or absent until the end of the game. The problem is even more severe when the computational budget available is limited. This article proposes a new approach based on event logging: the game state triggers an event every time one of its features changes. These events are processed by an event-value function (EF) that assigns a value to a single action or a sequence. Experiments show that this approach can mitigate the problem of scarce rewards and improve the artificial intelligence performance compared with both the point-based heuristics and state-value functions. Furthermore, this represents a step forward in a finer control of the strategy adopted by the artificial agent, by describing a much richer and controllable behavioral space through EFs. Tuned EFs are able to neatly synthesize the relevance of the events in the game. Agents using an EF are also more robust when playing games with several opponents

An Ecosystem Framework for the Meta in Esport Games

This paper examines the evolving landscape of modern digital games, emphasizing their nature as live services that continually evolve and adapt. In addition to engaging with the core gameplay, players and other stakeholders actively participate in various game-related experiences, such as tournaments and streaming. This interplay forms a vibrant and intricate ecosystem, facilitating the construction and dissemination of knowledge about the game. Such knowledge flow, accompanied by resulting behavioral changes, gives rise to the concept of a video game meta. Within the competitive gaming context, the meta represents the strategic and tactical knowledge that goes beyond the fundamental mechanics of the game, enabling players to gain a competitive advantage. We present a review of the state-of-the-art of knowledge for game metas and propose a novel model for the meta knowledge structure and propagation that accounts for this ecosystem, based on a review of the academic literature and practical examples. By exploring the dynamics of knowledge exchange and its influence on gameplay, the review presented here sheds light on the intricate relationship between game evolution, player engagement, and the associated emergence of game meta

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