299 research outputs found
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
Mastering Strategy Card Game (Hearthstone) with Improved Techniques
Strategy card game is a well-known genre that is demanding on the intelligent
game-play and can be an ideal test-bench for AI. Previous work combines an
end-to-end policy function and an optimistic smooth fictitious play, which
shows promising performances on the strategy card game Legend of Code and
Magic. In this work, we apply such algorithms to Hearthstone, a famous
commercial game that is more complicated in game rules and mechanisms. We
further propose several improved techniques and consequently achieve
significant progress. For a machine-vs-human test we invite a Hearthstone
streamer whose best rank was top 10 of the official league in China region that
is estimated to be of millions of players. Our models defeat the human player
in all Best-of-5 tournaments of full games (including both deck building and
battle), showing a strong capability of decision making.Comment: cog2023 ful
Game-based Platforms for Artificial Intelligence Research
Games have been the perfect test-beds for artificial intelligence research
for the characteristics that widely exist in real-world scenarios. Learning and
optimisation, decision making in dynamic and uncertain environments, game
theory, planning and scheduling, design and education are common research areas
shared between games and real-world problems. Numerous open-sourced games or
game-based environments have been implemented for studying artificial
intelligence. In addition to single- or multi-player, collaborative or
adversarial games, there has also been growing interest in implementing
platforms for creative design in recent years. Those platforms provide ideal
benchmarks for exploring and comparing artificial intelligence ideas and
techniques. This paper reviews the game-based platforms for artificial
intelligence research, discusses the research trend induced by the evolution of
those platforms, and gives an outlook
Symbolic Reasoning for Hearthstone
Trading-Card-Games are an interesting problem domain for Game AI, as they feature some challenges, such as highly variable game mechanics, that are not encountered in this intensity in many other genres. We present an expert system forming a player-level AI for the digital Trading-Card-Game Hearthstone. The bot uses a symbolic approach with a semantic structure, acting as an ontology, to represent both static descriptions of the game mechanics and dynamic game-state memories. Methods are introduced to reduce the amount of expert knowledge, such as popular moves or strategies, represented in the ontology, as the bot should derive such decisions in a symbolic way from its knowledge base. We narrow down the problem domain, selecting the relevant aspects for a play-to-win bot approach and comparing an ontology-driven approach to other approaches such as machine learning and case-based reasoning. Upon this basis, we describe how the semantic structure is linked with the game-state and how different aspects, such as memories, are encoded. An example will illustrate how the bot, at runtime, uses rules and queries on the semantic structure combined with a simple utility system to do reasoning and strategic planning. Finally, an evaluation is presented that was conducted by fielding the bot against the stock “Expert” AI that Hearthstone is shipped with, as well as Human opponents of various skill levels in order to assess how well the bot plays. Evaluating how believable the bot reasons is assessed through a Pseudo-Turing test
Counterfactual Regret Minimization を用いたトレーディングカードゲームの戦略計算
学位の種別: 修士University of Tokyo(東京大学
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