Papers Card Games
“Card Games” 태그가 달린 논문 52편 · 필터 해제
Reinforcement Learning for Hanabi
Hanabi has become a popular game for research when it comes to reinforcement learning (RL) as it is one of the few cooperative card games where you have incomplete knowledge of the entire environment, thus presenting a c…
Card GamesDeep Reinforcement LearningQ-Learningreinforcement-learning+2SPIN-Bench: How Well Do LLMs Plan Strategically and Reason Socially?
Reasoning and strategic behavior in social interactions is a hallmark of intelligence. This form of reasoning is significantly more sophisticated than isolated planning or reasoning tasks in static settings (e.g., math p…
Board GamesCard GamesMathGTR: Guided Thought Reinforcement Prevents Thought Collapse in RL-based VLM Agent Training
Reinforcement learning with verifiable outcome rewards (RLVR) has effectively scaled up chain-of-thought (CoT) reasoning in large language models (LLMs). Yet, its efficacy in training vision-language model (VLM) agents f…
Card GamesGFlowVLM: Enhancing Multi-step Reasoning in Vision-Language Models with Generative Flow Networks
Vision-Language Models (VLMs) have recently shown promising advancements in sequential decision-making tasks through task-specific fine-tuning. However, common fine-tuning methods, such as Supervised Fine-Tuning (SFT) an…
Card GamesDiversityReinforcement Learning (RL)Sequential Decision MakingCardiverse: Harnessing LLMs for Novel Card Game Prototyping
The prototyping of computer games, particularly card games, requires extensive human effort in creative ideation and gameplay evaluation. Recent advances in Large Language Models (LLMs) offer opportunities to automate an…
Card GamesCode GenerationOptimizing Hearthstone Agents using an Evolutionary Algorithm
Digital collectible card games are not only a growing part of the video game industry, but also an interesting research area for the field of computational intelligence. This game genre allows researchers to deal with hi…
Card GamesDecision MakingEvolutionary AlgorithmsSelf-LearningA Taxonomy of Collectible Card Games from a Game-Playing AI Perspective
Collectible card games are challenging, widely played games that have received increasing attention from the AI research community in recent years. Despite important breakthroughs, the field still poses many unresolved c…
Card GamesIdentifying and Clustering Counter Relationships of Team Compositions in PvP Games for Efficient Balance Analysis
How can balance be quantified in game settings? This question is crucial for game designers, especially in player-versus-player (PvP) games, where analyzing the strength relations among predefined team compositions-such …
Card GamesGame DesignQuantizationReal-Time Strategy Games+1AlphaDou: High-Performance End-to-End Doudizhu AI Integrating Bidding
Artificial intelligence for card games has long been a popular topic in AI research. In recent years, complex card games like Mahjong and Texas Hold'em have been solved, with corresponding AI programs reaching the level …
Card GamesContrastive Learning of Preferences with a Contextual InfoNCE Loss
A common problem in contextual preference ranking is that a single preferred action is compared against several choices, thereby blowing up the complexity and skewing the preference distribution. In this work, we show ho…
Card GamesContrastive LearningTripletLearning With Generalised Card Representations for "Magic: The Gathering"
A defining feature of collectable card games is the deck building process prior to actual gameplay, in which players form their decks according to some restrictions. Learning to build decks is difficult for players and m…
Card GamesEnhancing Commentary Strategies for Imperfect Information Card Games: A Study of Large Language Models in Guandan Commentary
Recent advancements in large language models (LLMs) have unlocked the potential for generating high-quality game commentary. However, producing insightful and engaging commentary for complex games with incomplete informa…
Card GamesReinforcement Learning (RL)RetrievalLearning to Beat ByteRL: Exploitability of Collectible Card Game Agents
While Poker, as a family of games, has been studied extensively in the last decades, collectible card games have seen relatively little attention. Only recently have we seen an agent that can compete with professional hu…
Card GamesTransformer Based Planning in the Observation Space with Applications to Trick Taking Card Games
Traditional search algorithms have issues when applied to games of imperfect information where the number of possible underlying states and trajectories are very large. This challenge is particularly evident in trick-tak…
Card GamesGTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial. This paper evaluates LLMs' reasoning abilities in competitiv…
Card GamesLogical ReasoningDanZero+: Dominating the GuanDan Game through Reinforcement Learning
The utilization of artificial intelligence (AI) in card games has been a well-explored subject within AI research for an extensive period. Recent advancements have propelled AI programs to showcase expertise in intricate…
Card Gamesreinforcement-learningReinforcement LearningTwo-Step Reinforcement Learning for Multistage Strategy Card Game
In the realm of artificial intelligence and card games, this study introduces a two-step reinforcement learning (RL) strategy tailored for "The Lord of the Rings: The Card Game (LOTRCG)," a complex multistage strategy ca…
Card GamesDecision Makingreinforcement-learningReinforcement Learning+1History Filtering in Imperfect Information Games: Algorithms and Complexity
Historically applied exclusively to perfect information games, depth-limited search with value functions has been key to recent advances in AI for imperfect information games. Most prominent approaches with strong theore…
Card GamesDecision MakingSequential Decision MakingSuspicion-Agent: Playing Imperfect Information Games with Theory of Mind Aware GPT-4
Unlike perfect information games, where all elements are known to every player, imperfect information games emulate the real-world complexities of decision-making under uncertain or incomplete information. GPT-4, the rec…
Card GamesDecision MakingPrompt EngineeringPyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
In recent years, Game AI research has made important breakthroughs using Reinforcement Learning (RL). Despite this, RL for modern tabletop games has gained little to no attention, even when they offer a range of unique c…
Board GamesCard Gamesreinforcement-learningReinforcement Learning+2