Conference

Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning

One of the notorious issues for Reinforcement Learning (RL) is poor sample efficiency. Compared to single agent RL, the sample efficiency for Multi-Agent Reinforcement Learning (MARL) is more challenging because of its inherent partial observability, …

Adaptivity of Card Recommendation Systems for Legends of Code and Magic

The complexity of online games asks for support of new players, e.g., by recommending deck improvements in case of Collectible Card Games. But many online games regularly change the properties of existing game elements to either balance the game …

Controlled Chain of Thought: Eliciting Role-Play Understanding in LLM Through Prompts

Tabletop Role Playing Games (TRPG) are games that require players to become the characters they play through engaging in role-play. The challenge of training AI lies in the requirement of it not only understanding the explicitly stated game rules, …

Game Generation via Large Language Models

Recently, the emergence of large language models (LLMs) has unlocked new opportunities for procedural content generation. However, recent attempts mainly focus on level generation for specific games with defined game rules such as Super Mario Bros. …

Interpreting Multi-objective Evolutionary Algorithms via Sokoban Level Generation

This paper presents an interactive platform to interpret multi-objective evolutionary algorithms. Sokoban level generation is selected as a showcase for its widespread use in procedural content generation. By balancing the emptiness and spatial …

LangBirds: An Agent for Angry Birds using a Large Language Model

The game Angry Birds is a challenging problem for artificial intelligence in that it requires physical reasoning ability. Previous approaches require domain knowledge or playing data, or have limitations in generalization performance. Inspired by …

Measuring Randomness in Tabletop Games

Tabletop games often incorporate random elements in the form of dice or shuffled card decks. This randomness is a key contributor to the player experience and the variety of game situations encountered. There is often a tension between a level of …

Enhancing Large Language Models-Based Code Generation by Leveraging Genetic Improvement

In recent years, the rapid advances in neural networks for Natural Language Processing (NLP) have led to the development of Large Language Models (LLMs), able to substantially improve the state-of-the-art in many NLP tasks, such as question answering …

DanZero: Mastering GuanDan Game with Reinforcement Learning

The use of artificial intelligence (AI) in card games has been a widely researched topic in the field of AI for an extended period. Recent advancements have led to AI programs exhibiting expert-level gameplay in complex card games such as Mahjong, …

Evolution of Walsh Transforms with genetic programming

The design of Boolean functions which exhibit high-quality cryptography properties is a crucial aspect when implementing secure stream ciphers. To this end, several methods have been proposed to search for secure Boolean functions, and, among those, …