Conference

Exploring the Integration of Cellular Structures in Genetic Programming-Based Methods

The introduction of a Cellular Automata (CA)-like structure on the population of Evolutionary Algorithms (EAs) has been verified to be a method to improve solutions quality. However, the study of CA-like structures for Genetic Programming (GP) has …

3D Building Generation in Minecraft via Large Language Models

Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs). To further validate the capabilities of LLMs, this …

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, …

“Hey Players, there is a problem…”: On Attribute Inference Attacks against Videogamers

We focus on a subtle privacy issue that affects (potentially hundreds of) millions of videogamers: attribute inference attacks (AIA). Through AIA, evildoers can infer gamers’ private attributes (e.g., age, gender, occupation) by leveraging ingame …

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, …

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 …