Conference

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, …

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, …

Examining the Role of Incentives in Scholarly Publishing with Multi-Agent Reinforcement Learning

Scientific research plays a crucial role in advancing human civilization, thanks to the efforts of a multitude of individual actors. Their behavior is largely driven by individual incentives, both explicit and implicit. In this paper, we propose and …

Compressing and Comparing the Generative Spaces of Procedural Content Generators

The past decade has seen a rapid increase in the level of research interest in procedural content generation (PCG) for digital games, and there are now numerous research avenues focused on new approaches for driving and applying PCG systems. An area …

A generalizability measure for program synthesis with genetic programming

The generalizability of programs synthesized by genetic programming (GP) to unseen test cases is one of the main challenges of GP-based program synthesis. Recent work showed that increasing the amount of training data improves the generalizability of …

Genetic Adversarial Training of Decision Trees

We put forward a novel learning methodology for ensembles of decision trees based on a genetic algorithm that is able to train a decision tree for maximizing both its accuracy and its robustness to adversarial perturbations. This learning algorithm …

Model Learning with Personalized Interpretability Estimation (ML-PIE)

High-stakes applications require AI-generated models to be interpretable. Current algorithms for the synthesis of potentially interpretable models rely on objectives or regularization terms that represent interpretability only coarsely (e.g., model …