4 citations · 9 across the 9 of their papers we have counts for
11 papers
Toxicity in Twitch Chats: An LLM-Based Analysis Across Gaming Communities
Ronja Fuchs, Florian Rupp, Timo Bertram +2
Toxicity in online gaming communities remains a persistent challenge, manifesting across genres, platforms, and player interactions. While much research is focused on in-game toxic…
From Code to Play: Benchmarking Program Search for Games Using Large Language Models
Manuel Eberhardinger, James Goodman, Alexander Dockhorn +5
Large language models (LLMs) have shown impressive capabilities in generating program code, opening exciting opportunities for applying program synthesis to games. In this work, we…
Strategy Game-Playing with Size-Constrained State Abstraction
Linjie Xu, Diego Perez-Liebana, Alexander Dockhorn
Playing strategy games is a challenging problem for artificial intelligence (AI). One of the major challenges is the large search space due to a diverse set of game components. In…
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning
Linjie Xu, Zichuan Liu, Alexander Dockhorn +4
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 (MA…
Elastic Monte Carlo Tree Search with State Abstraction for Strategy Game Playing
Linjie Xu, Jorge Hurtado-Grueso, Dominic Jeurissen +2
Strategy video games challenge AI agents with their combinatorial search space caused by complex game elements. State abstraction is a popular technique that reduces the state spac…
Portfolio Search and Optimization for General Strategy Game-Playing
Alexander Dockhorn, Jorge Hurtado-Grueso, Dominik Jeurissen +2
Portfolio methods represent a simple but efficient type of action abstraction which has shown to improve the performance of search-based agents in a range of strategy games. We fir…
Generating Diverse and Competitive Play-Styles for Strategy Games
Diego Perez-Liebana, Cristina Guerrero-Romero, Alexander Dockhorn +3
Designing agents that are able to achieve different play-styles while maintaining a competitive level of play is a difficult task, especially for games for which the research commu…
Design and Implementation of TAG: A Tabletop Games Framework
Raluca D. Gaina, Martin Balla, Alexander Dockhorn +2
This document describes the design and implementation of the Tabletop Games framework (TAG), a Java-based benchmark for developing modern board games for AI research. TAG provides…
The Design Of "Stratega": A General Strategy Games Framework
Diego Perez-Liebana, Alexander Dockhorn, Jorge Hurtado Grueso +1
Stratega, a general strategy games framework, has been designed to foster research on computational intelligence for strategy games. In contrast to other strategy game frameworks,…
Learning Local Forward Models on Unforgiving Games
Alexander Dockhorn, Simon M. Lucas, Vanessa Volz +3
This paper examines learning approaches for forward models based on local cell transition functions. We provide a formal definition of local forward models for which we propose two…
A Local Approach to Forward Model Learning: Results on the Game of Life Game
Simon M. Lucas, Alexander Dockhorn, Vanessa Volz +6
This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent. We transform Conway's Game of Life simulation into a sing…