4 citations · 4 across the 5 of their papers we have counts for
6 papers
Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents
Dominik Jeurissen, Diego Perez-Liebana, Jeremy Gow +2
Large Language Models (LLMs) have shown great success as high-level planners for zero-shot game-playing agents. However, these agents are primarily evaluated on Minecraft, where lo…
PyTAG: Challenges and Opportunities for Reinforcement Learning in Tabletop Games
Martin Balla, George E. M. Long, Dominik Jeurissen +3
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,…
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…
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,…