10 citations · 20 across the 9 of their papers we have counts for
10 papers
Seeding for Success: Skill and Stochasticity in Tabletop Games
James Goodman, Diego Perez-Liebana, Simon Lucas
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…
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…
PyTAG: Tabletop Games for Multi-Agent Reinforcement Learning
Martin Balla, George E. M. Long, James Goodman +2
Modern Tabletop Games present various interesting challenges for Multi-agent Reinforcement Learning. In this paper, we introduce PyTAG, a new framework that supports interacting wi…
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,…
Visualising Multiplayer Game Spaces
James Goodman, Diego Perez-Liebana, Simon Lucas
We compare four different `game-spaces' in terms of their usefulness in characterising multi-player tabletop games, with a particular interest in any underlying change to a game's…
AI and Wargaming
James Goodman, Sebastian Risi, Simon Lucas
Recent progress in Game AI has demonstrated that given enough data from human gameplay, or experience gained via simulations, machines can rival or surpass the most skilled human p…