17 citations · 22 across the 4 of their papers we have counts for
7 papers · 1 filter
Rinascimento: searching the behaviour space of Splendor
Ivan Bravi, Simon Lucas
The use of Artificial Intelligence (AI) for play-testing is still on the sidelines of main applications of AI in games compared to performance-oriented game-playing. One of the mai…
Rinascimento: using event-value functions for playing Splendor
Ivan Bravi, Simon Lucas
In the realm of games research, Artificial General Intelligence algorithms often use score as main reward signal for learning or playing actions. However this has shown its severe…
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…
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Ivan Bravi, Simon Lucas, Diego Perez-Liebana +1
Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques. Providing diverse challenges is crucial to push research to…
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…
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
Simon M. Lucas, Jialin Liu, Ivan Bravi +4
This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars…