18 citations · 51 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…
Shallow decision-making analysis in General Video Game Playing
Ivan Bravi, Jialin Liu, Diego Perez-Liebana +1
The General Video Game AI competitions have been the testing ground for several techniques for game playing, such as evolutionary computation techniques, tree search algorithms, hy…
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Vanessa Volz, Jacob Schrum, Jialin Liu +3
Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content…
Evolving Game Skill-Depth using General Video Game AI Agents
Jialin Liu, Julian Togelius, Diego Perez-Liebana +1
Most games have, or can be generalised to have, a number of parameters that may be varied in order to provide instances of games that lead to very different player experiences. The…
Learning opening books in partially observable games: using random seeds in Phantom Go
Tristan Cazenave, Jialin Liu, Fabien Teytaud +1
Many artificial intelligences (AIs) are randomized. One can be lucky or unlucky with the random seed; we quantify this effect and show that, maybe contrarily to intuition, this is…