Search Results for author:
Found 7 papers, 5 papers with code
Rinascimento: searching the behaviour space of Splendor
One of the main purposes of play-testing a game is gathering data on the gameplay, highlighting good and bad features of the design of the game, providing useful insight to the game designers for improving the design.
Rinascimento: using event-value functions for playing Splendor
These events are processed by an Event-value Function (EF) that assigns a value to a single action or a sequence.
Learning Local Forward Models on Unforgiving Games
This paper examines learning approaches for forward models based on local cell transition functions.
Rinascimento: Optimising Statistical Forward Planning Agents for Playing Splendor
Game-based benchmarks have been playing an essential role in the development of Artificial Intelligence (AI) techniques.
A Local Approach to Forward Model Learning: Results on the Game of Life Game
This paper investigates the effect of learning a forward model on the performance of a statistical forward planning agent.
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best
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.
Shallow decision-making analysis in General Video Game Playing
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, hyper heuristic based or knowledge based algorithms.
