activity
20182021
most citedAgent-Based Adaptive Level Generation for Dynamic Difficulty Adjustment in Angry Birds

6 citations · 15 across the 8 of their papers we have counts for

collaborators

21 papers

cs.AI2021

Optimised Playout Implementations for the Ludii General Game System

Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson +1

This paper describes three different optimised implementations of playouts, as commonly used by game-playing algorithms such as Monte-Carlo Tree Search. Each of the optimised imple…

cs.AI2021

Automatic Generation of Board Game Manuals

Matthew Stephenson, Eric Piette, Dennis J. N. J. Soemers +1

In this paper we present a process for automatically generating manuals for board games within the Ludii general game system. This process requires many different sub-tasks to be a…

cs.AI2021

General Board Game Concepts

Éric Piette, Matthew Stephenson, Dennis J. N. J. Soemers +1

Many games often share common ideas or aspects between them, such as their rules, controls, or playing area. However, in the context of General Game Playing (GGP) for board games,…

cs.AI2021

Deceptive Level Generation for Angry Birds

Chathura Gamage, Matthew Stephenson, Vimukthini Pinto +1

The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many differe…

cs.AI2021

General Game Heuristic Prediction Based on Ludeme Descriptions

Matthew Stephenson, Dennis J. N. J. Soemers, Eric Piette +1

This paper investigates the performance of different general-game-playing heuristics for games in the Ludii general game system. Based on these results, we train several regression…

cs.LG2020

Manipulating the Distributions of Experience used for Self-Play Learning in Expert Iteration

Dennis J. N. J. Soemers, Éric Piette, Matthew Stephenson +1

Expert Iteration (ExIt) is an effective framework for learning game-playing policies from self-play. ExIt involves training a policy to mimic the search behaviour of a tree search…