1 code implementation • 10 Nov 2021 • Andrew Cohen, Ervin Teng, Vincent-Pierre Berges, Ruo-Ping Dong, Hunter Henry, Marwan Mattar, Alexander Zook, Sujoy Ganguly
In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 4 Aug 2019 • Alexander Zook, Mark O. Riedl
We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics---what they do in the game.
no code implementations • 4 Aug 2019 • Alexander Zook, Brent Harrison, Mark O. Riedl
Using these case studies, we show how using simulated agents to model humans of varying skill levels allows us to extract metrics to describe game balance (in the case of Scrabble) and highlight potential design flaws (in the case of Cardonomicon).
no code implementations • 4 Aug 2019 • Alexander Zook, Eric Fruchter, Mark O. Riedl
Through a case study on a shoot-`em-up game we demonstrate the efficacy of active learning to reduce the amount of playtesting needed to choose the optimal set of game parameters for two classes of (formal) design objectives.