General Board Game Playing for Education and Research in Generic AI Game Learning
This framework addresses the problem of tedious coding and lack of standardization in AI game learning for education and research, though it is incremental in building on existing agent methods.
The authors introduced a general board game (GBG) framework to standardize interfaces for board games and AI agents, enabling competitions and reducing repetitive coding. They reported that the TD(λ)-n-tuple agent outperformed other generic agents like MCTS on various games.
We present a new general board game (GBG) playing and learning framework. GBG defines the common interfaces for board games, game states and their AI agents. It allows one to run competitions of different agents on different games. It standardizes those parts of board game playing and learning that otherwise would be tedious and repetitive parts in coding. GBG is suitable for arbitrary 1-, 2-, ..., N-player board games. It makes a generic TD($λ$)-n-tuple agent for the first time available to arbitrary games. On various games, TD($λ$)-n-tuple is found to be superior to other generic agents like MCTS. GBG aims at the educational perspective, where it helps students to start faster in the area of game learning. GBG aims as well at the research perspective by collecting a growing set of games and AI agents to assess their strengths and generalization capabilities in meaningful competitions. Initial successful educational and research results are reported.