# General Board Game Playing for Education and Research in Generic AI Game Learning

## TL;DR

This paper introduces a versatile framework for board game AI research and education, enabling standardized competition and learning across various games with a novel generic agent outperforming traditional methods.

## Contribution

It presents GBG, a standardized framework for multi-player board game AI, and introduces a generic TD(λ)-n-tuple agent applicable to arbitrary games, advancing research and education.

## Key findings

- TD(λ)-n-tuple outperforms MCTS in various games
- GBG facilitates faster learning for students in game AI
- Initial experiments show successful educational and research outcomes

## Abstract

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($\lambda$)-n-tuple agent for the first time available to arbitrary games. On various games, TD($\lambda$)-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.