Log In Sign Up

Spatial State-Action Features for General Games

by   Dennis J. N. J. Soemers, et al.

In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game's strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. Simple, linear policies of such features are unlikely to produce state-of-the-art playing strength like the deep neural networks that have been more commonly used in recent years do. However, they typically require significantly fewer resources to train, which is paramount for large-scale studies of hundreds to thousands of distinct games. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees.


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

We present a new general board game (GBG) playing and learning framework...

Biasing MCTS with Features for General Games

This paper proposes using a linear function approximator, rather than a ...

Deep Learning for General Game Playing with Ludii and Polygames

Combinations of Monte-Carlo tree search and Deep Neural Networks, traine...

Implicit State and Goals in QBF Encodings for Positional Games (extended version)

We address two bottlenecks for concise QBF encodings of maker-breaker po...

Learning to Play Othello with Deep Neural Networks

Achieving superhuman playing level by AlphaGo corroborated the capabilit...

State Representation and Polyomino Placement for the Game Patchwork

Modern board games are a rich source of entertainment for many people, b...

Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants

In this paper, we use fully convolutional architectures in AlphaZero-lik...