KR2022Proceedings of the 19th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning

Haifa, Israel. July 31–August 5, 2022.

Edited by

ISSN: 2334-1033
ISBN: 978-1-956792-01-0

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Published by

Copyright © 2022 International Joint Conferences on Artificial Intelligence Organization

A Graph Neural Network Reasoner for Game Description Language

  1. Alvaro Gunawan(Auckland University of Technology)
  2. Ji Ruan(Auckland University of Technology)
  3. Xiaowei Huang(University of Liverpool)


  1. Graphical models for knowledge representation and reasoning
  2. Machine-learning-driven reasoning algorithms
  3. Interplay between logic & neural and other learning paradigms
  4. Applications that combine KR with game theory


General Game Playing (GGP) aims to develop agents that are able to play any game with only rules given. The game rules are encoded in the Game Description Language (GDL). A GGP player processes the game rules to obtain game states and expand the game tree search for an optimal move. The recent accomplishments of AlphaGo and AlphaZero have triggered new works in extending neural network approaches to GGP. In these works, the neural networks are used only for optimal move selection, while the components dealing with GDL still use logic-based methods. This motivates us to explore if a neural network based method would be able to approximate the logical inference in GDL with a high accuracy. The structured nature of logic tends to be a difficulty for neural networks, which rely heavily on statistical features. Inspired by the recent works on neural network learning for logical entailments, we propose a neural network based reasoner that is able to learn logical inferences for GDL. We present three key contributions: (i) a general, game-agnostic graph-based representation for game states described in GDL, (ii) methods for generating samples and datasets to frame the GDL inference task as a neural network based machine learning problem and (iii) a GNN based neural reasoner that is able to learn and infer various game states with a high accuracy and has some capability of transfer learning across games.