Explaining How a Neural Network Play the Go Game and Let People Learn

Huilin Zhou, Huijie Tang, Mingjie Li, Hao Zhang, Zhenyu Liu +1 more
10/15/2023
cs.AIcs.CVcs.LG

No confident code implementation found for this paper. 2 possible matches can be shown below.

Abstract

The AI model has surpassed human players in the game of Go, and it is widely believed that the AI model has encoded new knowledge about the Go game beyond human players. In this way, explaining the knowledge encoded by the AI model and using it to teach human players represent a promising-yet-challenging issue in explainable AI. To this end, mathematical supports are required to ensure that human players can learn accurate and verifiable knowledge, rather than specious intuitive analysis. Thus, in this paper, we extract interaction primitives between stones encoded by the value network for the Go game, so as to enable people to learn from the value network. Experiments show the effectiveness of our method.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Learning to Play 7 Wonders Duel Without Human Supervision

82% match2024

ElementaryNet: A Non-Strategic Neural Network for Predicting Human Behavior in Normal-Form Games

81% match1 repo12025

NfgTransformer: Equivariant Representation Learning for Normal-form Games

80% match1 repo92024

Comparative Analysis of GAT and BERT for Human-Like Playtesting

80% match2026

The two-way knowledge interaction interface between humans and neural networks

80% match2024

Learning to Play Video Games with Intuitive Physics Priors

79% match2024

Cite this paper

@article{zhou2023explaining,
  title  = {Explaining How a Neural Network Play the Go Game and Let People Learn},
  author = {Huilin Zhou and Huijie Tang and Mingjie Li and Hao Zhang and Zhenyu Liu and Quanshi Zhang},
  year   = {2023},
  eprint = {2310.09838},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2310.09838v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2310.09838.svg)](https://codeofpaper.com/paper/2310.09838)

Discussion