Information based explanation methods for deep learning agents-with applications on large open-source chess models
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View abstract on PubMed
Summary
This summary is machine-generated.Researchers re-implemented concept detection for chess AI using open-source models. A novel explainable AI (XAI) method provides guaranteed visual explanations for discrete input domains like chess.
Area Of Science
- Artificial Intelligence
- Computer Science
- Computational Game Theory
Background
- Large neural network models like AlphaZero achieve state-of-the-art performance in computer chess.
- Challenges include explaining the internal knowledge of these models and their lack of open availability.
- Existing explainable AI (XAI) methods may not provide exhaustive or exclusive information guarantees.
Purpose Of The Study
- To re-implement the concept detection methodology applied to AlphaZero using open-source chess models.
- To develop a novel XAI method for explaining AI models in discrete input spaces.
- To provide strict guarantees on the information used by AI models during inference.
Main Methods
- Re-implementation of a concept detection methodology on large, open-source chess models with comparable performance to AlphaZero.
- Development of a novel XAI method that controls information flow between input and model.
- Application and demonstration of the XAI method on standard chess using open-source models.
Main Results
- Achieved results comparable to those obtained when applying the methodology to AlphaZero, using only open-source resources.
- The novel XAI method generates visual explanations guaranteed to highlight exhaustively and exclusively the information used by the model.
- Demonstrated the viability of the XAI method for explaining AI models in discrete input domains like chess.
Conclusions
- The re-implementation validates the concept detection methodology using accessible, open-source chess AI.
- The novel XAI method offers a robust approach to understanding AI decision-making in discrete domains.
- This work contributes to more transparent and explainable AI in complex strategic games.
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