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arXiv CS.AI·16d ago·5 min read
From Black Box to Executable Logic: Explainable Reinforcement Learning through Prolog Expert Systems
TL;DR
Reinforcement learning models often operate as black boxes, making their decision processes opaque. This research introduces a method to translate these models into Prolog expert systems, enabling explainability and transparency.
✦ Why It Matters
Implement Prolog expert systems to enhance the explainability of your reinforcement learning models today.
Key Takeaways
- Convert RL policies to Prolog for better interpretability.
- Utilize rule-based systems to explain AI decisions.
- Enhance trust in AI applications through transparency.
- Apply this framework in critical sectors like healthcare.
Full Summary
Reinforcement learning (RL) has gained popularity for its ability to learn optimal actions through trial and error, but its black box nature limits understanding of decision-making processes. This study presents a novel approach that converts RL policies into Prolog expert systems, which are rule-based systems that can provide logical explanations for their decisions.
The methodology involves extracting the learned policies from RL agents and encoding them in Prolog, allowing for human-readable explanations. Results demonstrate that this approach not only improves interpretability but also retains the performance of the original RL models.
By providing a clear logical framework, the research facilitates better trust and accountability in AI systems. This work has significant implications for fields requiring transparent AI, such as healthcare and finance, where understanding decision-making is crucial.