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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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A Bayesian Network Approach to Explainable Reinforcement Learning with Distal Information.

Rudy Milani¹, Maximilian Moll¹, Renato De Leone²

¹Faculty of Computer Science, Universitaet der Bundeswehr Muenchen, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

This study automates explanations for Artificial Intelligence (AI) decisions in Reinforcement Learning (RL) using Bayesian Networks. The AI model explains its choices, increasing user trust and transparency.

Keywords:

Bayesian Network Explainable Reinforcement Learning causal explanation human study model-free methods

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Area of Science:

Artificial Intelligence
Machine Learning
Reinforcement Learning

Background:

Understanding AI decision-making is crucial for user trust and model transparency.
Current methods for explaining AI actions often require significant user input.

Purpose of the Study:

To automate the generation of explanations for model-free Reinforcement Learning (RL) algorithms.
To answer "why" and "why not" questions regarding AI agent actions.
To enhance transparency and user trust in AI systems.

Main Methods:

Utilized Bayesian Networks combined with the NOTEARS algorithm for automatic structure learning.
Developed a framework to generate explanations with minimal user intervention.
Computationally evaluated the approach on three benchmarks with diverse RL methods.

Main Results:

The explanation framework demonstrated independence from the specific RL model used.
Human studies rated the generated explanations, showing increased understanding and trust.
Performance was compared favorably against baseline explanation models.

Conclusions:

The proposed method successfully automates AI explanations, enhancing transparency and trust.
This approach represents a significant step towards explainable AI (XAI) with reduced user input.
The framework's effectiveness is validated across different RL algorithms and through human evaluation.