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Related Experiment Video

Updated: Sep 26, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Prediction, Knowledge, and Explainability: Examining the Use of General Value Functions in Machine Knowledge.

Alex Kearney1, Johannes Günther1,2, Patrick M Pilarski1,2,3

  • 1Reinforcement Learning and Artificial Intelligence Lab, Department of Computing Science, University of Alberta, Edmonton, AB, Canada.

Frontiers in Artificial Intelligence
|April 18, 2022
PubMed
Summary
This summary is machine-generated.

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General Value Functions (GVFs) can be a form of explainable AI. By introspectively explaining their predictions, agents improve clarity in collaborative tasks.

Area of Science:

  • Computational Reinforcement Learning
  • Artificial Intelligence
  • Explainable AI (XAI)

Background:

  • Growing research in computational reinforcement learning focuses on agents encoding world knowledge via predictions.
  • General Value Functions (GVFs) are a prominent method for representing these predictions, with significant theoretical and applied advancements.
  • The explainability of GVF-based systems remains an underexplored area.

Purpose of the Study:

  • To explore the potential of General Value Functions (GVFs) as a framework for explainable AI.
  • To propose a subjective, agent-centric approach to explainability in sequential decision-making.
  • To investigate the role of self-explanation in an agent's decision-making process.

Main Methods:

  • Articulating a subjective, agent-centric perspective on explainability for sequential decision-making.
Keywords:
General Value Functionsexplainabilityknowledgepredictionreinforcement learning

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  • Reviewing existing applications of GVFs, particularly those involving human-agent collaboration.
  • Analyzing how agents can introspectively explain their own predictions.

    • Main Results:

    • GVFs can serve as a mechanism for achieving explainable AI.
    • An agent's ability to self-explain its predictions is crucial for external explainability.
    • Making subjective explanations public enhances operational clarity in collaborative scenarios.

    Conclusions:

    • General Value Functions offer a promising avenue for developing explainable AI systems.
    • Explainability in AI can be framed through an agent's internal, subjective understanding of its predictions.
    • Publicly sharing these subjective explanations can significantly improve human-agent collaboration and system transparency.