Understanding, Explanation, and Active Inference.

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Computational Neuroscience

Background:

• Machine learning models often lack transparency in their decision-making processes.
• Explainable AI (XAI) is crucial for understanding and trusting AI systems.
• Active inference offers a framework for understanding decision-making based on generative models.

Purpose of the Study:

• To investigate machine understanding through the lens of active inference.
• To develop a computational framework for explaining AI actions.
• To explore the parallels between machine and human understanding.

Main Methods:

• Utilizing active inference and deep generative models.
• Developing a hierarchical generative model to infer policies and counterfactual explanations.
• Comparing the computational architecture and dysfunction consequences with human understanding.

Main Results:

• Proposed a novel approach to machine understanding via active inference.
• Demonstrated how generative models can explain AI actions.
• Highlighted computational similarities between the proposed model and human understanding.

Conclusions:

• Active inference provides a viable paradigm for achieving machine understanding.
• The developed framework offers a pathway for explainable AI.
• The study suggests a shared computational basis for understanding in both AI and humans.