Explainable Goal-Driven Agents and Robots -- A Comprehensive Review

Deep learning AI systems currently lack transparency in safety-critical applications: The paper identifies that connectionist neural networks, despite their success, function as "black boxes" that cannot explain their decisions—a critical problem for autonomous systems like self-driving cars where trust and safety are paramount.

Explainability requires communicating both perception and reasoning: Effective AI transparency goes beyond data analysis to include explaining how agents perceive their environment (senses, vision) and how they make decisions (beliefs, desires, intentions, plans, and goals), requiring a more comprehensive approach than current XAI research typically addresses.

Goal-driven agents and robots need specialized explainability approaches: The review emphasizes that most existing explainability research focuses on computational data analysis rather than autonomous agents and robots, indicating a significant gap in understanding how to make increasingly deployed autonomous systems interpretable to humans.

Three key strategies enable better AI explainability: The paper highlights that transparency, understandability, and continual learning are essential strategies for creating explainable goal-driven systems—suggesting that one-time explanations are insufficient and AI systems must continuously improve their ability to communicate with humans.

Human-in-the-loop design is central to trustworthy autonomous systems: The emphasis on "humans in the loop" indicates that explainability isn't just a technical feature but requires ongoing human involvement in understanding and validating how autonomous agents and robots make decisions.