Towards Providing Explanations for AI Planner Decisions

Explainability is essential for trust in AI systems: The paper emphasizes that humans need to understand both what an AI system aims to accomplish and why it makes specific decisions—a fundamental requirement for building trust in AI planning systems.

AI Planning is uniquely suited for explanation: Unlike many AI approaches, AI planning systems have inherent structure that makes them well-positioned to provide transparent and comprehensible justifications for their decisions to users.

Comparative explanations help users understand AI choices: The paper's methodology allows users to propose alternative actions and compare them against the planner's chosen solution, helping people understand why the AI selected its particular course of action rather than other possibilities.

The XAI-Plan framework makes explanations practical: The researchers implemented their approach in a new framework called XAI-Plan, demonstrating that explainable AI planning can move from theory into working systems that users can actually interact with.

User-centered design improves AI comprehension: By letting users actively suggest alternatives and see comparisons, the methodology treats explanation as an interactive process rather than a one-way communication, potentially making AI decisions more understandable to non-technical users.