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Towards Providing Explanations for AI Planner Decisions
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In order to engender trust in AI, humans must understand what an AI system is trying to achieve, and why. To overcome this problem, the underlying AI process must produce justifications and explanations that are both transparent and comprehensible to the user. AI Planning is well placed to be able to address this challenge. In this paper we present a methodology to provide initial explanations for the decisions made by the planner. Explanations are created by allowing the user to suggest alternative actions in plans and then compare the resulting plans with the one found by the planner. The methodology is implemented in the new XAI-Plan framework.
Forward citations
Cited by 3 Pith papers
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Counterfactual Explanations as Plans
A formal account in modal situation calculus that defines counterfactual explanations as minimally distant alternative plans which toggle a goal, including reconciliation through added knowledge or corrected beliefs.
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Generating Explanations for Autonomous Robots: a Systematic Review
A systematic review of 22 papers maps explanation-generation methods for autonomous robots and finds no standardized way to evaluate them.
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Model-Based AI planning and Execution Systems for Robotics
A structured survey of model-based AI planning and execution systems for robotics, comparing ROSPlan, CLIPS Executive, PlanSys2, SkiROS2, the Skill-Based Architecture, and AOS, and proposing a future research agenda.
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