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About

Ben Meadows is a leading researcher in explainable artificial intelligence (XAI) and human-robot collaboration, with a focus on building autonomous systems that can reason about and communicate their decisions. His most influential work, "Explainable Agency for Intelligent Autonomous Systems" (153 citations), addresses the critical challenge of making opaque machine learning models interpretable as intelligent agents become more autonomous and prevalent. Meadows has developed foundational theories for explanation generation in robotics, including a comprehensive theory of explanations for human-robot collaboration that enables robots to describe their decisions, knowledge, and beliefs. His research uniquely integrates declarative programming with relational reinforcement learning to help robots discover domain axioms and reason about affordances—what actions are possible in a given context. Through his architecture for reasoning about and learning affordances, Meadows has advanced the ability of robots to represent and reason with incomplete domain knowledge. His work on explanation generation systems has established a multidimensional framework for characterizing how robots can explain unexpected observations and partial sensor data, making significant contributions to building more transparent and trustworthy autonomous systems.