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Mikhail Soutchanski is a leading researcher in artificial intelligence and robotics, specializing in high-level robot programming, decision-theoretic planning, and the situation calculus. His most influential work, "Decision-Theoretic, High-Level Agent Programming in the Situation Calculus" (2000, 195 citations), introduced the DTGolog model—a groundbreaking framework that seamlessly integrates explicit agent programming with decision-theoretic planning. This allows robots to partially specify control programs in a high-level logical language while an interpreter optimizes actions under uncertainty. Soutchanski also pioneered execution monitoring for robots operating online, enabling them to adapt to exogenous events and continue task execution despite unexpected changes in the world (1998, 102 citations). His research further advanced incremental execution and search control in Golog programs (2001, 25 citations), and his doctoral thesis (2005, 12 citations) provided a comprehensive logic-based approach to robot control in dynamic, incompletely known environments. With over 330 total citations, Soutchanski’s work has profoundly influenced the fields of autonomous robotics and AI, offering theoretical frameworks and practical tools for building resilient, intelligent agents.