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About

Yuki Goto is a researcher specializing in logic-based artificial intelligence, autonomous robotics, and multi-valued reasoning systems. Their work sits at the intersection of formal logic and practical robotics, addressing one of the field's central challenges: enabling robots to make reliable decisions in the unpredictable complexity of the real world. Goto's most significant contributions include developing logic-based frameworks for autonomous robot decision-making that remain robust against environmental noise and sensory disturbances. Their 2014 work introduced a formal mechanism allowing robots to reason about goal achievement even under uncertain conditions, while their 2013 architecture tackled the embodiment problem by enabling robots to dynamically adapt plans in response to continuous environmental changes. A notable 2016 contribution advanced autonomous navigation by having robots construct logical map representations from perception to guide their own pathfinding. Perhaps most distinctively, Goto extended this work into paraconsistent logic, with their 2018 paper implementing 3-valued paraconsistent logic programming to support agent decision-making systems — a sophisticated approach allowing reasoning to proceed meaningfully even amid contradictory information. With a growing citation record across these works, Goto's research offers foundational insights for students and engineers working on intelligent autonomous systems operating in real-world environments.