Ikuko Nishikawa
Papers
2
Total Citations
12
H-Index
2
About
Ikuko Nishikawa’s research lies at the intersection of autonomous robotics, decentralized control systems, and cognitive modeling for cooperative behavior. Her most-cited work, “Genetics-Based Machine Learning Approach for Rule Acquisition in an AGV Transportation System” (2008, 8 citations), introduces an innovative method for multiple automated guided vehicles (AGVs) to navigate uncertain delivery requests. By employing a genetics-based machine learning framework, each AGV acts as an autonomous agent that learns collision-free, time-minimizing route plans in real time—a significant contribution to decentralized multi-robot coordination. In her earlier study, “The role that the internal model of the others plays in cooperative behavior” (2004, 4 citations), Nishikawa explores the cognitive underpinnings of cooperation, drawing on “Theory of Mind” to demonstrate how internal models of others’ intentions enable predictive, cooperative actions. This work bridges artificial intelligence and cognitive science, offering insights into how agents—whether robotic or human—can achieve effective collaboration. Though her citation counts are modest, Nishikawa’s research is foundational for scholars interested in adaptive multi-agent systems and the computational modeling of social cognition.
Research Focus
Key Achievements
Top Papers
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