Yoshimasa Ohmoto

About

Yoshimasa Ohmoto’s research lies at the intersection of human-agent interaction, computational behavior modeling, and affective robotics. He is best known for developing frameworks that enable agents and robots to perceive, adapt to, and reason about human behavior and emotions in real time. His notable contribution, the G-SteX algorithm (2012, 9 citations), introduced a greedy stem extension method for free-length constrained motif discovery, advancing pattern detection in behavioral sequences. Earlier, Ohmoto designed the Capture and Express Behavior Environment (CEBE, 2010, 7 citations), a platform for realizing enculturating human-agent interaction, and a collaborative mutually adaptive agent system (2009, 4 citations). His work on the CPMD toolbox (2012, 3 citations) provided researchers with a practical tool for change point and motif discovery. More recently, Ohmoto proposed a three-component computation mechanism for situated sentient robots (2017, 2 citations), enabling machines to reason about how emotions shape perception, memory, and decision-making. Through these contributions, Ohmoto has laid groundwork for more intuitive, emotionally aware autonomous systems.

Research Focus

Key Achievements

Top Papers

1. 1
   G-SteX: Greedy Stem Extension for Free-Length Constrained Motif Discovery

   9 citations · 2012
2. 2
   Capture and Express Behavior Environment (CEBE) for Realizing Enculturating Human-Agent Interaction

   7 citations · 2010
3. 3
   A Platform System for Developing a Collaborative Mutually Adaptive Agent

   4 citations · 2009
4. 4
   CPMD: A Matlab Toolbox for Change Point and Constrained Motif Discovery

   3 citations · 2012
5. 5
   Computation mechanism for situated sentient robot

   2 citations · 2017

Key Collaborators

Contact & Links