Hisham Alsubeheen
Papers
1
Total Citations
3
H-Index
1
About
Hisham Alsubeheen is a researcher whose work sits at the fascinating intersection of cognitive science and robotics, exploring how human-like decision-making can enhance machine learning. His key research areas include cognitive biases, robotic motion learning, and the application of psychological models to artificial intelligence. Alsubeheen’s major contribution lies in proposing the use of symmetric cognitive biases—specifically, the human tendency to infer “if p then q” and “if not q then not p” from “if q then p”—to improve robotic learning processes. By applying the Shinohara model, which quantitatively represents these illogical yet efficient human reasoning patterns, his work offers a novel pathway for robots to learn motion more intuitively and adaptively. His most-cited paper, “The efficacy of symmetric cognitive biases in robotic motion learning” (2011), has garnered 3 citations, reflecting its niche but foundational impact in the field. This research bridges human psychology and artificial intelligence, opening doors for more natural human-robot interactions. Alsubeheen’s innovative approach continues to inspire those interested in how cognitive quirks can be harnessed to advance machine learning and robotics.
Research Focus
Key Achievements
Top Papers
- 1The efficacy of symmetric cognitive biases in robotic motion learning3 citations · 2011