Seng-Beng Ho

Agency for Science, Technology and Research, Institute of High Performance Computing

Papers

Total Citations

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About

Seng-Beng Ho is a pioneering researcher at the intersection of artificial intelligence, cognitive architecture, and robotics, with a focus on achieving human-like performance in machines. His work centers on developing AI systems that can reason, learn causally, and understand complex language, bridging the gap between symbolic cognition and embodied robotic action. Ho’s most cited paper, “On Human-Like Performance Artificial Intelligence – A Demonstration Using an Atari Game” (2019, 6 citations), showcases a landmark approach where AI mimics human cognitive strategies rather than relying solely on brute-force learning, achieving intuitive gameplay. His research on “Actional-Perceptual Causality: Concepts and Inductive Learning for AI and Robotics” (2020, 3 citations) addresses a critical gap in the field—the lack of a unified framework for causal learning in robotics—advocating for a consensus that moves beyond traditional supervised, unsupervised, and reinforcement learning paradigms. Ho also contributed to adaptive social robotics (2016, 5 citations) and complex sentence understanding in language-robotics integration (2019, 3 citations), demonstrating his commitment to creating AI that interacts naturally and intelligently. Though his citation counts are modest, his work is foundational for researchers seeking to build truly cognitive, human-like AI systems.