Publication
Unleashing Artificial Cognition: Integrating Multiple AI Systems
This paper explores the integration of multiple AI systems to unleash artificial cognition, presenting a novel framework for combining different AI models to achieve more complex and human-like cognitive abilities.

Buddhi Gamage
Research Student
Buddhi Gamage is a PhD candidate in the Faculty of Science and Technology at the University of Canberra, Australia. Originally from Sri Lanka and with a background in Computer Science, his research focuses on embodied artificial intelligence, cognitive robotics, and robot learning. His current work investigates world models, representation learning, and failure-aware robotic manipulation, with a particular interest in understanding how predictive representations can improve the robustness, generalisation, and interpretability of autonomous robotic systems. His broader research interests include embodied cognition, machine learning, human-robot interaction, and intelligent autonomous agents.
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Damith Herath
Founder/Director Collaborative Robotics Lab
Professor Damith Herath is the Founder and director of the Collaborative Robotics Lab (CRL). His work explores how robots and humans can collaborate, with an emphasis on the intersection of engineering, psychology, and the arts. His interdisciplinary approach actively seeks insights across diverse fields, promoting innovation and collaboration that transcends traditional disciplinary boundaries.
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Carlos Kuhn
Staff
Carlos Kuhn is a distinguished physicist and researcher with a profound expertise in Generative AI and Cognitive AI. He holds a Bachelor’s in Physics, a Master’s in Physics, and a PhD in Theoretical Quantum Physics from the Universidade Federal do Rio Grande do Sul (UFRGS), Brazil. His doctoral research on the Exactly Solvable Model for Hetero-Triatomic Molecular Bose-Einstein Condensate highlighted his exceptional skills in complex mathematical modelling.
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Adnan, Muntasir; Gamage, Buddhi; Xu, Zhiwei; Herath, Damith; and Kuhn, Carlos, “Unleashing Artificial Cognition: Integrating Multiple AI Systems” (2024). ACIS 2024 Proceedings. 31. https://aisel.aisnet.org/acis2024/31