# Paul Robertson (researcher)

Paul Robertson (born 1956) is a British-American AI researcher and computer scientist. He is the President of Dynamic Object Language Labs in Haverhill, Massachusetts, United States. Robertson is noted for exploring machine learning, the use of artificial intelligence in avoiding aviation accidents, and its use in autonomous robotics to reduce or eliminate human overhead responsibilities. He is a member of the Biologically Inspired Cognitive Architectures (BICA) Society, and the Institute of Electrical and Electronics Engineers (IEEE).

## Early life and education

Robertson was born in Leamington Spa, United Kingdom in 1956. He was British born, but now a naturalized American citizen. He attended the University of Essex from 1974 to 1977, and earned a BA in Computer Science; he later attended the University of Oxford from 1997 to 2001, earning a DPhil in Computer Vision/Artificial Intelligence. His doctoral thesis was entitled, ‘A self-adaptive architecture for image understanding’.

## Career

Robertson was an assistant professor at the University of Texas at Dallas from 1981 to 1982. He then served as vice president at Computer Thought from 1982 to 1985. He was Chief Technical Officer at Artelligence, Inc. (1985 - 1987); Manager, PC Products at Symbolics (1987 - 1991); Chief Technical Officer at Teela Technology, Inc. (1991 - 1993); Chief Scientist at DOLL, Inc. (1993 - 2004); Research Scientist at Massachusetts Institute of Technology (2004 - 2007); Senior Scientist at Raytheon BBN Technologies (2007 - Aug 2010); Enthusiast at BICA Society (2011 - 2014) and President at Dynamic Object Language Labs (Nov 2010–Present).
Robertson and his team conducted a Human subject research (HSR) study in which twenty-three pilots attempted a simulated challenging mission with a serious mechanical fault. The pilots who used AI assistant performed significantly better than those who did not. He has worked on several Defense Advanced Research Projects Agency (DARPA) funded programs.

## Research

Robertson (along with Olivier Georgeon and David Lurie) reconciled the neuroscience theory of Enactive Inference, which was proposed by Karl J. Friston and his team, with computer science and artificial intelligence theories. The theory explains how the brain infers knowledge about the world through the subject's interactive experiences. "Sensorimotor states induce perturbations in neural activity, and the brain infers hypothetical causes in the world that may explain these perturbations". Robertson and his colleagues applied this theory to Artificial Intelligence, "wherein artificial agents receive input data derived from the environment’s state and infer internal data structures used to guide decisions."

## Selected publications

* Georgeon, O.L., de Montéra, B., Robertson, P. (2025). Reducing Intuitive-Physics Prediction Error Through Playing. In: Buckley, C.L., et al. Active Inference. IWAI 2024. Communications in Computer and Information Science, vol 2193. Springer, Cham. https://doi.org/10.1007/978-3-031-77138-5_15
* Georgeon, Olivier L.; Lurie, David; Robertson, Paul (August 2024). "Artificial enactive inference in three-dimensional world". Cognitive Systems Research. Volume 86.
* Robertson, P. (2020). "Pamela: Integrating Modelling and Machine Learning for Autonomous Robotics", Scientia, January
* Robert P. Goldman, Mark Burstein, J. Benton, Ugur Kuter, Joseph Mueller, Paul Robertson, Dan Cerys, Andreas Hoffman, Rusty Bobrow, (2015). “Active Perception for Cyber Intrusion Detection and Defense”
* Terras, M.M. & Robertson, P. (2005). “Image and Interpretation. Using Artificial Intelligence to Read Ancient Roman Texts”, Human IT, Vol. 7 (3), pp.1-56.
* Zhang, Yuening; Robertson, Paul; Shu, Tianmin; Hong, Sungkweon; Williams, Brian C. (2024). “Risk-bounded online team interventions via theory of mind” in 2024 IEEE international conference on robotics and automation (ICRA). IEEE, pp. 12964–12970.
* Shah, J; Stedl, J.; Williams, B. C.; Robertson, P., (2007). “A Fast Incremental Algorithm for Maintaining Dispatchability of Partially Controllable Plans” in Proceedings ICAPS 2007.
* Robertson, P.; Georgeon, O. L. (2020). “Continuous Learning of Action and State Spaces (CLASS)”, Proceedings of the First International Workshop on Self-Supervised Learning Proceedings of Machine Learning Research, volume = 131, pp. 15–31.