Paul Robertson (researcher)

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Paul Robertson
Born1956
OccupationArtificial Intelligence Researcher
Academic background
Alma materUniversity of Oxford
ThesisA self-adaptive architecture for image understanding (2001)
Academic work
DisciplineComputer Scientist
Sub-disciplineArtificial Intelligence

Paul Robertson (born 1956) is a British-American AI researcher and computer scientist.[1][2] He is the President of Dynamic Object Language Labs in Haverhill, Massachusetts, United States.[1] Robertson is noted for exploring machine learning,[3][4] the use of artificial intelligence in avoiding aviation accidents,[1][5] and its use in autonomous robotics to reduce or eliminate human overhead responsibilities.[6] He is a member of the Biologically Inspired Cognitive Architectures (BICA) Society,[2] and the Institute of Electrical and Electronics Engineers (IEEE).

Early life and education

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Robertson was born in Leamington Spa, United Kingdom in 1956. He was British born, but now a naturalized American citizen.[7] He attended the University of Essex from 1974 to 1977, and earned a BA degree in Computer Science; he later attended the University of Oxford from 1997 to 2001, earning a DPhil in Computer Vision/Artificial Intelligence.[2] His doctoral thesis was entitled, ‘A self-adaptive architecture for image understanding’.[8]

Career

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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).[2]

He has worked on several Defense Advanced Research Projects Agency (DARPA) funded programs.[9]

Research

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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".[10] 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."[10]

Selected publications

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  • 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[11]
  • Georgeon, Olivier L.; Lurie, David; Robertson, Paul (August 2024). "Artificial enactive inference in three-dimensional world". Cognitive Systems Research. Volume 86.[10]
  • Robertson, P. (2020). "Pamela: Integrating Modelling and Machine Learning for Autonomous Robotics", Scientia, January [12]
  • 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”[13]
  • 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.[14]
  • 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.[15]
  • 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.[16]
  • 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.[17]

References

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  1. ^ a b c Iliyah (2025-02-20). "Dr Paul Robertson | Artificial Intelligence in the Cockpit: New Systems Could Help Prevent Aviation Accidents • scientia.global". scientia.global. Retrieved 2025-02-26.
  2. ^ a b c d "Profile of Paul Robertson.pdf". AI Experts in America. Retrieved 2025-02-26.
  3. ^ admin (2021-04-23). "Integrating Modelling and Machine Learning for Autonomous Robotics – Dr Paul Robertson, Dynamic Object Language Labs • scipod.global". scipod.global. Retrieved 2025-02-26.
  4. ^ Iliyah (2020-08-05). "Dr Paul Robertson – Pamela: Integrating Modelling and Machine Learning for Autonomous Robotics • scientia.global". scientia.global. Retrieved 2025-02-26.
  5. ^ "AI Robots can save PILOTS FROM DISASTER". ChiChi Movies. November 3, 2024.
  6. ^ Nishat (2020-12-17). "Machine learning for autonomous AI systems and robotics". Open Access Government. Archived from the original on 2024-08-10. Retrieved 2025-02-26.
  7. ^ "paul robertson interview.pdf". NEW BRITISH AMERICAN MAGAZINE. January 1, 2024. Retrieved 2025-02-27.
  8. ^ Iliyah (2019-10-24). "Dr Paul Robertson – CART: Pointing the Way to Reliable Robotic Assistants • scientia.global". scientia.global. Retrieved 2025-02-27.
  9. ^ "DOLL". www.dollabs.com. Retrieved 2025-02-26.
  10. ^ a b c Georgeon, Olivier L.; Lurie, David; Robertson, Paul (August 2024). "Artificial enactive inference in three-dimensional world". Cognitive Systems Research. 86 101234. doi:10.1016/j.cogsys.2024.101234.
  11. ^ Georgeon, Olivier L.; de Montéra, Béatrice; Robertson, Paul (2025). "Reducing Intuitive-Physics Prediction Error Through Playing". In Buckley, Christopher L.; Cialfi, Daniela; Lanillos, Pablo; Pitliya, Riddhi J.; Sajid, Noor; Shimazaki, Hideaki; Verbelen, Tim; Wisse, Martijn (eds.). Active Inference. Communications in Computer and Information Science. Vol. 2193. Cham: Springer Nature Switzerland. pp. 222–233. doi:10.1007/978-3-031-77138-5_15. ISBN 978-3-031-77138-5.
  12. ^ Robertson, Paul (January 2020). "Pamela: Integrating Modelling and Machine Learning for Autonomous Robotics" (PDF). Scientia. doi:10.33548/SCIENTIA531.
  13. ^ Goldman, Robert P.; Burstein, Mark; Benton, J.; Kuter, Ugur; Mueller, Joseph; Robertson, Paul; Cerys, Dan; Hoffman, Andreas; Bobrow, Rusty (September 21, 2015). "Active Perception for Cyber Intrusion Detection and Defense". 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. pp. 92–101. doi:10.1109/SASOW.2015.20. ISBN 978-1-4673-8439-1.
  14. ^ Terras, Melissa; Robertson, P. (2005). "Image and interpretation: Using artificial intelligence to read ancient Roman texts". HumanIT. 7 (3).
  15. ^ Zhang, Yuening; Robertson, Paul; Shu, Tianmin; Hong, Sungkweon; Williams, Brian C. (May 2024). "Risk-Bounded Online Team Interventions via Theory of Mind". 2024 IEEE International Conference on Robotics and Automation (ICRA). pp. 12964–12970. doi:10.1109/ICRA57147.2024.10609865. ISBN 979-8-3503-8457-4.
  16. ^ Shah, J.; Stedl, J.; Williams, B. C.; Robertson, P. (2007). "A Fast Incremental Algorithm for Maintaining Dispatchability of Partially Controllable Plans" (PDF). Proceedings ICAPS 2007.
  17. ^ Robertson, Paul; Georgeon, Olivier (2020-09-01). "Continuous Learning of Action and State Spaces (CLASS)". Proceedings of the First International Workshop on Self-Supervised Learning. PMLR: 15–31.