Description
Over the course of the last decade, machine learning has transformed not only AI as a field, but also many sectors of the economy. It is being successfully applied in predictive maintenance, smart grids, and vaccine development. Yet, ML has its limits. ML agents cannot be imaginative. They cannot perform well without a large quantity of high-quality data. And they cannot infer additional information from the context in which they are deployed.
In this seminar students will explore the practical limits of machine learning and venture into other AI approaches that are informed by the scientific study of the mind. Thereby, they will learn about the multifaceted nature of human intelligence. And they will scrutinize how AI developers design agents that exhibit human behavioral patterns in the context of multi-agent simulations, video games, and humanoid robots.
By the end of the course, students will gain a better understanding of the challenges associated with moving from narrow AI to general artificial intelligence. They will also appreciate why the pursuit of general AI is an integral part of the endeavor of advancing artificial intelligence.
Practical Aspects
- Each student will choose a topic. Students will have to study the literature, prepare a presentation, and write an essay.
- Essays must be submitted by the end of the semester.
- The lecturer will give the first three lectures. Afterwards, each class will consist of a student presentation and an extended discussion. Students are also expected to participate actively in the discussion.
- The evaluation depends on three factors: (1) presentation; (2) written essay; (3) participation in class discussion.
Prerequisites
This course is ideally suited for Master’s students. Having attended an introductory course to AI would be highly beneficial.
Instructor
Dr. Nabil Alsabah holds a PhD in psychology and a Master’s degree in computer science. In the past, he spent research stays at Peking University, National University of Singapore and Stanford University. The lecturer looks back at a 15-years experience researching and working at the intersection of artificial intelligence and psychology.
Literature
Bermúdez, J.L. (2020) Cognitive Science. Cambridge University Press, New York.
Dörner, D. (1997) The Logic Of Failure. Perseus, Cambridge, MA.
Gelin, R. & Laumond, J.-P. (2018) Humanoid Robot Applications: Introduction. In Humanoid Robotics: A Reference, Springer Netherlands, Dordrecht, pp. 1-4.
Jezic, G., Howlett, R.J. & Jain, L.C. (2016) Agent and Multi-Agent Systems: Technologies and Applications. Springer,
Kopel, M. & Hajas, T. (2018) Implementing AI for Non-player Characters in 3D Video Games. In Intelligent Information and Database Systems: Lecture Notes in Computer Science, Springer International Publishing, Cham, pp. 610-619.
Marcus, G. & Davis, E. (2020) Rebooting AI. Vintage, New York.
McCorduck, P. (2019) Machines Who Think. A K PETERS, Natick.
Merrick, K. (2008) Modeling motivation for adaptive nonplayer characters in dynamic computer game worlds. Computers in Entertainment, 5, 1-32.
Neo, J.R.J., Won, A.S. & Shepley, M.M. (2021) Designing Immersive Virtual Environments for Human Behavior Research. Frontiers in Virtual Reality, 2,
Ritter, F.E., Tehranchi, F. & Oury, J.D. (2019) ACT-R: A cognitive architecture for modeling cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 10, e1488.
Russell, S. (2020) Human Compatible. Penguin Books, New York.
Russell, S. & Norvig, P. (2019) Artificial Intelligence. Pearson Higher Education, Hoboken.
Schell, J. (2019) The Art of Game Design. CRC Press, Boca Raton.
Sun, R. (2008) Cognition and Multi-Agent Interaction. Cambridge University Press, New York.