Course Description | Learning Objectives | Outcomes | Content | Lecture Notes | Course Textbook | Recommended Reading | Useful Links
Course Description
The goal of this course is to expose students to a comprehensive cross-section of the main elements of artificial cognitive systems, a discipline which draws on artificial intelligence, developmental psychology, and cognitive neuroscience. Concepts are introduced in an intuitive, natural order, with an emphasis on the relationships among ideas and building to an overview of the field, equipping students with sufficient knowledge and understanding to study specific topics in greater depth. The course is delivered through a mix of teaching, reading, and in-class discussion. A particular feature of the course is its emphasis on progressive deepening: covering topics several times in increasing detail as the course proceeds. This approach helps the student develop her or his understanding each topic, and the relation of that topic to other topics. Student progress is assessed by a series of multiple choice tests and written individual & group assignments. There are no prerequisites for this course.
To find out what students who took the course thought of it, in 2019 I ran a competition at Carnegie Mellon University Africa in which each student made a pitch to hypothetical prospective students. The competition was judged by the other students and it was won by Timothy Odonga. Here is his pitch.
"There are three types of students:
Student Type B: You are interested in AI, but you don't know where to start. What do deep learning, machine learning, and computer vision achieve in the context of AI? What is a good place to start?
Student Type C: You're not one of the above two types but you are looking for a class outside your normal course profile.
This is what each student will gain from the course:
Student Type B: You will find more focus into what field you find interesting and want focus on in AI from cognitive robotics, deep learning, machine learning, and neuroscience.
Student Type C: You will learn a lot and build on your general knowledge. You will learn a lot of big words, and gain exposure to psychology, epistemology, nonlinear dynamics, and bit of information theory.
All three types of students will learn skills such as verbal and nonverbal communication, presentation skills, how to critique a research paper, and handling interviews.
The class focuses on the principles in building cognitive systems and incorporate the necessary cognitive capabilities, understanding the cognitive paradigms, and cognitive architectures. There is a progressive deepening of concepts presented that makes the learning a lot easier."
Learning Objectives
Students will learn about the nature of cognition and the motivation for studying artificial cognitive systems and they will be introduced to different ways to modelling cognitive systems. Students will learn about the three paradigms of cognitive science: the cognitivist paradigm, the emergent paradigm, and the hybrid paradigm. They will examine cognitive architectures in some detail, learning about the role of a cognitive architecture, its desirable characteristics, its core cognitive abilities, and the different options when designing one. They will study several representative cognitive architectures at various levels of detail. Students will learn about autonomy, both robotic and biological, and they will be introduced to the concepts of constitutive autonomy, behavioural autonomy, homeostasis, allostasis, and self-organization. They will learn about embodiment and the various hypotheses on embodiment, including empirical evidence to support these hypotheses. They will study development and learning and how these two processes differ, including motivation, drives, and value systems. Students will learn about the different forms of memory and how these are involved in different types of anticipation, through self-projection, prospection, and internal simulation. They will learn about the different stances taken on knowledge and representation, and the symbol grounding problem. Finally, they will learn about social cognition, social interaction, and the relevance of joint action, shared goals, shared intention, and joint attention. In this context, they will also learn about reading intentions, theory of mind, instrumental helping, collaboration, interaction dynamics, and different schools of thought on cognitive development.
Outcomes
After completing this course, students should be able to:
Lecture Notes
Module 1: The Nature of Cognition
Module 2: Paradigms of Cognitive Science
Module 3: Cognitive Architecture
Module 4: Autonomy
Module 5: Embodiment
Module 6: Development and Learning
Module 7: Memory and Prospection
Module 8: Knowledge & Representation
Module 9: Social Cognition
D. Vernon, Artificial Cognitive Systems, MIT Press (2014).
Recommended Reading
Kelly, J. E., Computing, cognition and the future of knowing, IBM Corp. 2015.
Lieto, A., Bhatt, M., Oltramari, A., and Vernon, D., "The Role of Cognitive Architectures in General Artificial Intelligence", editorial for a special issue on "Cognitive Architectures for Artificial Minds", Cognitive Systems Research, Vol. 48, pp. 1-3, 2017
Rosenbloom, P., Laird, J., and Lebiere, C. "Précis of 'a standard model of the mind'", Advances in Cognitive Systems, 5:1-4, 2017.
Vernon, D. "Cognitive Architectures", in Cognitive Robotics Handbook, A. Cangelosi and M. Asada (Eds.), MIT Press, 2022.
Vernon, D. "Two Ways (Not) To Design a Cognitive Architecture", Proceedings of EUCognition 2016, Cognitive Robot Architectures, European Society for Cognitive Systems, Vienna, 8-9 December, 2016, R. Chrisley. V. C. Müller, Y. Sandamirskaya. M. Vincze (eds.), CEUR-WS Vol-1855, ISSN 1613-0073, pp. 42-43, 2017.
Vernon, D., "Cognitive System", in Computer Vision: A Reference Guide, K. Ikeuchi (Ed.), Springer, 2014.
Vernon, D., Metta. G., and Sandini, G. A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents, IEEE Transactions on Evolutionary Computation, special issue on Autonomous Mental Development, Vol. 11, No. 2, pp. 151-180, 2007.
Vernon, D. and Vincze, M. "Industrial Priorities for Cognitive Robotics", Proceedings of EUCognition 2016, Cognitive Robot Architectures, European Society for Cognitive Systems, Vienna, 8-9 December, 2016, R. Chrisley. V. C. Müller, Y. Sandamirskaya. M. Vincze (eds.), CEUR-WS Vol-1855, ISSN 1613-0073, pp. 6-9, 2017.
Additional Reading
Kotseruba, I. and Tsotsos, J. "40 years of cognitive architectures: core cognitive abilities and practical applications", Artificial Intelligence Review, 53(1):17-94, 2020.
Laird, J. E., Lebiere, C., & Rosenbloom, P. S. "A standard model of the mind: Toward a common computational framework across artificial intelligence, cognitive science, neuroscience, and robotics", AI Magazine, 38(4), 13-26 , 2017.
Vernon, D. "Cognitive Architectures", in Cognitive Robotics, A. Cangelosi and M. Asada (Eds.), MIT Press, Chapter 10, 2022.
Vernon, D. "Reconciling Constitutive and Behavioural Autonomy: The Challenge of Modelling Development in Enactive Cognition", Intellectica, Vol. 65, pp. 63-79. 2016.
Vernon, D., von Hofsten, C., and Fadiga, L., A Roadmap for Cognitive Development in Humanoid Robots, Cognitive Systems Monographs (COSMOS), Springer, ISBN 978-3-642-16903-8 (2010).
Useful Links
Please refer to my Wiki for links to related resources and support material, including tutorials, research networks, and degree programmes in artificial cognitive systems.
Student Type A: You have taken computer vision, machine learning, or deep learning and you are wondering what to do with all this knowledge.
Student Type A: ACS brings everything together. The material you have learned will make more sense in the broader picture. The goal of AI is to build intelligent systems.
Lecture 1: Course overview. Motivation for studying artificial cognitive systems.
Lecture 2: What is cognition? Modelling cognitive systems. Levels of abstraction and the ultimate-proximate distinction.
Lecture 1: The different paradigms of cognitive science. The cognitivist paradigm of cognitive science.
Lecture 2: The emergent paradigm of cognitive science; connectionist, dynamical, and enactive approaches.
Lecture 3: Hybrid approaches. A comparison on cognitivist and emergent approaches.
Lecture 1: The role of a cognitive architecture. Desirable characteristics. Core cognitive abilities. How to design a cognitive architecture.
Lecture 2: Example cognitive architectures: Soar, ACT/R, Global Workspace, LIDA, BBD/Darwin.
Lecture 3: Example cognitive architectures: Clarion, ISAC.
Lecture 4: Example cognitive architectures: CRAM.
Lecture 5: Example cognitive architectures: The Common Model of Cognition.
Lecture 1: Robotic Autonomy. Strength vs. degree. Biological autonomy. Constitutive & Behavioural autonomy. Homeostasis. Allostasis. Self-organization.
Lecture 2: Autonomy 3: Autonomic Systems. Scales of Autonomy. Goals. Measuring Autonomy. Autonomy and Cognition.
Lecture 1: Cognitivist and emergent perspectives on embodiment. The embodied cognition thesis. Three hypotheses on embodiment.
Lecture 2: Evidence for the embodied stance. Off-line embodied cognition. From situation cognition to distributed cognition.
Lecture 1: Development, motivation, and imitation. Value systems.
Lecture 2: Development vs. learning. Phylogeny vs. Ontogeny.
Lecture 3: Development from the perspective of psychology.
Lecture 1: Types of memory. The role of memory.
Lecture 2: Self-projection, prospection, and internal simulation.
Lecture 3: Internal simulation and action. Forgetting.
Lecture 1: The duality of memory and knowledge. Representation and anti-representation. The symbol grounding problem.
Lecture 2: Joint perceptuo-motor representations.
Lecture 3: Acquiring and sharing knowledge.
Lecture 1: Social interaction. Action, goals, intention, attention.
Lecture 2: Reading intentions and theory of mind. Instrumental helping. Collaboration.
Lecture 3: Development and interaction dynamics. Piaget and Vygotsky. Zone of proximal development.
Vernon, D., von Hofsten, C., and Fadiga, L. Desiderata for Developmental Cognitive Architectures", Biologically Inspired Cognitive Architectures, Vol. 18, pp. 116-127, 2016.