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.
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.
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.