Artificial Cognitive Systems

David Vernon
Institute for Artificial Intelligence
University of Bremen
Germany


Course Description  |  Learning Objectives  |  Outcomes  |  Content  |  Lecture Notes  |  Course Textbook  |  Recommended Reading  |  Useful Links

Course Description

The primary goal of this course is to expose students to a comprehensive cross-section of the main elements of artificial cognitive systems. Inspired by artificial intelligence, developmental psychology, and cognitive neuroscience, the aim is to build systems that can act on their own to achieve goals: perceiving their environment, anticipating the need to act and the likely outcome of actions, learning from experience, adapting to changing circumstances, and interacting with humans.

The course surveys the cognitivist, emergent, and hybrid paradigms of cognitive science and discusses cognitive architectures derived from them. It then turns to the key issues of autonomy, embodiment, learning & development, memory & prospection, knowledge & representation, and social cognition.

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 balanced mix of teaching, reading, and in-class discussion.

Student Elevator Pitch for the Course

Last year, to find out what students who took the course thought of it, we ran a competition 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 A: You have taken computer vision, machine learning, or deep learning and you are wondering what to do with all this knowledge.

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 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.

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.

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Learning Objectives

After completing this course, students should be able to:

  1. Identify the key attributes of a cognitive system.
  2. Explain the main characteristics of cognitivist, emergent, and hybrid cognitive science.
  3. Compare cognitive architectures using several criteria and design an outline cognitive architecture for a given application scenario.
  4. Explain how a specific hybrid cognitive architecture works and show how it can be used to allow a robot to reason about its environment and achieve goals set by a user.
  5. Explain the implications of computational functionalism and its relationship to the embodied cognition thesis.
  6. Distinguish between learning and development and explain how these processes are facilitated by different forms of memory and knowledge.

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Course Content

The Nature of Cognition

  • Overview.
  • Motivation for studying artificial cognitive systems.
  • Aspects of modelling cognitive systems.
  • So, what is cognition anyway?
  • Levels of abstraction in modelling cognitive systems.

Paradigms of Cognitive Science

  • The cognitivist paradigm of cognitive science.
  • The emergent paradigm of cognitive science.
  • Hybrid Systems.
  • A comparison on cognitivist and emergent paradigms.
  • Which paradigm should we choose?
Cognitive Architectures
  • What is a cognitive architecture?
  • Desirable characteristics.
  • Designing a cognitive architecture.
  • Example cognitive architectures.
  • Cognitive architectures: what next?
Autonomy
  • Types of Autonomy.
  • Robotic Autonomy.
  • Biological Autonomy.
  • Autonomic Systems.
  • Different Scales of Autonomy.
  • Goals.
  • Measuring Autonomy.
  • Autonomy and Cognition.
  • A Menagerie of Autonomies.
Embodiment
  • Cognitivist perspective on embodiment.
  • Emergent perspective on embodiment.
  • The impact of embodiment on cognition.
  • Three hypotheses on embodiment.
  • Evidence for the embodied stance: the mutual dependence of perception and action.
  • Types of embodiment.
  • Off-line embodied cognition.
  • Interaction within.
  • From situation cognition to distributed cognition.
Development and Learning
  • Development.
  • Phylogeny vs. Ontogeny.
  • Development from the perspective of psychology.
Memory and Prospection
  • Types of memory.
  • The role of memory.
  • Self-projection, prospection, and internal simulation.
  • Internal simulation and action.
  • Forgetting.
Knowledge and Representation
  • The duality of memory and knowledge.
  • Representation and anti-representation.
  • The symbol grounding problem.
  • Joint perceptuo-motor representations.
  • Acquiring and sharing knowledge.
Social Cognition
  • Social interaction.
  • Reading intentions and theory of mind.
  • Instrumental helping.
  • Collaboration.
  • Development and interaction dynamics.

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Lecture Notes

Lecture 1: The Nature of Cognition I 
Lecture 2: The Nature of Cognition II 
Lecture 3: Paradigms of Cognitive Science I 
Lecture 4: Paradigms of Cognitive Science II 
Lecture 5: Paradigms of Cognitive Science III 
Lecture 6: Cognitive Architectures I 
Lecture 7: Cognitive Architectures II 
Lecture 8: Cognitive Architectures III 
Lecture 9: Cognitive Architectures IV 
Lecture 10: Cognitive Architectures V 
Lecture 11: Cognitive Architectures VI 
Lecture 12: Autonomy I 
Lecture 13: Autonomy II 
Lecture 14: Autonomy III 
Lecture 15: Embodiment I 
Lecture 16: Embodiment II 
Lecture 17: Embodiment III 
Lecture 18: Development and Learning I 
Lecture 19: Development and Learning II 
Lecture 20: Development and Learning II 
Lecture 21: Memory and Prospection I 
Lecture 22: Memory and Prospection II 
Lecture 23: Memory and Prospection III 
Lecture 24: Knowledge & Representation I 
Lecture 25: Knowledge & Representation II 
Lecture 26: Knowledge & Representation III 
Lecture 27: Social Cognition I 
Lecture 28: Social Cognition III 
Lecture 29: Social Cognition III 
Lecture 30: Social Cognition IV 

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Course Textbook

D. Vernon, Artificial Cognitive Systems, MIT Press (2014).

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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, in press.

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. Vernon, D., von Hofsten, C., and Fadiga, L. Desiderata for Developmental Cognitive Architectures", Biologically Inspired Cognitive Architectures, Vol. 18, pp. 116-127, 2016.

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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. "The Architect's Dilemmas", in Cognitive Architectures, Ferreira, M., Sequeira, J., and Ventura, R. (eds.), Intelligent Systems, Control and Automation: Science and Engineering, Springer, 2018.

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).

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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.

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David Vernon's Personal Website