Artificial Cognitive Systems

Prof. David Vernon
Carnegie Mellon University Africa in Rwanda
vernoncmu.edu


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, learning from experience, and adapting to changing circumstances.

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Learning Objectives

Students will learn to identify the key characteristics of cognition and the different levels of abstraction that are required to model cognitive systems. They will also learn to recognize the chief differences between the two main paradigms of cognitive science and to understand contemporary attempts to reconcile them. They will learn how models of cognition are captured by various cognitive architectures and they will study several architectures at different levels of detail. These will provide the basis for further study of the key issues of autonomy, embodiment, learning & development, memory & prospection, knowledge & representation, and social cognition.

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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  
Lecture 2: Paradigms of Cognitive Science  
Lecture 3: Cognitive Architectures  
Lecture 4: Autonomy  
Lecture 5: Embodiment  
Lecture 6: Development and Learning 
Lecture 7: Memory and Prospection  
Lecture 8: Knowledge & Representation  
Lecture 9: Social Cognition  

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Course Textbook

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

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Recommended Reading

Duch, W, Oentaryo, R. J., and Pasquier, M. Cognitive Architectures: Where do we go from here?, Proc. Conf. Artificial General Intelligence, 122-136 (2008).

Langley, P.: Cognitive architectures and general intelligent systems. AI Magazine 27(2), 33-44 (2006).

Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: Research issues and challenges. Cognitive Systems Research 10(2), 141-160 (2009).

Merrick, K. E. A Comparative Study of Value Systems for Self-motivated Exploration and Learning by Robots, IEEE Transactions on Autonomous Mental Development, Vol. 2, No. 2, 119-131 (2010).

Sun, R.: The importance of cognitive architectures: an analysis based on clarion. Journal of Experimental & Theoretical Artificial Intelligence 19(2), 159-193 (2007).

Sun, R.: Desiderata for cognitive architectures. Philosophical Psychology 17(3), 341-373 (2004).

Vernon, D. Cognitive System, Computer Vision: A Reference Guide, Springer (2014).

Vernon, D. and Vincze, M., "Industrial Priorities for Cognitive Robotics", Proceedings of the European Society for Cognitive Systems Meeting, EUCognition 2016, Vienna, 8-9 December 2016.

Vernon, D. Cognitive Vision: The Case for Embodied Perception, Image and Vision Computing, Special Issue on Cognitive Vision, Vol. 26, No. 1, pp. 127-141 (2008).

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., 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).

Vernon, D. Reconciling Autonomy with Utility: A Roadmap and Architecture for Cognitive Development", Proc. Int. Conf. on Biologically-Inspired Cognitive Architectures 2011, A. V. Samsonovich and K. R. Johannsdottir (Eds.), IOS Press, 412-418 (2011).

Vernon, D. and Furlong, D. Philosophical Foundations of Enactive AI, in 50 Years of AI, M. Lungarella et al. (Eds.), Festschrift, LNAI 4850, Springer-Verlag, Heidelberg, pp. 53-62 (2007).

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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