Cognitive Computation

, Volume 2, Issue 3, pp 199–216 | Cite as

Cognitive Architectures for Affect and Motivation

Article

Abstract

General frameworks of mind map across tasks and domains. By what means can a general architecture know when it has adapted to a specific task, a particular environment or a specific state of a previously known environment? Our current work on this theme presents an affect- and affordance-based core for mind. This draws on evidence from neuroscience, philosophy and psychology. However, we differentiate between the mechanisms and processes thought to be allied to cognition and intelligent behaviour in biological architectures and the foundational requirements necessary for similarly intelligent behaviour or cognitive-like processes to exist in synthetic architectures. Work on emotion is a morass of definitions and competing theories. We suggest that we should not further this confused framework with unnecessary (and often unneeded) models of emotion for artificial systems. Rather, we should look to foundational requirements for intelligent systems and ask do we require emotions in machines or an alternative equivalent, for example affect, of use in control and self-regulation? This paper addresses this issue with experimentation in a number of simulated and robotic test-beds.

Keywords

Cognitive architectures Affect Emotion Motivation Robots 

References

  1. 1.
    Minsky ML. The society of mind. London: William Heinemann Ltd.; 1987.Google Scholar
  2. 2.
    Sloman A. Beyond shallow models of emotion. Cogn Process. 2001;2(1):177–98.Google Scholar
  3. 3.
    Franklin S. A consciousness based architecture for a functioning mind. In: Proceedings of the AISB’00 symposium on how to design a functioning mind, April 2000.Google Scholar
  4. 4.
    Robinson P, El Kaliouby R. Computation of emotions in man and machines. Phil Trans R Soc B. 2009;364:3441–7.CrossRefPubMedGoogle Scholar
  5. 5.
    Vallverdú J, Casacuberta D. Handbook of research on synthetic emotions and sociable robotics. USA: Idea Group Inc (IGI); 2009.Google Scholar
  6. 6.
    Norman DA. Twelve issues for cognitive science. Cogn Sci. 1980;4:1–33.CrossRefGoogle Scholar
  7. 7.
    Simon HA. Motivational and emotional controls of cognition, reprinted in models of thought. New Haven: Yale University Press; 1979. p. 29–38. (originally Psychological Review, 74(1):29-39, 1967).Google Scholar
  8. 8.
    Ortony A, Clore GL, Collins A. The cognitive structure of emotions. Cambridge: Cambridge University Press; 1988.Google Scholar
  9. 9.
    Scherer K. Toward a concept of ‘Modal Emotions’. In: Ekman P, Davidson R, editors. Nature of emotion. New York: Oxford University Press; 1994.Google Scholar
  10. 10.
    Barrett LF, Lindquist KA, Gendron M. Language as context for the perception of emotion. Trends Cogn Sci. 2007;11(8):327–32.CrossRefPubMedGoogle Scholar
  11. 11.
    Neviarouskaya A. AffectIM: an avatar-based instant messaging system employing rule-based affect sensing from text. Dissertation, University of Tokyo; 2008.Google Scholar
  12. 12.
    Chanel G, Kierkels JJM, Soleymai M, Pu T. Short-term emotion assessment in a recall paradigm. Int J Hum Comput Stud. 2009;67(8):607–27.CrossRefGoogle Scholar
  13. 13.
    Arrabales R, Ledezma A, Sanchis A. Towards conscious-like behaviour in computer game characters. IEEE symposium on computational intelligence and games; 2009.Google Scholar
  14. 14.
    James W. What is an emotion? Mind. 1884;9:188–205.CrossRefGoogle Scholar
  15. 15.
    Plutchik R. The psychology and biology of emotion. New York: Harper Collins; 1994.Google Scholar
  16. 16.
    Darwin C. The expression of emotions in man and animals. London: Murray; 1872.CrossRefGoogle Scholar
  17. 17.
    Ekman P. The nature of emotion: fundamental questions. New York: Oxford University Press; 1994.Google Scholar
  18. 18.
    Scherer K. Appraisal considered as a process of multilevel sequential checking. In: Scherer K, Schorr A, Johnstone T, editors. Appraisal processes in emotion. New York: Oxford University Press; 2001.Google Scholar
  19. 19.
    Oatley K. Best laid schemes. Cambridge: Cambridge University Press; 1992.Google Scholar
  20. 20.
    Frijda N. The emotions. Cambridge: Cambridge University Press; 1986.Google Scholar
  21. 21.
    Frankel CB, Ray RD. Emotion, intention and the control architecture of adaptively competent information processing. In: Symposium on how to design a functioning mind, AISB’00 Convention, April 2000.Google Scholar
  22. 22.
    Davis DN. Multiple level representations of emotion in computational agents, emotion, cognition and affective computing. AISB2001:agents and cognition, University of York, 2001. Isbn: 1902956197.Google Scholar
  23. 23.
    De Houwer J, Hermans D. Cognition & emotion: reviews of current research and theories. London: Psychology Press; 2010.Google Scholar
  24. 24.
    Duffy E. Activation and behaviour. London: Wiley; 1962.Google Scholar
  25. 25.
    Wollheim R. On the emotions. New Haven: Yale University Press; 1999.Google Scholar
  26. 26.
    Picard R. Affective computing. Cambridge: MIT Press; 1997.Google Scholar
  27. 27.
    Rolls ET. The brain and emotion. Oxford: Oxford University Press; 1999.Google Scholar
  28. 28.
    Freud S (originally 1894). The defence neuro-psychoses. In Jones E, editor. Sigmund Freud: collected papers. New York: BasicBooks; 1959.Google Scholar
  29. 29.
    Clore GL. Why emotions are never unconscious. In: Ekman P, Davidson R, editors. Nature of emotion. New York: Oxford University Press; 1994.Google Scholar
  30. 30.
    Sloman A, Croucher M. Why robots will have emotions. In: Proceedings of IJCAI87, 197–202; 1987.Google Scholar
  31. 31.
    Sloman A. The mind as a control system. In: Hookway C, Peterson D, editors. Philosophy and cognitive science. Cambridge: Cambridge University Press; 1993.Google Scholar
  32. 32.
    Phelps E. Emotion and cognition: insights from studies of the human amygdala. Annu Rev Psychol. 2006;24(57):27–53.CrossRefGoogle Scholar
  33. 33.
    Ziemke T, Lowe R. On the role of emotion in embodied cognitive architectures: from organisms to robots. Cogn Comput. 2009;1:104–17.CrossRefGoogle Scholar
  34. 34.
    Gros C. Cognition and emotion: perspectives of a closing gap. Cogn Comput. 2010;2(2):78–85.CrossRefGoogle Scholar
  35. 35.
    Cohn JF. Foundations of human computing: facial expression and emotion. In: Proceedings of the 8th international conference on multimodal interfaces, Banff, pp 233–238; 2006.Google Scholar
  36. 36.
    Davis DN. Control states and complete agent architectures. Comput Intell. 2001;17(4):621–50.CrossRefGoogle Scholar
  37. 37.
    Gibson JJ. The ecological approach to visual perception. Boston: Houghton Mifflin; 1979.Google Scholar
  38. 38.
    Davis DN, Lewis SC. Computational models of emotion for autonomy and reasoning. Informatica (Special Edition on Perception and Emotion Based Reasoning). 2003; 27(2):159–165.Google Scholar
  39. 39.
    Beaudoin L. Goal processing in autonomous agents. Ph.D. Thesis, Cognition and Affect Group, Computer Science, University of Birmingham; 1994.Google Scholar
  40. 40.
    Barsalou LW. Perceptual symbol systems. Behav Brain Sci. 1999;22:577–609.PubMedGoogle Scholar
  41. 41.
    Sowa JF, Majumdar, AK. Analogical reasoning. In: Conceptual structures for knowledge creation and communication. Berlin: Springer; 2003.Google Scholar
  42. 42.
    Bourgne G. Affect-based multi-agent architecture (for a 5-aside football simulation). Thesis, Department of Computer Science, University of Hull; 2003.Google Scholar
  43. 43.
    Norman DA, Shallice T. Attention to action: willed and automatic control of behaviour. In: Davidson RJ, Schwartz GE, Shapiro D, editors. Consciousness and self-regulation, vol. 4. New York: Plenum Press; 1986. p. 1–18.Google Scholar
  44. 44.
    Gat E. Three-layer architectures. In: Kortenkamp D, Bonasso RP, Murphy R, editors. Artificial intelligence and mobile robots: case studies of successful robot systems. Cambridge: MIT Press; 1998. p. 195–210.Google Scholar
  45. 45.
    Singh P, Minsky M. An architecture for combining ways to think. In: Proceedings of the international conference on knowledge intensive multi-agent systems; 2003.Google Scholar
  46. 46.
    Vijayakumar MV. A society of mind approach to cognition and metacognition in a cognitive architecture. Ph.D. Thesis, Computer Science, University of Hull, August 2008.Google Scholar
  47. 47.
    Davis DN, Vijayakumar MVA. “Society of Mind” cognitive architecture based on the principles of artificial economics. Int J Artif Life Res. 2010;1(1):51–71.Google Scholar
  48. 48.
    Davis DN. Reactive and motivational agents. In: Muller JP, Wooldridge MJ, Jennings NR, editors. Intelligent agents III. New York: Springer; 1996.Google Scholar
  49. 49.
    Nunes HA. Investigation of motivation in agents using five-aside football. M.Sc. Thesis, Department of Computer Science, University of Hull; 2001.Google Scholar
  50. 50.
    Lewis SC. Computational models of affect. Ph.D. Thesis, Department of Computer Science, University of Hull; 2004.Google Scholar
  51. 51.
    Gwatkin J. Robo-CAMAL: anchoring in a cognitive robot. Ph.D. Thesis, Department of Computer Science, University of Hull, July 2009.Google Scholar
  52. 52.
    Bartsch K, Wellman H. Young children’s attribution of action to beliefs and desires. Child Dev. 1989;60:946–64.CrossRefPubMedGoogle Scholar
  53. 53.
    Wahl S, Spada H. Children’s reasoning about intentions, beliefs and behaviour. Cogn Sci Q. 2000;1:5–34.Google Scholar
  54. 54.
    Davis DN. Linking perception and action through motivation and affect. J Exp Theor Artif Intell. 2008;20(1):37–60.CrossRefGoogle Scholar
  55. 55.
    Toda M. The design of a fungus-eater: a model of human behaviour in an unsophisticated environment. Behav Sci. 1962;7:164–83.CrossRefGoogle Scholar
  56. 56.
    Wehrle T. New fungus eater experiments. In: Gaussier P, Nicoud J, editors. From perception to action. Los Alamitos: IEEE Computer Society Press; 1994. p. 400–3.CrossRefGoogle Scholar
  57. 57.
    Coradeschi S, Saffiotti A. An introduction to the anchoring problem. Robot Auton Syst. 2003;43:85–96.CrossRefGoogle Scholar
  58. 58.
    Harnad S. The symbol grounding problem. Physica D. 1990;42:335346.CrossRefGoogle Scholar
  59. 59.
    van der Velde F, de Kamps M. Learning of control in a neural architecture of grounded language processing. Cogn Syst Res. 2010;11(1):93–107.CrossRefGoogle Scholar
  60. 60.
    Fodor JA, Pylyshyn ZW. Connectionism and cognitive architecture: a critical analysis. Cognition. 1988;28:3–71.CrossRefPubMedGoogle Scholar
  61. 61.
    Franklin S. Perceptual memory and learning: recognizing, categorizing, and relating. Symposium on developmental robotics: American Association for Artificial Intelligence (AAAI), Stanford University, Palo Alto; 2005.Google Scholar
  62. 62.
    Hawes N, Wyatt J, Sloman A. Exploring design space for an integrated intelligent system. Knowl Based Syst 22(7), pp. 509–515, Elsevier. (Published as a best paper from Artificial Intelligence 2008 (AI-2008), The 28th SGAI International Conference on Artificial Intelligence); September 2009.Google Scholar
  63. 63.
    Hanks S, Pollack M, Cohen PR. Benchmarks, test-beds, controlled experimentation, and the design of agent architectures. AI Mag. 1993;14(4):17–42.Google Scholar
  64. 64.
    Konidaris G, Barto A. An adaptive robot motivational system, from animals to animats 9, Lecture notes in computer science. Springer, Berlin, vol 4095, pp 1611–3349; 2006.Google Scholar
  65. 65.
    Stoytchev A, Arkin R. Incorporating motivation in a hybrid robot architecture. J Adv Comput Intell Intell Inform. 2004;8(3):269–74.Google Scholar
  66. 66.
    Charland LC. Emotion as a natural kind: towards a computational foundation for emotion theory. Philos Psychol. 1995;8(1):59–85.CrossRefGoogle Scholar
  67. 67.
    Loula A, Gudwin R, El-Hani CN, Queiroz J. Emergence of self-organized symbol-based communication in artificial creatures. Cogn Syst Res. 2010;11(2):131–47.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of HullHullUK

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