Representing Collective Unconsciousness Using Neural Networks
Instead of representing individual cognition only, population cognition is represented using artificial neural networks whilst maintaining individuality. This population network trains continuously, simulating adaptation. An implementation of two coexisting populations is compared to the Lotka-Volterra model of predator-prey interaction. Applications include multi-agent systems such as artificial life or computer games.
[1] J. R. B.Muller, "Neural Networks: An Introduction 2nd Edition,"
pp. 2-12, 1990.
[2] H. B. D. Martin T. Hagan, Mark H. Beale, Neural Network Design,
Paperback ed: Martin Hagan, 2002.
[3] R. Rogers, "Object-Oriented Neural Networks in C++," pp. 133-
171, 1997.
[4] C. G. Jung, "The Archetypes and the Collective Unconscious,"
Collected Works of C.G. Jung, vol. 9, pp. 87-110, 1980.
[5] B. MacLennan, "Evolutionary Neurotheology and the Varieties of
Religious Experience," in NeuroTheology: Brain, Science,
Spirituality, Religious Experience: University Press, California,
2002.
[6] R. Sun, "Individual Action and Collective Function: from
Sociology to Muli-Agent Learning," Cognitive Systems Research,
vol. 2, 2001.
[7] M. P. a. W. Mende, "The Predator-Prey Model, Do We Live in a
Volterra World?," pp. 3-15, 1986.
[8] Y. Takeuchi, Global Dynamical Properties of Lotka-Volterra
Systems: World Scientific Publishing Company, 1996.
[9] D. H. Z. Guillermo Abramson, "Statistics of extinction and
survival in Lotka-Volterra systems," 1998.
[10] P. J. Morin, Community Ecology: Blackwell Publishers, 1999.
[11] J. H. E. R. M. Sibly, T. H. Clutton-Brock (Editor), Wildlife
Population Growth Rates: Cambridge University Press, 2003.
[12] M. Wooldridge and N. Jennings, "Intelligent Agents: Theory and
Practice," Knowledge Engineering Review, vol. 10, pp. 115-152,
1995.
[13] R. Brooks, "A robust layered control system for a mobile robot,"
IEEE Journal of Robotics and Automation, vol. RA-2, pp. 14-23,
1986.
[14] M. Bratman, Intention, Plans, and Practical Reason: Harvard
University Press, 1987.
[15] M. W. Ian Dickinson, "An Initial Response to the OAS-03
Challenge Problem," presented at Autonomous Agents and Multi-
Agent Systems (AAMAS-03), Melbourne, Australia, 2003.
[16] M. Namee and B. Cunningham, "A Proposal for an Agent
Architecture for Proactive Persistent Non Player Characters," TCDCS-
2001-20, Trinity College Dublin, 2001.
[1] J. R. B.Muller, "Neural Networks: An Introduction 2nd Edition,"
pp. 2-12, 1990.
[2] H. B. D. Martin T. Hagan, Mark H. Beale, Neural Network Design,
Paperback ed: Martin Hagan, 2002.
[3] R. Rogers, "Object-Oriented Neural Networks in C++," pp. 133-
171, 1997.
[4] C. G. Jung, "The Archetypes and the Collective Unconscious,"
Collected Works of C.G. Jung, vol. 9, pp. 87-110, 1980.
[5] B. MacLennan, "Evolutionary Neurotheology and the Varieties of
Religious Experience," in NeuroTheology: Brain, Science,
Spirituality, Religious Experience: University Press, California,
2002.
[6] R. Sun, "Individual Action and Collective Function: from
Sociology to Muli-Agent Learning," Cognitive Systems Research,
vol. 2, 2001.
[7] M. P. a. W. Mende, "The Predator-Prey Model, Do We Live in a
Volterra World?," pp. 3-15, 1986.
[8] Y. Takeuchi, Global Dynamical Properties of Lotka-Volterra
Systems: World Scientific Publishing Company, 1996.
[9] D. H. Z. Guillermo Abramson, "Statistics of extinction and
survival in Lotka-Volterra systems," 1998.
[10] P. J. Morin, Community Ecology: Blackwell Publishers, 1999.
[11] J. H. E. R. M. Sibly, T. H. Clutton-Brock (Editor), Wildlife
Population Growth Rates: Cambridge University Press, 2003.
[12] M. Wooldridge and N. Jennings, "Intelligent Agents: Theory and
Practice," Knowledge Engineering Review, vol. 10, pp. 115-152,
1995.
[13] R. Brooks, "A robust layered control system for a mobile robot,"
IEEE Journal of Robotics and Automation, vol. RA-2, pp. 14-23,
1986.
[14] M. Bratman, Intention, Plans, and Practical Reason: Harvard
University Press, 1987.
[15] M. W. Ian Dickinson, "An Initial Response to the OAS-03
Challenge Problem," presented at Autonomous Agents and Multi-
Agent Systems (AAMAS-03), Melbourne, Australia, 2003.
[16] M. Namee and B. Cunningham, "A Proposal for an Agent
Architecture for Proactive Persistent Non Player Characters," TCDCS-
2001-20, Trinity College Dublin, 2001.
@article{"International Journal of Information, Control and Computer Sciences:51419", author = "Pierre Abou-Haila and Richard Hall and Mark Dawes", title = "Representing Collective Unconsciousness Using Neural Networks", abstract = "Instead of representing individual cognition only, population cognition is represented using artificial neural networks whilst maintaining individuality. This population network trains continuously, simulating adaptation. An implementation of two coexisting populations is compared to the Lotka-Volterra model of predator-prey interaction. Applications include multi-agent systems such as artificial life or computer games.
", keywords = "Collective unconsciousness, neural networks, adaptation, predator-prey simulation.", volume = "1", number = "5", pages = "1198-5", }
