Its purpose is to consider how ideas and analogies drawn from observations of real societies might be applied to computational systems. Recently biology has been a source of inspiration for AI, e.g. spawning the field of evolutionary computation. Now ideas and analogies drawn from the social sciences are starting to be used in computational systems. This is partly because it is being recognised that controlling interacting societies of artificial agents is difficult - traditional methods can not anticipate the emergent outcomes, so that some researchers are looking to real societies. Another strand is the increasing recognition that much of what we had labelled as individual intelligence derives from the society it inhabits. A third strand can be traced to the influence of social simulation techniques. This symposium will focus on these areas, welcoming especially interdisciplinary work and work grounded in observation of real societies and real problems.
If you are in doubt of the relevence of your paper please contact either of the special issue editors.
Refereed Articles:
Kerstin
Dautenhahn and Steven J. Coles
Narrative
Intelligence from the Bottom Up: A Computational Framework for the
Study
of Story-Telling in Autonomous Agents
Alexander
Staller and Paolo Petta
Introducing
Emotions into the Computational Study of Social Norms: A First
Evaluation
Rosaria
Conte and Mario Paolucci
Intelligent
Social Learning
Dietrich
Fliedner
Six
Levels of Complexity
Access it at: http://jasss.soc.surrey.ac.uk/4/1/contents.html
Bruce
Edmonds,
http://bruce.edmonds.name Centre for Policy Modelling, Manchester Metropolitan University, Aytoun Building, Aytoun St., Manchester M1 3GH. UK. E-mail: B.Edmonds@mmu.ac.uk Fax: +44 (0) 161-247 6802 Tel: +44 (0) 161-247 6479 |
Kerstin
Dautenhahn,
http://homepages.feis.herts.ac.uk/~comqkd/ Adaptive Systems Research Group Department of Computer Science University of Hertfordshire College Lane, Hatfield, Hertfordshire AL10 9AB, UK. E-mail: K.Dautenhahn@herts.ac.uk Fax: +44 (0) 707 284 303 Tel: +44 (0) 1707 284 321 |