Integrating Learning and Inference in Multi-agent Systems Using Cognitive Context

  • Bruce Edmonds
  • Emma Norling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4442)


Both learning and reasoning are important aspects of intelligence. However they are rarely integrated within a single agent. Here it is suggested that imprecise learning and crisp reasoning may be coherently combined via the cognitive context. The identification of the current context is done using an imprecise learning mechanism, whilst the contents of a context are crisp models that may be usefully reasoned about. This also helps deal with situations of logical under- and over-determination because the scope of the context can be adjusted to include more or less knowledge into the reasoning process. An example model is exhibited where an agent learns and acts in an artificial stock market.


Inference System Multiagent System Knowledge Item Local Learning Cognitive Context 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bruce Edmonds
    • 1
  • Emma Norling
    • 1
  1. 1.Centre for Policy Modelling, Manchester Metropolitan University 

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