Abstract
In the last few years we have witnessed increased popularity of agent systems. This popularity is the result of agents’ ability to work effectively and perform complex tasks in a wide range of applications. In this paper, we highlight the importance of learning mechanisms that are essential for behavioural adaptation of agents in complex environments. We provide a high-level introduction and overview of different types of learning approaches proposed in recent years. We also argue the necessity of dynamic learning processes for handling uncertainty, and propose an uncertainty-oriented architecture of agents together with a specialized knowledge base.
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Zadeh, P.D.H., Reformat, M.Z. (2013). Learning Techniques in Presence of Uncertainty. In: Yager, R., Abbasov, A., Reformat, M., Shahbazova, S. (eds) Soft Computing: State of the Art Theory and Novel Applications. Studies in Fuzziness and Soft Computing, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34922-5_10
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DOI: https://doi.org/10.1007/978-3-642-34922-5_10
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