Chapter 4Decision Making and Learning 1

 

 

 

4.1. Introduction

In spite of scientific advances in the fields of neuropsychology and, more generally, in cognitive sciences during the last 50 years, we are still very far from understanding the physiological mechanisms that explain learning and decision making in the animal kingdom. Although no computer can still claim cognitive qualities similar to human beings, the problem of learning and decision making for an intelligent telecommunications system starts by establishing precise rules that give birth to these functions. The learning for a cognitive system corresponds to a phase of interpretation of stimuli provided by the environment in a language that is understandable by the system and is minimalist in terms of information storage. In this chapter, we will describe a mathematical approach based on Bayesian probabilities and the principle of maximum entropy, which allows learning via cognitive radios (CRs). The advantages and drawbacks of these techniques will be elaborated through examples of channel modeling and channel estimation.

After learning, the intelligent device is required to make decisions involving actions that will enable it to adapt itself to its surrounding environment (and sometimes modify this environment). This decision comprises the choice of the action to be performed. This choice will be guided by information acquired by the system and, in particular, in intelligent systems for telecommunications, by information ...

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