Publisher's Synopsis
Tremendous advances in all disciplines including engineering, science, health care, business, avionics, management, and so on, can also be attributed to the development of artificial intelligence paradigms. In fact, researchers are always interested in desi- ing machines which can mimic the human behaviour in a limited way. Therefore, the study of neural information processing paradigms have generated great interest among researchers, in that machine learning, borrowing features from human intelligence and applying them as algorithms in a computer friendly way, involves not only Mathem- ics and Computer Science but also Biology, Psychology, Cognition and Philosophy (among many other disciplines). Generally speaking, computers are fundamentally well-suited for performing au- matic computations, based on fixed, programmed rules, i.e. in facing efficiently and reliably monotonous tasks, often extremely time-consuming from a human point of view. Nevertheless, unlike humans, computers have troubles in understanding specific situations, and adapting to new working environments. Artificial intelligence and, in particular, machine learning techniques aim at improving computers behaviour in tackling such complex tasks. On the other hand, humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial intelligence can help us understanding this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.
