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.
