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> Introduction To AI Paradigms (i.e, Artificial Intelligence Paradigms)
post Mar 13, 2004, 12:15 PM
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Artificial intelligence comprises methods, tools, and systems for solving problems that normally require the intelligence of humans. The term intelligence is always defined as the ability to learn effectively, to react adaptively, to make proper decisions, to communicate in language or images in a sophisticated way, and to understand. The main objectives of AI are to develop methods and systems for solving problems, usually solved by the intellectual activity of humans, for example, image recognition, language and speech processing, planning, and prediction, thus enhancing computer information systems; and to develop models which simulate living organisms and the human brain in particular, thus improving our understanding of how the human brain works.

The main AI directions of development are to develop methods and systems for solving AI problems without following the way humans do so, but providing similar results, for example, expert systems; and to develop methods and systems for solving AI problems by modeling the human way of thinking or the way the brain works physically, for example, artificial neural networks.

In general, AI is about modeling human intelligence. There are two main paradigms adopted in AI in order to achieve this: (1) the symbolic, and (2) the subsymbolic. The first is based on symbol manipulation and the second on neurocomputing.

The symbolic paradigm is based on the theory of physical symbolic systems (Newel and Simon 1972). A symbolic system consists of two sets:

(1) a set of elements (or symbols) which can be used to construct more complicated elements or structures; and (2) a set of processes and rules, which, when applied to symbols and structures, produce new structures. The symbols have semantic meanings. They represent concepts or objects. Propositional logic, predicate logic, and the production systems explained in chapter 2 facilitate dealing with symbolic systems. Some of their corresponding AI implementations are the simple rule-based systems, the logic programming and production languages, also discussed in chapter 2. Symbolic Al systems have been applied to natural language processing, expert systems, machine learning, modeling cognitive processes, and others. Unfortunately, they do not perform well in all cases when inexact, missing, or uncertain information is used, when only raw data are available and knowledge acquisition should be performed, or when parallel solutions need to be elaborated. These tasks do not prove to be difficult for humans. The subsymbolic paradigm (Smolenski 1990) claims that intelligent behavior is performed at a subsymbolic level which is higher than the neuronal level in the brain but different from the symbolic one. Knowledge processing is about changing states of networks constructed of small elements called neurons, replicating the analogy with real neurons. A neuron, or a collection of neurons, can represent a microfeature of a concept or an object. It has been shown that it is possible to design an intelligent system that achieves the proper global behavior even though all the components of the system are simple and operate on purely local information. The subsymbolic paradigm makes possible not only the use of all the significant results in the area of artificial neural networks achieved over the last 20 years in areas like pattern recognition and image and speech processing but also makes possible the use of connectionist models for knowledge processing. The latter is one of the objectives of this book. As the subsymbolic models move closer, though slowly, to the human brain, it is believed that this is the right way to understand and model human intelligence for knowledge engineering.

There are several ways in which the symbolic and subsymbolic models of knowledge processing may interact:
1. They can be developed and used separately and alternatively.
2. Hybrid systems that incorporate both symbolic and subsymbolic systems can be developed.
3. Subsymbolic systems can be used to model pure symbolic systems.

So, there is a third paradigm—a mixture of symbolic and subsymbolic systems. We shall see that fuzzy systems can represent symbolic knowledge, but they also use numerical representation similar to the one used in subsymbolic systems.

At the moment it seems that aggregation of symbolic and subsymbolic methods provides in most cases the best possible solutions to complex AI problems.

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