There are two fundamentally different major approaches in the field of Artificial Intelligence :
- Termed Traditional Symbolic Artificial Intelligence, which has been historically dominant. It is characterized by a high level of abstraction and a macroscopic view. Classical psychology operates at a similar level. Knowledge engineering systems and logic programming fall in this category. Symbolic Artificial Intelligence covers areas such as knowledge based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing.
- Microscopic Biological Models (based on low level), similar to the emphasis of physiology or genetics. Neural networks and genetic algorithms are the prime examples of this latter approach. These biological models do not necessarily resemble their original biological counterparts. However, they are evolving areas from which many people expect significant practical applications in the future.
In addition to the two major categories mentioned above, there are relatively new Artificial Intelligence techniques which include fuzzy systems, rough set theory, and chaotic systems or chaos for short. Fuzzy systems and rough set theory can be employed for symbolic as well as numeric applications, often dealing with incomplete or imprecise data. These nontraditional Artificial Intelligence areas:
- Neural Networks, Computational models of the brain. Artificial neurons are interconnected by edges, forming a neural network. Similar to the brain, the network receives input, internal processes take place such as activations of the neurons, and the network yields output.
- Genetic Algorithms or evolutionary computing, Computational models of genetics and evolution. The three basic ingredients are selection of solutions based on their fitness, reproduction of genes, and occasional mutation. The computer finds better and better solutions to a problem as species evolve to better adapt to their environments.
- Fuzzy Systems, a technique of "continuization," that is, extending concepts to a continuous paradigm, especially for traditionally discrete disciplines such as sets and logic. In ordinary logic, proposition is either true or false, with nothing between, but fuzzy logic allows truthfulness in various degrees.
- Rough Set Theory, A technique of "quantization" and mapping. "Rough" sets means approximation sets. Given a set of elements and attribute values associated with these elements, some of which can be imprecise or incomplete, the theory is suitable to reasoning and discovering relationships in the data.
- Chaos, Nonlinear deterministic dynamical systems that exhibit sustained irregularity and extreme sensitivity to initial conditions.