Artificial intelligence deals with mimicking the way the human brain works or evolution of life and other such natural phenomena. Here are some of the artificial intelligence techniques:
- Artificial Neural Networks
- Fuzzy logic
- Genetic algorithms
- Cellular automata
Artificial Neural Networks are inspired by the way the brain works. A neural network consists of a network of nodes. Each node is capable of making a simplistic decision or a simple calculation on the inputs, and providing an output. By interconnecting a large number of such nodes, it is possible to do data analysis and complex decision making. Each node has a threshold assigned and each connection between nodes is assigned a weight. A random NN is constructed to begin with. This is then “trained” by providing inputs, and matching given outputs against pre-calculated known outputs. When a mismatch is detected between current output and favoured output, the weights and thresholds are suitably modified.
Genetic algorithms are based on the process of evolution and natural selection. To begin with a pool of random “algorithms” is built. Each such algorithm is tested with given input and required output. Those algorithms that give results closest to the desired outcome are selected. Thereafter the next generation is built by combining pairs of algorithms from the previous generation and adding more steps (random mutation). This generation is again tested for fitness. This process is repeated as many times as needed. The algorithms keep increasing in complexity with each generation.
Please refer to Cellular automaton for more information on cellular automata and Fuzzy logic for more information on Fuzzy Logic.