1. Expert systems: apply the capabilities of the consideration to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on such information.
2. Petimbangan based case
3. Bayesian Networks
4. Behavior based AI: a modular method of building AI systems manually
Computational intelligence involves iterative development or learning (eg parameter tuning eg in connectionist systems. Learning is based on empirical data and are associated with non-symbolic AI, scruffy AI and soft computing. Methods mainly include:
1. Neural networks: systems with pattern recognition capabilities are very strong
2. Fuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems.
3. Evolutionary computation: applies the concepts of biologically inspired, such as populations, mutation and survival of the fittest to produce better solutions.
These methods most notably divide into evolutionary algorithms (eg genetic algorithms) and swarm intelligence (eg ant algorithms)
With hybrid intelligent systems attempts are made to combine these two groups. Expert inference rules can be generated through neural network or production rules from statistical learning such as in ACT-R. A promising new approach mentioned that the strengthening of intelligence tried to achieve artificial intelligence in the process of evolutionary development as a side effect of the strengthening of human intelligence through technology.