text: It DEPENDS UPON YOUR PROBLEM.\nArtificiaI Intelligence=AI\nAI divides roughly into two schools of thought: Conventional AI and Computational Intelligence (CI).\n\nConventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI). (Also see semantics.) Methods include:\n\nExpert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them. \nCase based reasoning \nBayesian networks \nBehavior based AI: a modular method of building AI systems by hand. \nComputational Intelligence involves iterative development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. Methods mainly include:\n\nNeural networks: systems with very strong pattern recognition capabilities. \nFuzzy systems: techniques for reasoning under uncertainty, have been widely used in modern industrial and consumer product control systems. \nEvolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms). \nWith 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. It is thought that the human brain uses multiple techniques to both formulate and cross-check results. Thus, integration is seen as promising and perhaps necessary for true AI.