Expert System (ES) and Artificial Intelligence (AI)

Expert System (ES) is a knowledge representation to describe the way an expert in approaching a problem. ES is more centered on how to manipulate the coding and knowledge of information (rules eg if...then). As for how the ES as follows:

1. Users communicate with the system using an interactive dialog.
2. ES asks questions (which will ask an expert) and the user provides an answer.
3. The answer used to determine which rules are used and the ES system provides recommendations based on rules that have been saved.
4. A knowledge engineer responsible for the acquisition of knowledge on how to do, as an analyst but are trained to use different techniques.

Artificial Intelligence (AI) is defined as the intelligence of scientific entities. Such systems are generally considered to be a computer. Intelligence was created and put into a machine (computer) in order to do the job as do humans. Several kinds of fields that use artificial intelligence expert systems, among others, computer games (games), fuzzy logic, neural networks and robotics.

Broadly speaking, the AI ​​is divided into two schools of thought namely Conventional AI and Computational Intelligence (CI, Computational Intelligence). Conventional AI mostly involves methods now diklasifiksikan as machine learning, characterized by formalism and statistical analysis. Also known as symbolic AI, logical AI, AI and AI pure old fashioned way (GOFAI, Good Old Fashioned Artificial Intelligence). Method-the method include:

1. Expert systems: the capability to apply judgment to reach conclusions. An expert system can process large amounts of known information and provide conclusions based on such information.
2. Considerations based on case
3. Bayesian networks
4. Behavior-based AI: a modular method to the formation of AI systems manually

Computational intelligence involves iterative development or learning (eg parameter tuning as in connectionist systems. Learning is based on empirical data and are associated with non-symbolic AI, AI irregular and soft computing. Basic methods include:

1. Neural networks: systems with pattern recognition capabilities are very strong
2. Fuzzy systems: techniques for consideration under uncertainty, has been used extensively in modern industrial and consumer product control systems.
3. Evolutionary Computation: applying concepts such as biologically inspired population, mutation and the "survival of the fittest" to produce a better solution.

These methods are mainly divided into evolutionary algorithms (eg genetic algorithms) and swarm intelligence (eg ant algorithms)

With hybrid intelligent systems, experiments designed to combine these two groups. Expert inference rules can be generated through a neural network or production rules from statistical learning such as the ACT-R. A promising new approach is mentioned that the strengthening of intelligence to try to achieve artificial intelligence in the process of evolutionary development as a side effect of the strengthening of human intelligence through technology.