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1. 1. Concept

   • Artificial intelligence techniques refer to different fields of research and application development of artificial intelligence. AI is the type of intelligence that shows the different artifacts created by humans, usually referred to computer systems. But it also refers to the field of scientific research in the intention of creating environments that make use of said intelligence.AI is intended to come close to the workings of the human mind. And it is used when it is considered useful to incorporate into a computer system knowledge or behavior in the face of events that would be more typical of a human being.
     • 1. Machine learning or automatic learning Machine Learning is the branch of science that seeks to develop artificial intelligence techniques that allow computers to learn by themselves. To do this, programs are created that can generalize certain responses from unstructured information, which is provided as examples. With this, knowledge is induced by the computer. 2. Fuzzy logic or fuzzy logic Known as heuristic logic. This technique affects the relative of an observed scenario as a differential position. It is a type of logic that takes two random values, contextualized and related to each other. For example, consider a person of 2 meters as tall having previously taken the value of a person of one meter as short.
       • 3. Artificial life It consists of the study of life and artificial environments that show qualities of living beings in simulated environments. One of the artificial intelligence techniques with the most projection for the future in the field of research. 4. Expert systems It refers to an information system that is based on the knowledge of a highly complex and very specific application area. Serves as a consultant and expert assistant for the users of its interface
         • 5. Data mining or data mining This technique consists of the discriminated extraction of information that is implicit in the data handled. Such information, previously unknown, is destined to be used in some other process. Data mining probes, prepares and explores the data in order to extract some information that is hidden in it. 6. Bayesian Networks Also known as belief networks, these networks are a multivariate probabilistic model, which relates a set of random variables using a directed graph to explicitly indicate chance influence. With a probability updating engine called Bayes' Theorem, these networks become a very useful tool when calculating probabilities in cases of new evidence. It is one of the types of networks that are called casual type.

2. 2. Main characteristics

3. 3. Objective and/or function