Artificial Intelligence is a concept that concerned people from all around the world and from all times. Ancient Greeks and Egyptians represented in their myths and philosophy machines and artificial entities which have qualities resembling to those of humans, especially in what thinking, reasoning and intelligence are concerned.

Artificial intelligence is a branch of computer science concerned with the study and the design of the intelligent machines. The term of “artificial intelligence”, coined at the conference that took place at Dartmouth  in 1956 comes from John McCarthy who defined it as the science of creating intelligent machine.

Along with the development of the electronic computers, back in 1940s, this domain and concept known as artificial intelligence and concerned with the creation of intelligent machines resembling to humans, more precisely, having qualities such as those of a human being, started produce intelligent machines.

Mechanical or “formal” reasoning has been developed by philosophers and mathematicians since antiquity. The study of logic led directly to the invention of the programmable digital electronic computer, based on the work of mathematician Alan Turing and others. Turing’s theory of computation suggested that a machine, by shuffling symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. This, along with recent discoveries in neurology, information theory and cybernetics, inspired a small group of researchers to begin to seriously consider the possibility of building an electronic brain.

The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert Simon, became the leaders of AI research for many decades. They and their students wrote programs that were, to most people, simply astonishing: computers were solving word problems in algebra, proving logical theorems and speaking English. By the middle of the 1960s, research in the U.S. was heavily funded by the Department of Defense and laboratories had been established around the world. AI’s founders were profoundly optimistic about the future of the new field: Herbert Simon predicted that “machines will be capable, within twenty years, of doing any work a man can do” and Marvin Minsky agreed, writing that “within a generation … the problem of creating ‘artificial intelligence‘ will substantially be solved”.

The disciplines implied by the artificial intelligence are extremely various. Fields of knowledge such as Mathematics, Psychology, Philosophy, Logic, Engineering, Social Sciences, Cognitive Sciences and Computer Science are extremely important and closely interrelated are extremely important when it comes to artificial intelligence. All these fields and sciences contribute to the creation of intelligent machines that have resemblance to human beings.

The application areas of artificial intelligence are extremely various such as Robotics, Soft Computing, Learning Systems, Planning, Knowledge Representation and Reasoning, Logic Programming, Natural Language Processing, Image Recognition, Image Understanding, Computer Vision, Scheduling, Expert Systems and more others.

Recursion An algorithmic technique where, in order to accomplish a task, a function calls itself with some part of the task.

Symbolic computation AI programming involves (mainly) manipulating symbols and not numbers. These symbols might represent objects in the world and relationships between those objects – complex structures of symbols are needed to capture our knowledge of the world.

Term The fundamental data structure in Prolog is the term which can be a constant, a variable or a structure. Structures represent atomic propositions of predicate calculus and consist of a functor name and a parameter list.

PROGRAMMING LANGUAGES IN ARTIFICIAL INTELLIGENCE (AI) are the major tool for exploring and building computer programs that can be used to simulate intelligent processes such as learning, reasoning and understanding symbolic information in context. Although in the early days of computer language design the primarily use of computers was for performing calculations with numbers, it was also found out quite soon that strings of bits could represent not only numbers but also features of arbitrary objects. Operations on such features or symbols could be used to represent rules for creating, relating or manipulating symbols. This led to the notion of symbolic computation as an appropriate means for defining algorithms that processed information of any type, and thus could be used for simulating human intelligence. Soon it turned out that programming with symbols required a higher level of abstraction than was possible with those programming languages which were designed especially for number processing, e.g., Fortran.

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