In this we are going to discuss various disciplines such as philosophy mathematics, economics, neuroscience, psychology computer engineering, control theory and cybernetics that contribute various ideas, view points and techniques to AI.

Philosophy:

• AI takes the following ideas from philosophy.

• Can formal rules be used to draw valid conclusion?

Aristotle (312 – 322 B.C ) was the first to formulate the precise set of laws governing the rational parts of the mind. He developed an informal system of syllogisms for proper reasoning, which in principle allowed one to generate conclusions mechanically given initial premises.

• How does the mind arises from a physical brain?

It is one thing to say that mind operates, at least in part, according to logical rules and to build physical system that emulates some of those rules, but it's another thing to say that mind itself is such a physical system.

• Where does knowledge from?

Given a physical mind that manipulate knowledge, the next problem is to establish the source of knowledge.

• How does knowledge lead to action?

Philosophy tries to answer the connection between knowledge and action. This question is vital to AI because intelligence requires action as well as reasoning.

Mathematics:

Philosophers staked out some of the fundamental ideals of AI, but the leap to formal science required a level of mathematical formalization in their fundamental areas:

• What are the formal rules to draw valid conclusions?

• What can be compute?

• How do we reason with uncertain information?

Economics:

AI takes the following ideas from economics.

• 1] How should we make decisions so as to maximize payoff (utility)?

Decision theory, which combines probability theory. With utility theory, provides a formal and complete framework for decision made under uncertainty. This is suitable for large “economics” where each agent need pay no attention to the action of the other gent as individuals.

• How should we do this when others may not go along?

For small economics, the situation is much more like a game. The action of one player can significantly affect the utility of another. Unlike decision theory game theory does not offer an unambiguous prescription for selecting actions.

• How should we do this when the payoff may be far in the future?

For the most part economist did not address this question. This topic was pursed in the field of operation research (OR).

Neuroscience.

Neuroscience is the study of the nervous system, particularly the brain. This study of how brain process information directly help in the development of AI.

Psychology.

Psychology tries to answer the following question:

• How do humans and animals think and act? Modern science proves that computer models could be used to address the psychology of memory, language and logical thinking respectively. It is now common view among psychologist that "a cognitive theory should be like a computer program."

Computer engineering:

• How can we build an efficient computer?

For AI to succeed, we need two things:

Intelligence and artifact.

The computer has been the artifact of the choice.

Control theory and cybernetics.

• How can artifacts operate under their own control?

Control theory and cybernetics deals with the self controlling machine. Modern control theory has its goal to design the system that maximize an objective function overtime. This roughly match the AI view: designing system that behave optimally.

Linguistics:

Language is directly related to thought. Modern linguistics and AI, were born at about the same time and grew up together, intersecting in a hybrid field called computational linguistics or natural language processing.