ARTRIFICAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE

Artificial intelligence is the branch of computer science concerned with making computer behave like humans. And process all activities similar to the brain of human being.
The human brain is the fundamental to control all body activities of body with provider of instructions to all glands immediately as an electrical

Artificial intelligence based system that converts the knowledge of an expert in a specific subject into a software code. This code can be merged with other such codes (based on the knowledge of other experts) and used for answering questions (queries) submitted through a computer.

The central goals of Artificial intelligence research include reasoning, knowledge, planning, and learning, natural language processing (communication), perception and the ability to move and manipulate objects

Knowledge based on acquires the skills from human brain and perform similar to that with the source code processed

Planning is the actual way of thinking and produces the desired program that will be used for the long time.

Mechanical or has been developed by philosophers and mathematicians since antiquity

Human likenesses believed to have intelligence were built in every major civilization.

Artificial intelligence includes the following areas of specialization:

Games playing: programming computers to play games against human opponents

 Expert systems: programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)

 Natural language: programming computers to understand natural human languages

 Neural networks: Systems that simulate intelligence by attempting to reproduce the types of physical connections that occur in animal brains

 Robotic programming computers to see and hear and react to other sensory stimuli

GAME PLAYING

Game artificial intelligence is the techniques used in computer and video games to produce the illusion of intelligence in the behavior of non-player characters

•      Rich tradition of creating game-playing programs in AI

•      Many similarities to search

•      Most of the games studied

–     have two players,

–     are zero-sum: what one player wins, the other loses

–     have perfect information: the entire state of the game is known to both players at all times

•      E.g., tic-tac-toe, checkers, chess, Go, backgammon, …

•      Will focus on these for now

•      Recently more interest in other games

–     Esp. games without perfect information; e.g., poker

•      Need probability theory, game theory for such games

• A game must ‘feel’ natural

• Obey laws of the game

• Characters aware of the environment

• Path finding (A* algorithm)

• Decision making

• Planning

• Game ‘bookkeeping’, scoring

• ~50% of game project time is spent on building AI

Expert System

An expert system, sometimes known as artificial intelligence, is a computer program that simulates the knowledge and judgment of humans.

Expert systems typically consist of three parts

1. Knowledge base

2. Inference engine

3. Interface

Knowledge base which contains the information acquired by interviewing experts and logic rules that govern how that information is applied

Inference engine that interprets the submitted problemagainst the rules and logic of information stored in the knowledge base

Interface that allows the user to express the problem in a human language such as English.

Expert systems have played a large role in financial services, healthcare, manufacturing and video games.

It is in artificial intelligence that expert systems have had the most impact, especially in finance, telecommunications, customer service, transportation, aviation, and more recently, written communication.

Natural Language

Natural-language processing this allows people to interact with computers without needing any specialized knowledge

Natural language processing gives machines the ability to read and understand the languages that humans speak

Neural Networks
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.

It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people

 An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.

Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.

There are some important of neural networks which include

·         Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience

·         Self-organization An ANN can create its own organization or representation of the information it receives during learning time.

·         Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability

·         Fault Tolerance via Redundant Information Coding: Partial destruction of a network leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.

Neural networks acts as a human  as human brain a typical neuron collects signals from others through a host of fine structures called dendrites. The neuron sends out spikes of electrical activity through a long, thin stand known as an axon, which splits into thousands of branches.

At the end of each branch, a structure called a synapse converts the activity from the axon into electrical effects that inhibit or excite activity from the axon into electrical effects that inhibit or excite activity in the connected neurones. When a neuron receives excitatory input that is sufficiently large compared with its inhibitory input, it sends a spike of electrical activity down its axon. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on another changes.

 

 

 Robots

Robotics is a recreation of the human thought process -- a man-made machine with our intellectual abilities.
This would include the ability to learn just about anything, the ability to reason, the ability to use language and the ability to formulate original ideas.

 Today's Artificial intelligence machines can replicate some specific elements of intellectual ability.
Computers can already solve problems in limited realms. The basic idea of AI problem-solving is very simple, though its execution is complicated. First, the AI robot or computer gathers facts about a situation through sensors or human input.
 The computer compares this information to stored data and decides what the information signifies. The computer runs through various possible actions and predicts which action will be most successful based on the collected information.

 The entire of the robotics play a great role in the development of science and technology where by the huge activities of holding materials and prepare many device within a short period of time and contribute a lot of money such as build of cars lorry construction of railway and so many