Artificial Intelligence by: Biswanil Bose

This paper is the introduction to Artificial intelligence (AI). Artificial intelligence is exhibited by artificial entity, a system is generally assumed to be a computer. AI systems are now in routine use in economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games like computer chess and other video games. We tried to explain the brief ideas of AI and its application to various fields. It cleared the concept of computational and conventional categories. It includes various advanced systems such as Neural Network, Fuzzy Systems and Evolutionary computation. AI is used in typical problems such as Pattern recognition, Natural language processing and more. This system is working throughout the world as an artificial brain. Intelligence involves mechanisms, and AI research has discovered how to make computers carry out some of them and not others. If doing a task requires only mechanisms that are well understood today, computer programs can give very impressive performances on these tasks. Such programs should be considered “somewhat intelligent”. It is related to the similar task of using computers to understand human intelligence.

INTRODUCTION :-
Artificial intelligence (AI) is defined as intelligence exhibited by an artificial entity. Such a system is generally assumed to be a computer. Although AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behaviour, learning and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, speech, and facial recognition. As such, it has become a scientific discipline, focused on providing solutions to real life problems. AI systems are now in routine use in economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games like computer chess and other video games.

History :-
The intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Intelligent artifacts appear in literature since then, with real mechanical devices actually demonstrating behaviour with some degree of intelligence. After modern computers became available following World War-II, it has become possible to create programs that perform difficult intellectual tasks. 1950 – 1960:-

The first working AI programs were written in 1951 to run on the Ferranti Mark I machine of the University of Manchester (UK): a draughts-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz.

1960 – 1970 :-
During the 1960s and 1970s Marvin Minsky and Seymour Papert publish Perceptrons, demonstrating limits of simple neural nets and Alain Colmerauer developed the Prolog computer language. Ted Shortliffe demonstrated the power of rule-based systems for knowledge representation and inference in medical diagnosis and therapy in what is sometimes called the first expert system. Hans Moravec developed the first computer-controlled vehicle to autonomously negotiate cluttered obstacle courses. 1980’s ONWARDS :-

In the 1980s, neural networks became widely used with the back propagation algorithm, first described by Paul John Werbos in 1974. The 1990s marked major achievements in many areas of AI and demonstrations of various applications. Most notably Deep Blue, a chess-playing computer, beat Garry Kasparov in a famous six-game match in 1997.

Fundamentals

* The notion of expressing computation as an algorithm
* Godel’s Incompleteness Theorm(1931):
In any language expressive enough to describe the properties of natural numbers, there are true statements that are undecidable, that is, their truth cannot be established by any algorithm.

* Church-Turing Thesis (1936):

* The Turing machine is capable of computing any computable function

* This is the accepted definition of computability

* The notion of intractability

* NP completeness

* Reduction

Approaches:-
Field of AI can be divided into two broad categories:

1. Bottom-Up approach: – Build electronic replicas of the human brain’s complex network of neurons. (e.g. Artificial Neural Network)

2. Top-Down approach: – It attempts to mimic the brain’s behavior with computer programs. (e.g. Genetic Programming, Fuzzy Logic) Branches of AI
* Logical AI
* Search
* Natural language processing
* pattern recognition
* Knowledge representation
* Inference From some facts, others can be inferred.
* Automated reasoning
* Learning from experience
* Planning To generate a strategy for achieving some goal
Epistemology Study of the kinds of knowledge that are required for solving problems in the world. * Ontology Study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are. * Genetic programming

Categories of AI :-
AI divides roughly into two schools of thought:

* Conventional AI.

* Computational Intelligence (CI).

Conventional AI :-
Conventional AI mostly involves methods now classified as machine learning, characterized by formalism and statistical analysis. This is also known as symbolic AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI). Methods include:

* Expert systems: apply reasoning capabilities to reach a conclusion. An expert system can process large amounts of known information and provide conclusions based on them. * Case based reasoning

* Bayesian networks
Behavior based AI: a modular method of building AI systems by hand.

Computational Intelligence (CI) :-
Computational Intelligence involves iterative development or learning (e.g. parameter tuning e.g. in connectionist systems). Learning is based on empirical data and is associated with non-symbolic AI, scruffy AI and soft computing. Methods include:

* Neural networks: systems with very strong pattern recognition capabilities. * Fuzzy systems: techniques for reasoning under uncertainty, has been widely used in modern industrial and consumer product control systems. * Evolutionary computation: applies biologically inspired concepts such as populations, mutation and survival of the fittest to generate increasingly better solutions to the problem. These methods most notably divide into evolutionary algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant algorithms).

Artificial Neural Networks

An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems process information. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems.

Human Neuron Artificial Neuron

Firing Rule of ANN
* The firing rule is an important concept in neural networks and accounts for their high flexibility. A firing rule determines how one calculates whether a neuron should fire for any input pattern. It relates to all the input patterns, not only the ones on which the node was trained. * For example, a 3-input neuron is taught to output 1 when the input (X1,X2 and X3) is 111 or 101 and to output 0 when the input is 000 or 001. Then, according truth tables before and after applying the firing rule.`

Firing Rule of ANN (contd.)
X1| 0| 0| 0| 0| 1| 1| 1| 1|
X2| 0| 0| 1| 1| 0| 0| 1| 1|
X3| 0| 1| 0| 1| 0| 1| 0| 1|
OUT| 0| 0| 0/1| 0/1| 0/1| 1| 0/1| 1|

X1| 0| 0| 0| 0| 1| 1| 1| 1|
X2| 0| 0| 1| 1| 0| 0| 1| 1|
X3| 0| 1| 0| 1| 0| 1| 0| 1|
OUT| 0| 0| 0| 0/1| 0/1| 1| 1| 1|

Genetic Programming
* Genetic programming is an automated method for creating a working computer program from a high-level problem statement of a problem. * It achieves this goal of automatic programming by genetically breeding a population of computer programs using the principle of Darwinian natural selection and biologically inspired operations.

Preparatory Steps of Genetic Programming

The set of terminals (e.g., the independent variables of the problem) for each branch of the to-be-evolved program. 1. The set of primitive functions for each branch of the to-be-evolved program. 2. The fitness measure.

3. Certain parameters for controlling the run.
4. The termination criterion and method for designating the result of the run.

Genetic Operations
1. Crossover : – Create new offspring program(s) for the new population by recombining randomly chosen parts from two selected programs 2. Mutation : – Create one new offspring program for the new population by randomly mutating a randomly chosen part of one selected program. 3. Reproduction: – Copy the selected individual program to the new population. 4. Architecture Altering operations : – Choose an architecture-altering operation from the available repertoire of such operations and create one new offspring program for the new population by applying the chosen architecture-altering operation to one selected program.

Executional Steps of Genetic Programming

1. Randomly create an initial population.
2. Iteratively perform the following steps until the termination criterion is satisfied: * Execute each program in the population and ascertain its fitness using the problem’s fitness measure. * Select one or two individual program(s) from the population with a probability based on fitness (with reselection allowed) to participate in the genetic operations. * Create new individual program(s) for the population by applying genetic operations with specified probabilities. 3. After the termination criterion is satisfied, the single best program in the population produced during the run is harvested and designated as the result of the run. If the run is successful, the result may be a solution (or approximate solution) to the problem.

Main Goals Of AI
* Two main goals of AI:

* To understand human intelligence better. We test theories of human intelligence by writing programs which emulate it. * To create useful “smart” programs able to do tasks that would normally require a human expert.

Typical Problems To Which AI Methods Are Applied :-
* Pattern recognition
* Optical character recognition
* Handwriting recognition
* Speech recognition
* Face recognition
* Non-linear control and Robotics
* Computer vision, Virtual reality and Image processing
* Game theory and Strategic planning

Other fields in which AI methods are implemented :-
* Automation.
* Cybernetics.
* Hybrid intelligent system.
* Intelligent agent.
* Intelligent control.
* Automated reasoning.
* Data mining.
* Behavior-based robotics.
* Cognitive robotics.
* Developmental robotics.
* Evolutionary robotics.
* Chatbot.
* Knowledge Representation.
American Association for Artificial Intelligence (AAAI) :-

Founded in 1979, the American Association for Artificial Intelligence (AAAI) is a nonprofit scientific society devoted to advancing the scientific understanding of the mechanisms underlying thought and intelligent behaviour and their embodiment in machines. AAAI also aims to increase public understanding of artificial intelligence, improve the teaching and training of AI practitioners, and provide guidance for research planners and funders concerning the importance and potential of current AI developments and future directions.

APPLICATIONS OF AI :-

* Game Playing :-
You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation–looking at hundreds of thousands of positions. * Speech Recognition :-

In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.

* Understanding Natural Language :-
Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains. * Computer Vision :-

The world is composed of three-dimensional objects, but the inputs to the human eye and computer’s TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use. * Expert Systems :-

A “knowledge engineer” interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. How well this works depends on whether the intellectual mechanisms required for the task are within the present state of AI. One of the first expert systems was MYCIN in 1974, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. * Heuristic Classification :-

One of the most feasible kinds of expert system given the present knowledge of AI is to put some information in one of a fixed set of categories using several sources of information. An example is advising whether to accept a proposed credit card purchase. Information is available about the owner of the credit card, his record of payment and also about the item he is buying and about the establishment from which he is buying it (e.g., about whether there have been previous credit card frauds at this establishment).

Limits OF AI

Today’s AI systems have been able to achieve limited success in some of these tasks. * In Computer vision, the systems are capable of face recognition

* In Robotics, we have been able to make vehicles that are mostly autonomous.

* In Natural language processing, we have systems that are capable of simple machine.

* Translation.

* Today’s Expert systems can carry out medical diagnosis in a narrow domain

* Speech understanding systems are capable of recognizing several thousand words

* Continuous speech

* In Games, AI systems can play at the Grand Master level in chess (world champion),

Conclusion :-

We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. AI systems are now in routine use in various field such as economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games etc. AI is an exciting and rewarding discipline. AI is branch of computer science that is concerned with the automation of intelligent behavior. The revised definition of AI is – AI is the study of mechanisms underlying intelligent behavior through the construction and evaluation of artifacts that attempt to enact those mechanisms. So it is concluded that it work as an artificial human brain which have an unbelievable artificial thinking power.

BIBLIOGRAPHY:-

* Programs with Common Sense :-
John McCarthy, In Mechanization of Thought Processes, Proceedings of the Symposium of the National Physics Laboratory, 1959.

* Artificial Intelligence, Logic and Formalizing Common Sense :- Richmond Thomason, editor, Philosophical Logic and Artificial Intelligence. Klüver Academic, 1989.

* Concepts of Logical AI :-
Tom Mitchell.
Machine Learning.
McGraw-Hill, 1997.

* Logic and artificial intelligence :-
Richmond Thomason.
In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Fall 2003. http://plato.stanford.edu/archives/fall2003/entries/logic-ai/.

LINKS :-

* http://www.aaai.org/

* http://www-formal.stanford.edu/

* http://insight.zdnet.co.uk/hardware/emergingtech/

* http://www.genetic-programming.com/