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  CS6659 - Artificial Intelligence Unit-1INTRODUCTION TO AI AND PRODUCTION SYSTEMS   Introduction to AI-Problem formulation, Problem Definition - Production systems, Control  strategies, Search strategies. Problem characteristics, Production system characteristics -Specialized productions system- Problem solving methods  Problem graphs, !atching, Inde ing and #euristic functions -#ill Climbing-Depth first and $reath first, Constraints satisfaction  %elated algorithms, !easure of performance and analysis of search algorithms. 1. Introduction to Artificial Intelligence Definition:  Artificial Intelligence (AI) is a branc !f Science ic #eals it el$ing %acines t! fin# s!l&ti!ns t! c!%$le' $r!ble%s in a %!re &%an-lie fasi!n  This generally involves borrowing characteristics from human intelligence, and applyingthem as algorithms in a computer friendly way.  AI is the branch of computer science that attempts to approximate the results of humanreasoning by organizing and manipulating factual and heuristic knowledge.  AI is generally associated with Computer Science , but it has many important links withother fields such as  !aths ,  Psychology , Cognition ,  $iology  and  Philosophy , among manyothers  Areas of AI activity include expert systems, natural language understanding, speechrecognition, vision, and robotics.  Our ability to combine knowledge from all these fields will ultimately benefit our  progress in the uest of creating an intelligent artificial being.  The term was coined in ! #$ by %ohn &c'arthy at the &assachusetts Institute of Technology.  There are several programming languages that are known as AI languages because theyare used almost exclusively for AI applications. The two most common are LISP  ()ist*rocessing+ and Prolog  (*rogramming )ogic+ .   The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In &ay, ! , an I-& supercomputer called /eep -lue defeated world chess champion 0ary 1asparov in a chessmatch. 1.1Branches of Artificial intelligence 2 a!es la#ing 3 programming computers to play games such as chess and checkers.2 $% ert s#ste!s 3 programming computers to make decisions in reallife situations (for example, some expert systems help doctors diagnose diseases based on symptoms+.2 &atural Language Processing 3 programming computers to understand natural humanlanguages.2 &eural &et'or(s 3 4ystems that simulate intelligence by attempting to reproduce the typesof physical connections that occur in human brains. 2 )o*otics 3 programming computers to see and hear and react to other sensory stimulicurrently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior+. 1.+ A lications of AIa!e la#ing There is some AI in them, but they play well against people mainly through brute forcecomputationlooking at hundreds of thousands of positions. To beat a world champion by bruteforce and known reliable heuristics reuires being able to look at 566 million positions per second. S eech recognition In the ! 6s, computer speech recognition reached a practical level for limited purposes. Thus7nited Airlines has replaced its keyboard tree for flight information by a system using speechrecognition of flight numbers and city names. It is uite convenient. ,nderstanding natural language %ust getting a seuence of words into a computer is not enough. *arsing sentences is not enougheither. 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. Co! uter ision The world is composed of threedimensional ob8ects, but the inputs to the human eye andcomputers9 T: cameras are two dimensional. 4ome useful programs can work solely in twodimensions, but full computer vision reuires partial threedimensional information that is not 8ust 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. $% ert s#ste!s A ;;knowledge engineer99 interviews experts in a certain domain and tries to embody their knowledge in a computer program for carrying out some task. <ow well this works depends on  whether the intellectual mechanisms reuired for the task are within the present state of AI. Oneof the first expert systems was &='I> in ! ?, which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical students or practicing doctors, provided its limitations were observed. Intelligent )o*ots @ obots are able to perform the tasks given by a human. They have sensorsto detect physical data from the real world such as light, heat, temperature, movement, sound, bump, and pressure. They have efficient processors, multiple sensors and huge memory, toexhibit intelligence. In addition, they are capable of learning from their mistakes and they canadapt to the new environment. euristic classification One of the most feasible kinds of expert system given the present knowledge of AI is to put someinformation in one of a fixed set of categories using several sources of information. An exampleis advising whether to accept a proposed credit card purchase. Information is available about theowner of the credit card, his record of payment and also about the item he is buying and aboutthe establishment from which he is buying it (e.g., about whether there have been previous creditcard frauds at this establishment+. 1./ istor# of AI <ere is the history of AI during 56th century @ 0ear ! ?B! ?#! #6! #$! #C! $ ! B ilestone 2 Innoation Doundations for neural networks laid.Isaac Asimov, a 'olumbia 7niversity alumni, coined the term  %obotics .AlanTuringintroducedTuringTestforevaluationofintelligenceandpublished Computing !achinery and Intelligence. 'laude 4hannon published  Detailed Analysis of Chess Playing as a search.%ohn &c'arthy coined the term  Artificial Intelligence . /emonstration of the first runningAI program at 'arnegie &ellon 7niversity.%ohn &c'arthy invents )I4* programming language for AI.4cientists at 4tanford esearch Institute /eveloped Sha&ey , a robot, euipped with locomotion, perception, and problem solving.The Assembly obotics group at Edinburgh 7niversity built  'reddy , the Damous 4cottishobot, capable of using vision to locate and assemble models.!  ! 6 The first computercontrolled autonomous vehicle, 4tanford 'art, was built. &a8or advances in all areas of AI F   4ignificant demonstrations in machine learning  'asebased reasoning  &ultiagent planning  4cheduling  /ata mining, Geb 'rawler  3  ! 5666  natural language understanding and translation  :ision, :irtual eality  0amesThe /eep -lue 'hess *rogram beats the then world chess champion, 0arry 1asparov.Interactive robot pets become commercially available. &IT displays  (ismet  , a robot witha face that expresses emotions. +. Pro*le! Soling There are four things that are to be followed in order to solve a problem3!.  Define the problem . A precise definition that must include precise specifications of whatthe initial situation(s+ will be as well as what final situations constitute acceptablesolutions to the problem.5.  Problem must be analyzed  . 4ome of the important features land up having an immenseimpact on the appropriateness of various possible techniues for solving the problem.B.  Isolate and represent the tas& &no)ledge that is necessary to solve the problem.?.Amongst the available ones choose the best problem-solving techni*ue (s+ and apply thesame to the particular problem. +.1Defining the Pro*le! as state Search To understand what exactly artificial intelligence is, we illustrate some common problems.*roblems dealt with in artificial intelligence generally use a common term called 9state9. A staterepresents a status of the solution at a given step of the problem solving procedure. The solutionof a problem, thus, is a collection of the problem states.The problem solving procedure applies an operator to a state to get the next state. Then it appliesanother operator to the resulting state to derive a new state. The process of applying an operator to a state and its subseuent transition to the next state, thus, is continued until the goal (desired+state is derived. 4uch a method of solving a problem is generally referred to as state spaceapproach Dor example, in order to solve the problem play a game, which is restricted to two person table or board games, we reuire the rules of the game and the targets for winning as wellas a means of representing positions in the game.The opening position can be defined as the initial state and a winning position as a goal state,there can be more than one. legal moves allow for transfer from initial state to other statesleading to the goal state. <owever the rules are far too copious in most games especially chesswhere they exceed the number of particles in the universe !6. Thus the rules cannot in general besupplied accurately and computer programs cannot easily handle them. The storage also presentsanother problem but searching can be achieved by hashing. The number of rules that are usedmust be minimized and the set can be produced by expressing each rule in as general a form as possible.The representation of games in this way leads to a state space representation and it is natural for 
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