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A paper presentation abstract

  1. 1. A PAPER PRESENTATIONONArtificial IntelligenceJ.G.M.Jagagdeesh KumarDepartment of C.S.E. (III year)Affiliated to JNTU KDJR College of Engineering and Technology,Gudavalli, VijayawadaKrishna (dt.), Andhra Pradesh, India.Contact details: J.G.M.Jagagdeesh KumarMobile number:9700234518Email Id:jjagadeesh13@gmail.com
  2. 2. IntroductionIn which we try to explain why we considerartificial intelligence to be a subject mostworthy of study, and in which we try to decidewhat exactly it is, this being a good thing todecide before embarking.Humankind has given itself the scientificname homo sapiens--man the wise--becauseour mental capacities are so important to oureveryday lives and our sense of self. The fieldof artificial intelligence, or AI, attempts tounderstand intelligent entities. Thus, onereason to study it is to learn more aboutourselves. But unlike philosophy andpsychology, which are also concerned withintelligence, AI strives to build intelligententities as well as understand them. Anotherreason to study AI is that these constructedintelligent entities are interesting and useful intheir own right. AI has produced manysignificant and impressive products even atthis early stage in its development. Althoughno one can predict the future in detail, it isclear that computers with human-levelintelligence (or better) would have a hugeimpact on our everyday lives and on the futurecourse of civilization.AI addresses one of the ultimate puzzles. Howis it possible for a slow, tiny brain{brain},whether biological or electronic, to perceive,understand, predict, and manipulate a worldfar larger and more complicated than itself?How do we go about making something withthose properties? These are hard questions, butunlike the search for faster-than-light travel oran antigravity device, the researcher in AI hassolid evidence that the quest is possible. Allthe researcher has to do is look in the mirror tosee an example of an intelligent system.AI is one of the newest disciplines. It wasformally initiated in 1956, when the name wascoined, although at that point work had beenunder way for about five years. Along withmodern genetics, it is regularly cited as the``field I would most like to be in by scientistsin other disciplines. A student in physics mightreasonably feel that all the good ideas havealready been taken by Galileo, Newton,Einstein, and the rest, and that it takes manyyears of study before one can contribute newideas. AI, on the other hand, still has openingsfor a full-time Einstein.The study of intelligence is also one of theoldest disciplines. For over 2000 years,philosophers have tried to understand howseeing, learning, remembering, and reasoningcould, or should, be done. The advent ofusable computers in the early 1950s turned thelearned but armchair speculation concerningthese mental faculties into a real experimentaland theoretical discipline. Many felt that thenew ``Electronic Super-Brains had unlimitedpotential for intelligence. ``Faster ThanEinstein was a typical headline. But as well asproviding a vehicle for creating artificiallyintelligent entities, the computer provides atool for testing theories of intelligence, andmany theories failed to withstand the test--acase of ``out of the armchair, into the fire. AIhas turned out to be more difficult than manyat first imagined, and modern ideas are muchricher, more subtle, and more interesting as aresult.AI currently encompasses a huge variety ofsubfields, from general-purpose areas such asperception and logical reasoning, to specifictasks such as playing chess, provingmathematical theorems, writingpoetry{poetry}, and diagnosing diseases.Often, scientists in other fields move graduallyinto artificial intelligence, where they find thetools and vocabulary to systematize andautomate the intellectual tasks on which theyhave been working all their lives. Similarly,workers in AI can choose to apply theirmethods to any area of human intellectualendeavor. In this sense, it is truly a universalfield.What is AI?We have now explained why AI is exciting,but we have not said what it is. We could justsay, ``Well, it has to do with smart programs,so lets get on and write some. But the historyof science shows that it is helpful to aim at theright goals. Early alchemists, looking for apotion for eternal life and a method to turnlead into gold, were probably off on the wrongfoot. Only when the aim changed, to that offinding explicit theories that gave accuratepredictions of the terrestrial world, in the same
  3. 3. way that early astronomy predicted theapparent motions of the stars and planets,could the scientific method emerge andproductive science take place. Definitions ofartificial intelligence according to eight recenttextbooks are shown in the table below. Thesedefinitions vary along two main dimensions.The ones on top are concerned with thoughtprocesses and reasoning, whereas the ones onthe bottom address behavior. Also, thedefinitions on the left measure success interms of human performance, whereas theones on the right measure againstan ideal concept of intelligence, which we willcallrationality. A system is rational if it doesthe right thing.``The exciting neweffort to makecomputers think... machines withminds, in the full andliteral sense(Haugeland, 1985)``The automation ofactivities that weassociate with humanthinking, activitiessuch as decision-making, problemsolving, learning ...(Bellman, 1978)``The study of mentalfaculties through theuse of computationalmodels (Charniakand McDermott,1985)``The study of thecomputations thatmake it possible toperceive, reason, andact (Winston, 1992)``The art of creatingmachines thatperform functionsthat requireintelligence whenperformed by people(Kurzweil, 1990)``The study of how tomake computers dothings at which, at themoment, people arebetter (Rich andKnight, 1991)``A field of study thatseeks to explain andemulate intelligentbehavior in terms ofcomputationalprocesses(Schalkoff, 1990)``The branch ofcomputer science thatis concerned with theautomation ofintelligent behavior(Luger andStubblefield, 1993)This gives us four possible goals to pursue inartificial intelligence:Systems that thinklike humans.Systems that thinkrationally.Systems that act likehumansSystems that actrationallyHistorically, all four approaches have beenfollowed. As one might expect, a tensionexists between approaches centered aroundhumans and approaches centered aroundrationality. (We should point out that bydistinguishingbetween human and rational behavior, we arenot suggesting that humans are necessarily``irrational in the sense of ``emotionallyunstable or ``insane. One merely need notethat we often make mistakes; we are not allchess grandmasters even though we may knowall the rules of chess; and unfortunately, noteveryone gets an A on the exam. Somesystematic errors in human reasoning arecataloged by Kahneman et al..) A human-centered approach must be an empiricalscience, involving hypothesis andexperimental confirmation. A rationalistapproach involves a combination ofmathematics and engineering. People in eachgroup sometimes cast aspersions on work donein the other groups, but the truth is that eachdirection has yielded valuable insights. Let uslook at each in more detail.Acting humanly: The Turing Test approachThe Turing Test, proposed by Alan Turing(Turing, 1950), was designed to provide asatisfactory operational definition ofintelligence. Turing defined intelligentbehavior as the ability to achieve human-levelperformance in all cognitive tasks, sufficient tofool an interrogator. Roughly speaking, the testhe proposed is that the computer should beinterrogated by a human via a teletype, andpasses the test if the interrogator cannot tell ifthere is a computer or a human at the otherend. Chapter 26 discusses the details of thetest, and whether or not a computer is reallyintelligent if it passes. For now, programminga computer to pass the test provides plenty towork on. The computer would need to possessthe following capabilities:
  4. 4. natural language processing to enableit to communicate successfully inEnglish (or some other humanlanguage);knowledge representation to storeinformation provided before or duringthe interrogation;automated reasoning to use the storedinformation to answer questions and todraw new conclusions;machine learning to adapt to newcircumstances and to detect andextrapolate patterns.Turings test deliberately avoided directphysical interaction between the interrogatorand the computer, because physical simulationof a person is unnecessary for intelligence.However, the so-called total TuringTestincludes a video signal so that theinterrogator can test the subjects perceptualabilities, as well as the opportunity for theinterrogator to pass physical objects ``throughthe hatch. To pass the total Turing Test, thecomputer will needcomputer vision to perceive objects,androbotics to move them about.Within AI, there has not been a big effort totry to pass the Turing test. The issue of actinglike a human comes up primarily when AIprograms have to interact with people, as whenan expert system explains how it came to itsdiagnosis, or a natural language processingsystem has a dialogue with a user. Theseprograms must behave according to certainnormal conventions of human interaction inorder to make themselves understood. Theunderlying representation and reasoning insuch a system may or may not be based on ahuman model.Thinking humanly: The cognitive modellingapproachIf we are going to say that a given programthinks like a human, we must have some wayof determining how humans think. We need toget inside the actual workings of humanminds. There are two ways to do this: throughintrospection--trying to catch our ownthoughts as they go by--or throughpsychological experiments. Once we have asufficiently precise theory of the mind, itbecomes possible to express the theory as acomputer program. If the programsinput/output and timing behavior matcheshuman behavior, that is evidence that some ofthe programs mechanisms may also beoperating in humans. For example, Newell andSimon, who developed GPS, the ``GeneralProblem Solver (Newell and Simon, 1961),were not content to have their programcorrectly solve problems. They were moreconcerned with comparing the trace of itsreasoning steps to traces of human subjectssolving the same problems. This is in contrastto other researchers of the same time (such asWang (1960)), who were concerned withgetting the right answers regardless of howhumans might do it. The interdisciplinary fieldof cognitive science brings together computermodels from AI and experimental techniquesfrom psychology to try to construct preciseand testable theories of the workings of thehuman mind. Although cognitive science is afascinating field in itself, we are not going tobe discussing it all that much in this book. Wewill occasionally comment on similarities ordifferences between AI techniques and humancognition. Real cognitive science, however, isnecessarily based on experimentalinvestigation of actual humans or animals, andwe assume that the reader only has access to acomputer for experimentation. We will simplynote that AI and cognitive science continue tofertilize each other, especially in the areas ofvision, natural language, and learning. Thehistory of psychological theories of cognitionis briefly covered on page 12.Thinking rationally: The laws of thoughtapproachThe Greek philosopher Aristotle was one ofthe first to attempt to codify ``right thinking,that is, irrefutable reasoning processes. Hisfamous syllogisms provided patterns forargument structures that always gave correctconclusions given correct premises. Forexample, ``Socrates is a man; all men aremortal; therefore Socrates is mortal. Theselaws of thought were supposed to govern theoperation of the mind, and initiated the fieldoflogic.
  5. 5. The development of formal logic in the latenineteenth and early twentieth centuries, whichwe describe in more detail in Chapter 6,provided a precise notation for statementsabout all kinds of things in the world and therelations between them. (Contrast this withordinary arithmetic notation, which providesmainly for equality and inequality statementsabout numbers.) By 1965, programs existedthat could, given enough time and memory,take a description of a problem in logicalnotation and find the solution to the problem,if one exists. (If there is no solution, theprogram might never stop looking for it.) Theso-called logicist tradition within artificialintelligence hopes to build on such programsto create intelligent systems.There are two main obstacles to this approach.First, it is not easy to take informal knowledgeand state it in the formal terms required bylogical notation, particularly when theknowledge is less than 100% certain. Second,there is a big difference between being able tosolve a problem ``in principle and doing so inpractice. Even problems with just a few dozenfacts can exhaust the computational resourcesof any computer unless it has some guidanceas to which reasoning steps to try first.Although both of these obstacles applyto any attempt to build computationalreasoning systems, they appeared first in thelogicist tradition because the power of therepresentation and reasoning systems are well-defined and fairly well understood.Acting rationally: The rational agent approachActing rationally means acting so as to achieveones goals, given ones beliefs. An agent isjust something that perceives and acts. (Thismay be an unusual use of the word, but youwill get used to it.) In this approach, AI isviewed as the study and construction ofrational agents.In the ``laws of thought approach to AI, thewhole emphasis was on correct inferences.Making correct inferences issometimes part of being a rational agent,because one way to act rationally is to reasonlogically to the conclusion that a given actionwill achieve ones goals, and then to act on thatconclusion. On the other hand, correctinference is not all of rationality, because thereare often situations where there is no provablycorrect thing to do, yet something must still bedone. There are also ways of acting rationallythat cannot be reasonably said to involveinference. For example, pulling ones hand offof a hot stove is a reflex action that is moresuccessful than a slower action taken aftercareful deliberation.All the ``cognitive skills needed for theTuring Test are there to allow rational actions.Thus, we need the ability to representknowledge and reason with it because thisenables us to reach good decisions in a widevariety of situations. We need to be able togenerate comprehensible sentences in naturallanguage because saying those sentences helpsus get by in a complex society. We needlearning not just for erudition, but becausehaving a better idea of how the world worksenables us to generate more effective strategiesfor dealing with it. We need visual perceptionnot just because seeing is fun, but in order toget a better idea of what an action mightachieve--for example, being able to see a tastymorsel helps one to move toward it.The study of AI as rational agent designtherefore has two advantages. First, it is moregeneral than the ``laws of thought approach,because correct inference is only a usefulmechanism for achieving rationality, and not anecessary one. Second, it is more amenable toscientific development than approaches basedon human behavior or human thought, becausethe standard of rationality is clearly definedand completely general. Human behavior, onthe other hand, is well-adapted for one specificenvironment and is the product, in part, of acomplicated and largely unknown evolutionaryprocess that still may be far from achievingperfection. This book will thereforeconcentrate on general principles of rationalagents, and on components for constructingthem. We will see that despite the apparentsimplicity with which the problem can bestated, an enormous variety of issues come upwhen we try to solve it. Chapter 2 outlinessome of these issues in more detail. Oneimportant point to keep in mind: we will seebefore too long that achieving perfectrationality--always doing the right thing--is notpossible in complicated environments. The
  6. 6. computational demands are just too high.However, for most of the book, we will adoptthe working hypothesis that understandingperfect decision making is a good place tostart. It simplifies the problem and providesthe appropriate setting for most of thefoundational material in the field. Chapters 5and 17 deal explicitly with the issue of limitedrationality--acting appropriately when there isnot enough time to do all the computations onemight like.The ``History of AI sections from the bookare omitted from this online version.The State of the ArtInternational grandmaster Arnold Denkerstudies the pieces on the board in front of him.He realizes there is no hope; he must resignthe game. His opponent, Hitech, becomes thefirst computer program to defeat a grandmasterin a game of chess.``I want to go from Boston to San Francisco,the traveller says into the microphone. ``Whatdate will you be travelling on? is the reply.The traveller explains she wants to go October20th, nonstop, on the cheapest available fare,returning on Sunday. A speech understandingprogram named Pegasus handles the wholetransaction, which results in a confirmedreservation that saves the traveller $894 overthe regular coach fare. Even though the speechrecognizer gets one out of ten words wrong, itis able to recover from these errors because ofits understanding of how dialogs are puttogether.An analyst in the Mission Operations room ofthe Jet Propulsion Laboratory suddenly startspaying attention. A red message has flashedonto the screen indicating an ``anomaly withthe Voyager spacecraft, which is somewherein the vicinity of Neptune. Fortunately, theanalyst is able to correct the problem from theground. Operations personnel believe theproblem might have been overlooked had itnot been for Marvel, a real-time expert systemthat monitors the massive stream of datatransmitted by the spacecraft, handling routinetasks and alerting the analysts to more seriousproblems.Cruising the highway outside of Pittsburgh at acomfortable 55 mph, the man in the driversseat seems relaxed. He should be--for the past90 miles, he has not had to touch the steeringwheel. The real driver is a robotic system thatgathers input from video cameras, sonar, andlaser range finders attached to the van. Itcombines these inputs with experience learnedfrom training runs and succesfully computeshow to steer the vehicle.A leading expert on lymph-node pathologydescribes a fiendishly difficult case to theexpert system, and examines the systemsdiagnosis. He scoffs at the systems response.Only slightly worried, the creators of thesystem suggest he ask the computer for anexplanation of the diagnosis. The machinepoints out the major factors influencing itsdecision, and explains the subtle interaction ofseveral of the symptoms in this case. Theexpert admits his error, eventually.From a camera perched on a street light abovethe crossroads, the traffic monitor watches thescene. If any humans were awake to read themain screen, they would see ``Citroen 2CVturning from Place de la Concorde intoChamps Elysees, ``Large truck of unknownmake stopped on Place de la Concorde, andso on into the night. And occasionally, ``Majorincident on Place de la Concorde, speedingvan collided with motorcyclist, and anautomatic call to the emergency services.These are just a few examples of artificialintelligence systems that exist today. Notmagic or science fiction--but rather science,engineering, and mathematics, to which thisbook provides an introduction.SummaryThis chapter defines AI and establishes thecultural background against which it hasdeveloped. Some of the important points are asfollows:Different people think of AIdifferently. Two important questionsto ask are: Are you concerned with
  7. 7. thinking or behavior? Do you want tomodel humans, or work from an idealstandard?In this book, we adopt the view thatintelligence is concerned mainlywith rational action. Ideally,an intelligent agent takes the bestpossible action in a situation. We willstudy the problem of building agentsthat are intelligent in this sense.Philosophers (going back to 400 B.C.)made AI conceivable by consideringthe ideas that the mind is in someways like a machine, that it operateson knowledge encoded in someinternal language, and that thought canbe used to help arrive at the rightactions to take.Mathematicians provided the tools tomanipulate statements of logicalcertainty as well as uncertain,probabilistic statements. They also setthe groundwork for reasoning aboutalgorithms.Psychologists strengthened the ideathat humans and other animals can beconsidered information processingmachines. Linguists showed thatlanguage use fits into this model.Computer engineering provided theartifact that makes AI applicationspossible. AI programs tend to be large,and they could not work without thegreat advances in speed and memorythat the computer industry hasprovided.The history of AI has had cycles ofsuccess, misplaced optimism, andresulting cutbacks in enthusiasm andfunding. There have also been cyclesof introducing new creativeapproaches and systematically refiningthe best ones.Recent progress in understanding thetheoretical basis for intelligence hasgone hand in hand with improvementsin the capabilities of real systems.Refferences Linkshttp://www.aaai.org/http://www-formal.stanford.edu/http://insight.zdnet.co.uk/hardware/emergingtech/http://www.genetic-programming.com/