A PAPER PRESENTATION ON Artificial Intelligence

J.G.M.Jagagdeesh Kumar
Department of C.S.E. (III year)
Affiliated to JNTU K
DJR College of Engineering and Technology,
Gudavalli, Vijayawada
Krishna (dt.), Andhra Pradesh, India.
Contact details: J.G.M.Jagagdeesh Kumar Mobile number:9700234518 Email Id:jjagadeesh13@gmail.com

Introduction
In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
Humankind has given itself the scientific name homo sapiens--man the wise--because our mental capacities are so important to our everyday lives and our sense of self. The field of artificial intelligence, or AI, attempts to understand intelligent entities. Thus, one reason to study it is to learn more about ourselves. But unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities as well as understand them. Another reason to study AI is that these constructed intelligent entities are interesting and useful in their own right. AI has produced many significant and impressive products even at this early stage in its development. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization.
AI addresses one of the ultimate puzzles. How is it possible for a slow, tiny brain{brain}, whether biological or electronic, to perceive, understand, predict, and manipulate a world far larger and more complicated than itself? How do we go about making something with those properties? These are hard questions, but unlike the search for faster-than-light travel or an antigravity device, the researcher in AI has solid evidence that the quest is possible. All the researcher has to do is look in the mirror to see an example of an intelligent system.
AI is one of the newest disciplines. It was formally initiated in 1956, when the name was coined, although at that point work had been under way for about five years. Along with modern genetics, it is regularly cited as the ``field I would most like to be in'' by scientists in other disciplines. A student in physics might reasonably feel that all the good ideas have already been taken by Galileo, Newton, Einstein, and the rest, and that it takes many years of study before one can contribute new ideas. AI, on the other hand, still has openings for a full-time Einstein.
The study of intelligence is also one of the oldest disciplines. For over 2000 years, philosophers have tried to understand how seeing, learning, remembering, and reasoning could, or should, be done. The advent of usable computers in the early 1950s turned the learned but armchair speculation concerning these mental faculties into a real experimental and theoretical discipline. Many felt that the new ``Electronic Super-Brains'' had unlimited potential for intelligence. ``Faster Than Einstein'' was a typical headline. But as well as providing a vehicle for creating artificially intelligent entities, the computer provides a tool for testing theories of intelligence, and many theories failed to withstand the test--a case of ``out of the armchair, into the fire.'' AI has turned out to be more difficult than many at first imagined, and modern ideas are much richer, more subtle, and more interesting as a result.
AI currently encompasses a huge variety of subfields, from general-purpose areas such as perception and logical reasoning, to specific tasks such as playing chess, proving mathematical theorems, writing poetry{poetry}, and diagnosing diseases. Often, scientists in other fields move gradually into artificial intelligence, where they find the tools and vocabulary to systematize and automate the intellectual tasks on which they have been working all their lives. Similarly, workers in AI can choose to apply their methods to any area of human intellectual endeavor. In this sense, it is truly a universal field.
What is AI?
We have now explained why AI is exciting, but we have not said what it is. We could just say, ``Well, it has to do with smart programs, so let's get on and write some.'' But the history of science shows that it is helpful to aim at the right goals. Early alchemists, looking for a potion for eternal life and a method to turn lead into gold, were probably off on the wrong foot. Only when the aim changed, to that of finding explicit theories that gave accurate predictions of the terrestrial world, in the same way that early astronomy predicted the apparent motions of the stars and planets, could the scientific method emerge and productive science take place. Definitions of artificial intelligence according to eight recent textbooks are shown in the table below. These definitions vary along two main dimensions. The ones on top are concerned with thought processes and reasoning, whereas the ones on the bottom address behavior. Also, the definitions on the left measure success in terms of human performance, whereas the ones on the right measure against an ideal concept of intelligence, which we will callrationality. A system is rational if it does the right thing. ``The exciting new effort to make computers think ... machines with minds, in the full and literal sense'' (Haugeland, 1985)``The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning ...'' (Bellman, 1978) | ``The study of mental faculties through the use of computational models'' (Charniak and McDermott, 1985)``The study of the computations that make it possible to perceive, reason, and act'' (Winston, 1992) | ``The art of creating machines that perform functions that require intelligence when performed by people'' (Kurzweil, 1990)``The study of how to make computers do things at which, at the moment, people are better'' (Rich and Knight, 1991) | ``A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes'' (Schalkoff, 1990)``The branch of computer science that is concerned with the automation of intelligent behavior'' (Luger and Stubblefield, 1993) |
This gives us four possible goals to pursue in artificial intelligence: Systems that think like humans. | Systems that think rationally. | Systems that act like humans | Systems that act rationally |
Historically, all four approaches have been followed. As one might expect, a tension exists between approaches centered around humans and approaches centered around rationality. (We should point out that by distinguishing between human and rational behavior, we are not suggesting that humans are necessarily ``irrational'' in the sense of ``emotionally unstable'' or ``insane.'' One merely need note that we often make mistakes; we are not all chess grandmasters even though we may know all the rules of chess; and unfortunately, not everyone gets an A on the exam. Some systematic errors in human reasoning are cataloged by Kahneman et al..) A human-centered approach must be an empirical science, involving hypothesis and experimental confirmation. A rationalist approach involves a combination of mathematics and engineering. People in each group sometimes cast aspersions on work done in the other groups, but the truth is that each direction has yielded valuable insights. Let us look at each in more detail.
Acting humanly: The Turing Test approach
The Turing Test, proposed by Alan Turing (Turing, 1950), was designed to provide a satisfactory operational definition of intelligence. Turing defined intelligent behavior as the ability to achieve human-level performance in all cognitive tasks, sufficient to fool an interrogator. Roughly speaking, the test he proposed is that the computer should be interrogated by a human via a teletype, and passes the test if the interrogator cannot tell if there is a computer or a human at the other end. Chapter 26 discusses the details of the test, and whether or not a computer is really intelligent if it passes. For now, programming a computer to pass the test provides plenty to work on. The computer would need to possess the following capabilities: * natural language processing to enable it to communicate successfully in English (or some other human language); * knowledge representation to store information provided before or during the interrogation; * automated reasoning to use the stored information to answer questions and to draw new conclusions; * machine learning to adapt to new circumstances and to detect and extrapolate patterns.
Turing's test deliberately avoided direct physical interaction between the interrogator and the computer, because physical simulation of a person is unnecessary for intelligence. However, the so-called total Turing Testincludes a video signal so that the interrogator can test the subject's perceptual abilities, as well as the opportunity for the interrogator to pass physical objects ``through the hatch.'' To pass the total Turing Test, the computer will need * computer vision to perceive objects, and * robotics to move them about.
Within AI, there has not been a big effort to try to pass the Turing test. The issue of acting like a human comes up primarily when AI programs have to interact with people, as when an expert system explains how it came to its diagnosis, or a natural language processing system has a dialogue with a user. These programs must behave according to certain normal conventions of human interaction in order to make themselves understood. The underlying representation and reasoning in such a system may or may not be based on a human model.
Thinking humanly: The cognitive modelling approach
If we are going to say that a given program thinks like a human, we must have some way of determining how humans think. We need to get inside the actual workings of human minds. There are two ways to do this: through introspection--trying to catch our own thoughts as they go by--or through psychological experiments. Once we have a sufficiently precise theory of the mind, it becomes possible to express the theory as a computer program. If the program's input/output and timing behavior matches human behavior, that is evidence that some of the program's mechanisms may also be operating in humans. For example, Newell and Simon, who developed GPS, the ``General Problem Solver'' (Newell and Simon, 1961), were not content to have their program correctly solve problems. They were more concerned with comparing the trace of its reasoning steps to traces of human subjects solving the same problems. This is in contrast to other researchers of the same time (such as Wang (1960)), who were concerned with getting the right answers regardless of how humans might do it. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to try to construct precise and testable theories of the workings of the human mind. Although cognitive science is a fascinating field in itself, we are not going to be discussing it all that much in this book. We will occasionally comment on similarities or differences between AI techniques and human cognition. Real cognitive science, however, is necessarily based on experimental investigation of actual humans or animals, and we assume that the reader only has access to a computer for experimentation. We will simply note that AI and cognitive science continue to fertilize each other, especially in the areas of vision, natural language, and learning. The history of psychological theories of cognition is briefly covered on page 12.
Thinking rationally: The laws of thought approach
The Greek philosopher Aristotle was one of the first to attempt to codify ``right thinking,'' that is, irrefutable reasoning processes. His famous syllogisms provided patterns for argument structures that always gave correct conclusions given correct premises. For example, ``Socrates is a man; all men are mortal; therefore Socrates is mortal.'' These laws of thought were supposed to govern the operation of the mind, and initiated the field oflogic.
The development of formal logic in the late nineteenth and early twentieth centuries, which we describe in more detail in Chapter 6, provided a precise notation for statements about all kinds of things in the world and the relations between them. (Contrast this with ordinary arithmetic notation, which provides mainly for equality and inequality statements about numbers.) By 1965, programs existed that could, given enough time and memory, take a description of a problem in logical notation and find the solution to the problem, if one exists. (If there is no solution, the program might never stop looking for it.) The so-called logicist tradition within artificial intelligence hopes to build on such programs to create intelligent systems.
There are two main obstacles to this approach. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between being able to solve a problem ``in principle'' and doing so in practice. Even problems with just a few dozen facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first. Although both of these obstacles apply to any attempt to build computational reasoning systems, they appeared first in the logicist tradition because the power of the representation and reasoning systems are well-defined and fairly well understood.
Acting rationally: The rational agent approach
Acting rationally means acting so as to achieve one's goals, given one's beliefs. An agent is just something that perceives and acts. (This may be an unusual use of the word, but you will get used to it.) In this approach, AI is viewed as the study and construction of rational agents.
In the ``laws of thought'' approach to AI, the whole emphasis was on correct inferences. Making correct inferences is sometimes part of being a rational agent, because one way to act rationally is to reason logically to the conclusion that a given action will achieve one's goals, and then to act on that conclusion. On the other hand, correct inference is not all of rationality, because there are often situations where there is no provably correct thing to do, yet something must still be done. There are also ways of acting rationally that cannot be reasonably said to involve inference. For example, pulling one's hand off of a hot stove is a reflex action that is more successful than a slower action taken after careful deliberation.
All the ``cognitive skills'' needed for the Turing Test are there to allow rational actions. Thus, we need the ability to represent knowledge and reason with it because this enables us to reach good decisions in a wide variety of situations. We need to be able to generate comprehensible sentences in natural language because saying those sentences helps us get by in a complex society. We need learning not just for erudition, but because having a better idea of how the world works enables us to generate more effective strategies for dealing with it. We need visual perception not just because seeing is fun, but in order to get a better idea of what an action might achieve--for example, being able to see a tasty morsel helps one to move toward it.
The study of AI as rational agent design therefore has two advantages. First, it is more general than the ``laws of thought'' approach, because correct inference is only a useful mechanism for achieving rationality, and not a necessary one. Second, it is more amenable to scientific development than approaches based on human behavior or human thought, because the standard of rationality is clearly defined and completely general. Human behavior, on the other hand, is well-adapted for one specific environment and is the product, in part, of a complicated and largely unknown evolutionary process that still may be far from achieving perfection. This book will therefore concentrate on general principles of rational agents, and on components for constructing them. We will see that despite the apparent simplicity with which the problem can be stated, an enormous variety of issues come up when we try to solve it. Chapter 2 outlines some of these issues in more detail. One important point to keep in mind: we will see before too long that achieving perfect rationality--always doing the right thing--is not possible in complicated environments. The computational demands are just too high. However, for most of the book, we will adopt the working hypothesis that understanding perfect decision making is a good place to start. It simplifies the problem and provides the appropriate setting for most of the foundational material in the field. Chapters 5 and 17 deal explicitly with the issue of limited rationality--acting appropriately when there is not enough time to do all the computations one might like.

The ``History of AI'' sections from the book are omitted from this online version.

The State of the Art
International grandmaster Arnold Denker studies the pieces on the board in front of him. He realizes there is no hope; he must resign the game. His opponent, Hitech, becomes the first computer program to defeat a grandmaster in a game of chess.
``I want to go from Boston to San Francisco,'' the traveller says into the microphone. ``What date will you be travelling on?'' is the reply. The traveller explains she wants to go October 20th, nonstop, on the cheapest available fare, returning on Sunday. A speech understanding program named Pegasus handles the whole transaction, which results in a confirmed reservation that saves the traveller $894 over the regular coach fare. Even though the speech recognizer gets one out of ten words wrong, it is able to recover from these errors because of its understanding of how dialogs are put together.
An analyst in the Mission Operations room of the Jet Propulsion Laboratory suddenly starts paying attention. A red message has flashed onto the screen indicating an ``anomaly'' with the Voyager spacecraft, which is somewhere in the vicinity of Neptune. Fortunately, the analyst is able to correct the problem from the ground. Operations personnel believe the problem might have been overlooked had it not been for Marvel, a real-time expert system that monitors the massive stream of data transmitted by the spacecraft, handling routine tasks and alerting the analysts to more serious problems.
Cruising the highway outside of Pittsburgh at a comfortable 55 mph, the man in the driver's seat seems relaxed. He should be--for the past 90 miles, he has not had to touch the steering wheel. The real driver is a robotic system that gathers input from video cameras, sonar, and laser range finders attached to the van. It combines these inputs with experience learned from training runs and succesfully computes how to steer the vehicle.
A leading expert on lymph-node pathology describes a fiendishly difficult case to the expert system, and examines the system's diagnosis. He scoffs at the system's response. Only slightly worried, the creators of the system suggest he ask the computer for an explanation of the diagnosis. The machine points out the major factors influencing its decision, and explains the subtle interaction of several of the symptoms in this case. The expert admits his error, eventually.
From a camera perched on a street light above the crossroads, the traffic monitor watches the scene. If any humans were awake to read the main screen, they would see ``Citroen 2CV turning from Place de la Concorde into Champs Elysees,'' ``Large truck of unknown make stopped on Place de la Concorde,'' and so on into the night. And occasionally, ``Major incident on Place de la Concorde, speeding van collided with motorcyclist,'' and an automatic call to the emergency services.
These are just a few examples of artificial intelligence systems that exist today. Not magic or science fiction--but rather science, engineering, and mathematics, to which this book provides an introduction.
Summary
This chapter defines AI and establishes the cultural background against which it has developed. Some of the important points are as follows: * Different people think of AI differently. Two important questions to ask are: Are you concerned with thinking or behavior? Do you want to model humans, or work from an ideal standard? * In this book, we adopt the view that intelligence is concerned mainly with rational action. Ideally, an intelligent agent takes the best possible action in a situation. We will study the problem of building agents that are intelligent in this sense. * Philosophers (going back to 400 B.C.) made AI conceivable by considering the ideas that the mind is in some ways like a machine, that it operates on knowledge encoded in some internal language, and that thought can be used to help arrive at the right actions to take. * Mathematicians provided the tools to manipulate statements of logical certainty as well as uncertain, probabilistic statements. They also set the groundwork for reasoning about algorithms. * Psychologists strengthened the idea that humans and other animals can be considered information processing machines. Linguists showed that language use fits into this model. * Computer engineering provided the artifact that makes AI applications possible. AI programs tend to be large, and they could not work without the great advances in speed and memory that the computer industry has provided. * The history of AI has had cycles of success, misplaced optimism, and resulting cutbacks in enthusiasm and funding. There have also been cycles of introducing new creative approaches and systematically refining the best ones. * Recent progress in understanding the theoretical basis for intelligence has gone hand in hand with improvements in the capabilities of real systems.

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Refferences Links

http://www.aaai.org/

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

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

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