Introduction - Artificial Intelligence - Lecture Notes, Study notes of Artificial Intelligence

Download Introduction - Artificial Intelligence - Lecture Notes and more Study notes Artificial Intelligence in PDF only on Docsity! 1 Artificial Intelligence Handouts Lectures 01 - 03 1 Introduction This booklet is organized as chapters that elaborate on various concepts of Artificial Intelligence. The field itself is an emerging area of computer sciences and a lot of work is underway in order to mature the concepts of this field. In this booklet we will however try to envelop some important aspects and basic concepts which will help the reader to get an insight into the type of topics that Artificial Intelligence deals with. We have used the name of the field i.e. Artificial Intelligence (commonly referred as AI) without any explanation of the name itself. Let us now look into a simple but comprehensive way to define the field. To define AI, let us first try to understand that what is Intelligence? 2 What is Intelligence? If you were asked a simple question; how can we define Intelligence, many of you would exactly know what it is but most of you won’t exactly be able to define it. Is it something tangible? We all know that it does exist but what actually it is. Some of us will attribute intelligence to living beings and would be of the view that all living species are intelligent. But how about these plants and tress? They are living species but are they also intelligent? So can we say that Intelligence is a trait of some living species? Let us try to understand the phenomena of intelligence by using a few examples. Consider the following image where a mouse is trying to search a maze in order to find its way from the bottom left to the piece of cheese in the top right corner of the image. Docsity.com 2 This problem can be considered as a common real life problem which we deal with many times in our life, i.e. finding a path, may be to a university, to a friends house, to a market, or in this case to the piece of cheese. The mouse tries various paths as shown by arrows and can reach the cheese by more than one paths. In other words the mouse can find more than one solutions to this problem. The mouse was intelligent enough to find a solution to the problem at hand. Hence the ability of problem solving demonstrates intelligence. Let us consider another problem. Consider the sequence of numbers below: 1, 3, 7, 13, 21, ___ If you were asked to find the next number in the sequence what would be your answer? Just to help you out in the answer let us solve it for you “adding the next even number to the” i.e. if we add 2 to 1 we get 3, then we add 4 to 3 we get 7, then we get 6 to 7 we get 13, then we add 8 to 13 we get 21 and finally if we’ll add 10 to 21 we’ll get 31 as the answer. Again answering the question requires a little bit intelligence. The characteristic of intelligence comes in when we try to solve something, we check various ways to solve it, we check different combinations, and many other things to solve different problems. All this thinking, this memory manipulation capability, this numerical processing ability and a lot of other things add to ones intelligence. All of you have experienced your college life. It was very easy for us to look at the timetable and go to the respective classes to attend them. Never even caring that how that time table was actually developed. In simple cases developing such a timetable is simple. But in cases where we have 100s of students studying in different classes, where we have only a few rooms and limited time to schedule all those classes. This gets tougher and tougher. The person who makes the timetable has to look into all the time schedule, availability of the teachers, availability of the rooms, and many other things to fit all the items correctly within a fixed span of time. He has to look into many expressions and thoughts like “If room A is free AND teacher B is ready to take the class AND the students of the class are not studying any other course at that time” THEN “the class can be scheduled”. This is a fairly simple one, things get complex as we add more and more parameters e.g. if we were to consider that teacher B might teach more than one course and he might just prefer to teach in room C and many other things like that. The problem gets more and more complex. We am pretty much sure than none of us had ever realized the complexity through which our teachers go through while developing these schedules for our classes. However, like we know such time tables can be developed. All this information has to reside in the developer’s brain. His intelligence helps him to create such a schedule. Hence the ability to think, plan and schedule demonstrate intelligence. Docsity.com 5 We will have to call such a machine Intelligent. Is this real or natural intelligence? NO! This is Artificial Intelligence. 4 Formal Definitions for Artificial Intelligence In their book “Artificial Intelligence: A Modern Approach” Stuart Russell and Peter Norvig comment on artificial intelligence in a very comprehensive manner. They present the definitions of artificial intelligence according to eight recent textbooks. These definitions can be broadly categorized under two themes. The ones in the left column of the table below are concerned with thought process and reasoning, where as the ones in the right column address behavior. Systems that think like humans Systems that act like humans “The exciting new effort to make computers think … machines with minds, in the full and literal sense” (Haugeland, 1985) “The art of creating machines that perform functions that require intelligence when performed by people” (Kurzweil 1990) “[The automation of] activities that we associate with human thinking, activities such as decision making, problem solving, learning …” (Bellman, 1978) “The study of how to make computers do things at which, at the moment, people are better” (Rich and Knight, 1991) “The study of mental faculties through the use of computational models” (Charniak and McDermott) “A field of study that seeks to explain and emulate intelligent behavior in terms of computational processes” (Schalkoff, 1990) “The study of computation that make it possible to perceive reason and act” (Winston 1992) “The branch of computer science that is concerned with the automation of intelligent behavior” (Luger and Stubblefield, 1993) To make computers think like humans we first need to devise ways to determine that how humans think. This is not that easy. For this we need to get inside the actual functioning of the human brain. There are two ways to do this: Introspection: that is trying to catch out own thoughts as they go by. Psychological Experiments: that concern with the study of science of mental life. Docsity.com 6 Once we accomplish in developing some sort of comprehensive theory that how humans think, only then can we come up with computer programs that follow the same rules. 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 working of human mind. The issue of acting like humans comes up when AI programs have to interact with people or when they have to do something physically which human usually do in real life. For instance when a natural language processing system makes a dialog with a person, or when some intelligent software gives out a medical diagnosis, or when a robotic arm sorts out manufactured goods over a conveyer belt and many other such scenarios. Keeping in view all the above motivations let us give a fairly comprehensive comment that Artificial Intelligence is an effort to create systems that can learn, think, perceive, analyze and act in the same manner as real humans. People have also looked into understanding the phenomena of Artificial Intelligence from a different view point. They call this strong and weal AI. Strong AI means that machines act intelligently and they have real conscious minds. Weak AI says that machines can be made to act as if they are intelligent. That is Weak AI treats the brain as a black box and just emulates its functionality. While strong AI actually tries to recreate the functions of the inside of the brain as opposed to simply emulating behavior. The concept can be explained by an example. Consider you have a very intelligent machine that does a lot of tasks with a lot of intelligence. On the other hand you have a very trivial specie e.g. a cat. If you throw both of them into a pool of water, the cat will try to save her life and would swim out of the pool. The “intelligent” machine would die out in the water without any effort to save itself. The mouse had strong Intelligence, the machine didn’t. If the machine has strong artificial intelligence, it would have used its knowledge to counter for this totally new situation in its environment. But the machine only knew what we taught it or in other wards only knew what was programmed into it. It never had the inherent capability of intelligence which would have helped it to deal with this new situation. Most of the researchers are of the view that strong AI can’t actually ever be created and what ever we study and understand while dealing with the field of AI is related to weak AI. A few are also of the view that we can get to the essence of strong AI as well. However it is a standing debate but the purpose was to introduce you with another aspect of thinking about the field. Docsity.com 7 5 History and Evolution of Artificial Intelligence AI is a young field. It has inherited its ideas, concepts and techniques from many disciplines like philosophy, mathematics, psychology, linguistics, biology etc. From over a long period of traditions in philosophy theories of reasoning and learning have emerged. From over 400 years of mathematics we have formal theories of logic, probability, decision-making and computation. From psychology we have the tools and techniques to investigate the human mind and ways to represent the resulting theories. Linguistics provides us with the theories of structure and meaning of language. From biology we have information about the network structure of a human brain and all the theories on functionalities of different human organs. Finally from computer science we have tools and concepts to make AI a reality. 5.1 First recognized work on AI The first work that is now generally recognized as AI was done by Warren McCulloch and Walter Pitts (1943). Their work based on three sources: The basic physiology and function of neurons in the human brain The prepositional logic The Turing’s theory of computation The proposed an artificial model of the human neuron. Their model proposed a human neuron to be a bi-state element i.e. on or off and that the state of the neuron depending on response to stimulation by a sufficient number of neighboring neurons. They showed, for example, that some network of connected neurons could compute any computable function, and that all the logical connectives can be implemented by simple net structures. They also suggested that suitably connected networks can also learn but they didn’t pursue this idea much at that time. Donald Hebb (1949) demonstrated a simple updating rile for the modifying the connection strengths between neurons, such that learning could take place. 5.2 The name of the field as “Artificial Intelligence” In 1956 some of the U.S researchers got together and organized a two-months workshop at Dartmouth. There were altogether only 10 attendees. Allen Newell and Herbert Simon actually dominated the workshop. Although all the researchers had some excellent ideas and a few even had some demo programs like checkers, but Newell and Herbert already had a reasoning program, the Logic Theorist. The program came up with proofs for logic theorems. The Dartmouth workshop didn’t lead to any new breakthroughs, but it did all the major people who were working in the field to each other. Over the next twenty years these people, their students and colleagues at MIT, CMU, Stanford and IBM, dominated the field of artificial intelligence. The most lasting and memorable thing that came out of that workshop was an agreement to adopt the new name for the field: Artificial Intelligence. So this was when the term was actually coined. Docsity.com 10 5.7 AI becomes part of Commercial Market Even after realizing the basic hurdles and problems in the way of achieving success in this field, the researchers went on exploring grounds and techniques. The first successful commercial expert system, R1, began operation at Digital Equipment Corporation (McDermott, 1982). The program basically helped to configure the orders for new computer systems. Detailed study of what expert systems are will be dealt later in this book. For now consider expert systems as a programs that somehow solves a certain problem by using previously stored information about some rules and fact of the domain to which that problem belongs. In 1981, the Japanese announced the “Fifth Generation” project, a 10-year plan to build intelligent computers running Prolog in much the same way that ordinary computers run the machine code. The project proposed to achieve full-scale natural language understanding along with many other ambitious goals. However, by this time people began to invest in this field and many AI projects got commercially funded and accepted. 5.8 Neural networks reinvented Although computer science had rejected this concept of neural networks after Minsky and Papert’s Perceptrons book, but in 1980s at least four different groups reinvented the back propagation learning algorithm which was first found in 1969 by Bryson and Ho. The algorithm was applied to many learning problem in computer science and the wide spread dissemination of the results in the collection Parallel Distributed Processing (Rumelhart and McClelland, 1986) caused great excitement. People tried out the back propagation neural networks as a solution to many learning problems and met great success. The diagram above summarizes the history and evolution of AI in a comprehensive shape. Docsity.com 11 6 Applications Artificial finds its application is a lot of areas not only related to computer sciences but many other fields as well. We will briefly mention a few of the application areas and throughout the content of this booklet you will find various applications of the field in detail later. Many information retrieval systems like Google search engine uses artificially intelligent crawlers and content based searching techniques to efficiency and accuracy of the information retrieval. A lot of computer based games like chess, 3D combat games even many arcade games use intelligent software to make the user feel as if the machine on which that game is running is intelligent. Computer Vision is a new area where people are trying to develop the sense of visionary perception into a machine. Computer vision applications help to establish tasks which previously required human vision capabilities e.g. recognizing human faces, understanding images and to interpret them, analyzing medical scan and innumerable amount of other tasks. Natural language processing is another area which tries to make machines speak and interact with humans just like humans themselves. This requires a lot from the field of Artificial Intelligence. Expert systems form probably the largest industrial applications of AI. Software like MYCIN and XCON/R1 has been successfully employed in medical and manufacturing industries respectively. Robotics again forms a branch linked with the applications of AI where people are trying to develop robots which can be rather called as humanoids. Organizations have developed robots that act as pets, visitor guides etc. In short there are vast applications of the field and a lot of research work is going on around the globe in the sub-branches of the field. Like mentioned previously, during the course of the booklet you will find details of many application of AI. Docsity.com 12 7 Summary Intelligence can be understood as a trait of some living species Many factors and behaviors contribute to intelligence Intelligent machines can be created To create intelligent machines we first need to understand how the real brain functions Artificial intelligence deals with making machines think and act like humans It is difficult to give one precise definition of AI History of AI is marked by many interesting happenings through which the field gradually evolved In the early years people made optimistic claims about AI but soon they realized that it’s not all that smooth AI is employed in various different fields like gamming, business, law, medicine, engineering, robotics, computer vision and many other fields This book will guide you though basic concepts and some core algorithms that form the fundamentals of Artificial Intelligence AI has enormous room for research and posses a diverse future Docsity.com