Artificial Intelligence :1 Artificial Intelligence CSC 4601


List of Books :2 List of Books Artificial Intelligence (A modern Approach), 2nd Edition, by Stuart Russell and Peter Norvig. Artificial Intelligence, 3rd Edition, Winston. Artificial Intelligence : Structures and Strategies for Complex Problem Solving, 5th Edition, George Luger. Artificial Intelligence, Elaine Rich and Kevin Knight.


Chapters (List of Contents) :3 Chapters (List of Contents) 1 Introduction 2 Problem Solving 3 Genetic Algorithms 4 Knowledge Representation and Reasoning 5 Expert Systems 6 Handling uncertainty with fuzzy systems 7 Introduction to learning 8 Planning 9 Advanced Topics 10 Conclusion


1.1 What is intelligence? :4 1.1 What is intelligence? The ability of problem solving demonstrates intelligence Example-1 Consider a mouse trying to search/reach the piece of cheese placed at right top corner of the image. 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 path. In other words the mouse can find more than one solutions to this problem. We can say that the mouse is intelligent enough to find a solution to the problem. Hence the ability of problem solving demonstrates intelligence.


Slide 5:5 1.1 What is intelligence?... Example-2 Biological example of Intelligence and AI… How insects apply Rosenblatt’s/Hebbian Learning ?


Slide 6:6 1.1 What is intelligence?... Biological example of Intelligence and AI… How insects apply Rosenblatt’s/Hebbian Learning ?


1.1 What is intelligence?... :7 1.1 What is intelligence?... The Capabilities like thinking, memory manipulation (storing, recalling), numerical processing, and decision making come in intelligence. Example-3: Find the next number in the sequence given below: 1, 4, 9, 16,… The next number is obviously 25 but is achieved through thinking, memory recalling, numerical processing, and decision making. 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.


1.1 What is intelligence… :8 1.1 What is intelligence… Example-4 A doctor checks a patient The doctor collects some knowledge about the patient by asking some questions and measuring temperature (T), Blood Pressure (BP), Pulse Rate (PR) etc. Then based on his previous knowledge he tries to diagnose the disease. His previous knowledge is based on rules like: “If the patient has a high BP and normal T and normal PR then he is not well”. Diagnosing a disease has many other complex information and observations involved, we have just mentioned a very simple case here. It is important to consider here that a doctor who would have a better memory to store all this precious knowledge, better ability of retrieving the correct portion of the knowledge for the correct patient, will be better able to classify a patient. Hence, telling us that good memory, good recall, and efficient memory and information manipulation also comes in intelligence.


1.1 What is intelligence… :9 Example-5: Ambiguous and fuzzy problems demonstrates intelligence Things are not so simple. Moreover, people don’t think in the same manner. Are you short, medium or tall? You might think that you are tall but your friend who is taller than you might say that NO! You are not. Some people might think that the people around 4ft are short, around 5ft are medium, and around 6ft are tall. Others might say that the people around 4.5ft are short, around 5.5ft are medium and around 6.5ft are tall. Even having the same measurements, different people can get completely different results because they approach the problem in different fashions. Things can be even more complex when the same person, having observed same measurements and solves the same problem in two different ways and reaches different solutions. We all know that we answer such fuzzy questions very efficiently in our daily lives. Our intelligence actually helps us to do this. Hence the ability to tackle ambiguous and fuzzy problems demonstrates intelligence. 1.1 What is intelligence…


1.2 Intelligent Machines :10 1.2 Intelligent Machines A machine is intelligent if It can find a path by searching through a mesh. It can solve problems like the next number in the sequence. It can develop plans/schedule like a time-table. It can diagnose and prescribe, like a doctor. It can answers ambiguous questions. It can recognizes fingerprints, faces, and optical character. It can understands. It can perceives knowledge. It can get trained. It can upgrade its previous learning. Preferably, It should be general purpose and multitasking having. It should have distributed and parallel architecture. In short, we wish it to behave like human! A machine having such properties is called an intelligent machine. But do you think this is the real natural intelligence?. Answer is No; Not at all. Instead this is Artificial intelligence.


1.3 Formal Definitions of AI :11 1.3 Formal Definitions of AI 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). The study of computation 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).


1.3 Formal Definitions of AI … :12 1.3 Formal Definitions of AI … Thinking Humanly To make computers think like human we need a way of determining how human think. For this we need to get inside the actual functioning of the human mind. There are two ways to do this: (i) through introspection - trying to catch out our own thoughts as they go by. And (ii) through psychological experiments: that concern with the activities of brain. Once we have a precise theory of mind, it becomes possible to express the theory as a computer program that follows 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.


1.3 Formal Definitions of AI … :13 1.3 Formal Definitions of AI … Acting Humanly The issue of acting like human comes up when AI programs 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 coming over a conveyer belt. Keeping in view all the above motivations, let us give a fairly comprehensive comment that Artificial Intelligence is a field which deals with the study and development of systems that can perceive, learn, think, analyze and act like a real human.


1.4 History and Evolution of AI :14 1.4 History and Evolution of AI AI is a young field. It has inherited its ideas, concepts and techniques from many disciplines like biology, psychology, philosophy, linguistics, mathematics, etc. From philosophy, we have theories of reasoning and learning . From 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 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.


1.4.1 First recognized work on AI :15 1.4.1 First recognized work on AI The first work that is now generally recognized as AI was done by Warren McCulloch and Walter Pitts (1943). They proposed a neuron model. They showed that the neuron is a bi-state element i.e. on or off and that the state of the neuron is the response of sufficient stimulation by a number of neighboring neurons. They claimed (without providing an evidence) that any logical task can be performed by suitably connecting a sufficient number of neurons. but they didn’t pursued this idea much at that time. Donald Hebb (1949) demonstrated a simple updating rule (training method) for modifying the connection strengths (weights) between neurons such that learning could take place.


1.4.2 The name of the field as Artificial Intelligence :16 1.4.2 The name of the field as Artificial Intelligence In 1956 some of the U.S researchers got together and organized a two-month 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 most lasting and memorable thing that came out of that workshop was an agreement to adopt the new name for the field: Artificial Intelligence. Over the next twenty years these people, their students and colleagues at MIT, CMU, Stanford and IBM, dominated the field of artificial intelligence.


1.4.3 First program that thought humanly :17 1.4.3 First program that thought humanly In the early years AI met drastic success. The researchers were highly motivated to try out AI techniques to solve problems that were not yet been solved. Many of them met great successes. Newell and Simon’s early success was followed up with the General Problem Solver. Unlike Logic Theorist, this program was developed in the manner that it attacked a problem imitating the steps that human take when solving a problem. Though it was catered for a limited class of problems but it was found out that it addressed those problems in a way very similar to that as human. It was probably the first program that imitated human thinking approach.


1.4.4 Development of Lisp :18 1.4.4 Development of Lisp In 1958 In MIT AI Lab, McCarthy defined the high-level language Lisp that became the dominant AI programming language in the proceeding years. Though McCarthy had the required tools with him to implement programs in this language but access to scarce and expensive computing resources were also a serious problem. Thus he and other researchers at MIT invented time sharing. Also in 1958 he published a paper titled Programs with Common Sense. This Program can be seen as the first complete AI system. Unlike the other systems at that time, it was to cater general knowledge of the world. For example, he showed that how some simple rules could help a program generate a plan to drive to an airport and catch the plane.


1.5 Applications :19 1.5 Applications Artificial finds its application in a lot of areas. A few of the applications will be mentioned here. 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 use intelligent software to make the user feel as if the machine on which that game is running were intelligent. Computer Vision is a new area where people are trying to develop the sense of visionary perception into a machine. 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.


1.5 Applications :20 1.5 Applications 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 many tasks. 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.


1.6 Summary :21 1.6 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 AI has enormous room for research and posses a diverse future