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Note for Artificial Intelligence - AI by B.M Brinda

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UNIT I INTRODUCTION TO Al AND PRODUCTION SYSTEMS CHAPTER - 1 What is Artificial Intelligence? 1. INTELLIGENCE  The capacity to learn and solve problems.  In particular,  the ability to solve novel problems (i.e solve new problems)  the ability to act rationally (i.e act based on reason)  the ability to act like humans 1.1 What is involved in intelligence? • Ability to interact with the real world – to perceive, understand, and act – e.g., speech recognition and understanding and synthesis – e.g., image understanding – e.g., ability to take actions, have an effect • Reasoning and Planning – modeling the external world, given input – solving new problems, planning, and making decisions – ability to deal with unexpected problems, uncertainties • Learning and Adaptation – we are continuously learning and adapting – our internal models are always being ―updated‖ • e.g., a baby learning to categorize and recognize animals 2. ARTIFICIAL INTELLIGENCE It is the study of how to make computers do things at which, at the moment, people are better. The term AI is defined by each author in own ways which falls into 4 categories 1. 2. 3. 4. The system that think like humans. System that act like humans. Systems that think rationally. Systems that act rationally. CS6659 – ARTIFICIAL INTELLIGENCE 1

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2.1 SOME DEFINITIONS OF AI  Building systems that think like humans ―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, … such as decision-making, problem solving, learning, …‖ -- Bellman, 1978  Building systems that act like humans ―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  Building systems that think rationally ―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  Building systems that act rationally ―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 2.1.1. Acting Humanly: The Turing Test Approach  Test proposed by Alan Turing in 1950  The computer is asked questions by a human interrogator. The computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or not. Programming a computer to pass, the computer need to possess the following capabilities:  Natural language processing to enable it to communicate successfully in English.  Knowledge representation to store what it knows or hears  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. To pass the complete Turing Test, the computer will need  Computer vision to perceive the objects, and  Robotics to manipulate objects and move about. CS6659 – ARTIFICIAL INTELLIGENCE 2

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2.1.2 Thinking humanly: The cognitive modeling approach We need to get inside actual working of the human mind: (a) Through introspection – trying to capture our own thoughts as they go by; (b) Through psychological experiments Allen Newell and Herbert Simon, who developed GPS, the ―General Problem Solver‖ tried to trace the reasoning steps to traces of human subjects solving the same problems. 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 2.1.3 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 (ie. Impossible to deny) reasoning processes. His syllogism provided patterns for argument structures that always yielded correct conclusions when 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; their study initiated a field called logic. 2.1.4 Acting rationally : The rational agent approach An agent is something that acts. Computer agents are not mere programs, but they are expected to have the following attributes also: (a) operating under autonomous control, (b) perceiving their environment, (c) persisting over a prolonged time period, (e) adapting to change. A rational agent is one that acts so as to achieve the best outcome. 3. HISTORY OF AI • 1943: early beginnings – McCulloch & Pitts: Boolean circuit model of brain • 1950: Turing – Turing's "Computing Machinery and Intelligence― • 1956: birth of AI – Dartmouth meeting: "Artificial Intelligence―name adopted • 1950s: initial promise – Early AI programs, including – Samuel's checkers program – Newell & Simon's Logic Theorist CS6659 – ARTIFICIAL INTELLIGENCE 3

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• 1955-65: ―great enthusiasm‖ – Newell and Simon: GPS, general problem solver – Gelertner: Geometry Theorem Prover – McCarthy: invention of LISP • 1966—73: Reality dawns – Realization that many AI problems are intractable – Limitations of existing neural network methods identified • Neural network research almost disappears • 1969—85: Adding domain knowledge – Development of knowledge-based systems – Success of rule-based expert systems, • E.g., DENDRAL, MYCIN • But were brittle and did not scale well in practice • 1986-- Rise of machine learning – Neural networks return to popularity – Major advances in machine learning algorithms and applications • 1990-- Role of uncertainty – Bayesian networks as a knowledge representation framework • 1995--AI as Science – Integration of learning, reasoning, knowledge representation – AI methods used in vision, language, data mining, etc 3.1 AI Technique AI technique is a method that exploits knowledge that should be represented in such a way that: • The knowledge captures generalizations. In other words, it is not necessary to represent separately each individual situation. Instead, situations that share important properties are grouped together. If knowledge does not have this property, inordinate amounts of memory and updating will be required. So we usually call something without this property "data" rather than knowledge. • It can be understood by people who must provide it. Although for many programs, the bulk of the data can be acquired automatically (for example, by taking readings from a variety of CS6659 – ARTIFICIAL INTELLIGENCE 4

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