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Prathyusha Engineering College Lecture Notes PRATHYUSHA ENGINEERING COLLEGE LECTURE NOTES Course code : CS8691 Name of the course : ARTIFICIAL INTELLIGENCE Regulation : 2017 Course faculty : Dr.KAVITHA.V.R PRATHYUSHA ENGINEERING COLLEGE Department of Computer Science and Engineering VI Semester (EVEN 2019-20) CS8691ARTIFICIAL INTELLIGENCE OBJECTIVES: To understand the various characteristics of Intelligent agents To learnthe different search strategies in AI To learn to represent knowledge in solving AI problems To understand the different ways of designing software agents To know about the various applications of AI. UNIT IINTRODUCTION 09 Introduction–Definition -Future of Artificial Intelligence –Characteristics of Intelligent Agents–Typical Intelligent Agents –Problem Solving Approach to Typical AI problems. UNIT II PROBLEM SOLVING METHODS 9 Problem solving Methods -Search Strategies-Uninformed -Informed -Heuristics -Local Search Algorithms and Optimization Problems -Searching with Partial Observations -Constraint Satisfaction Problems –Constraint Propagation -Backtracking Search -Game Playing -Optimal Decisions in Games –Alpha -Beta Pruning - Stochastic Games UNIT IIIKNOWLEDGE REPRESENTATION 9 First Order Predicate Logic –Prolog Programming –Unification –Forward Chaining-Backward Chaining – Resolution –Knowledge Representation -Ontological Engineering-Categories and Objects –Events -Mental Events and Mental Objects -Reasoning Systems for Categories -Reasoning with Default Information UNIT IV SOFTWARE AGENTS 9 Architecture for Intelligent Agents –Agent communication –Negotiation and Bargaining –Argumentation among Agents –Trust and Reputation in Multi-agent systems. UNIT V APPLICATIONS 9 AI applications –Language Models –Information Retrieval-Information Extraction –Natural Language Processing -Machine Translation –Speech Recognition –Robot –Hardware –Perception –Planning –Moving TOTAL :45 PERIODS OUTCOMES: Upon completion of the course, the students will be able to: Use appropriate search algorithms for any AI problem Represent a problem using first order and predicate logic Provide the apt agent strategy to solve a given problem Design software agents to solve a problem Design applications for NLP that useArtificial Intelligence. TEXT BOOKS: 1.S. Russell and P. Norvig, "Artificial Intelligence: A Modern Approach, Prentice Hall, Third Edition, 2009. 2. Bratko, ―Prolog: Programming for Artificial Intelligence, Fourth edition, Addison-Wesley Educational Publishers Inc., 2011. REFERENCES: 1.M. Tim Jones, ―Artificial Intelligence: A Systems Approach(Computer Science), Jones and Bartlett Publishers, Inc.; First Edition, 2008 2.Nils J. Nilsson, ―The Quest for Artificial Intelligence, Cambridge University Press, 2009. 3.William F. Clocksin and Christopher S. Mellish, Programming in Prolog: Using the ISO Standard, Fifth Edition, Springer, 2003. 4.Gerhard Weiss, ―Multi Agent Systems, Second Edition, MIT Press, 2013. 5.David L. Poole and Alan K. Mackworth, ―Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010 UNIT I INTRODUCTION 9 Introduction–Definition -Future of Artificial Intelligence –Characteristics of Intelligent Agents–Typical Intelligent Agents –Problem Solving Approach to Typical AI problems. 1. INTRODUCTION Artificial intelligence is already all around us, from web search to video games. AI methods plan the driving directions, filter email spam, and focus the digital cameras on faces. AI lets us guide our phone with our voice and read foreign newspapers in English. Beyond today's applications, AI is at the core of many new technologies that will shape our future. From self-driving cars to household robots, advancements in AI help transform science fiction into real systems The foundations of Artificial Intelligence The various disciplines that contributed ideas, viewpoints, and techniques to AI are given below : Philosophy(428 B.C. – present) Aristotle (384-322 B.C.) was the first to formulate a precise set of laws governing the rational part of the mind. He developed an informal system of syllogisms for proper reasoning, which allowed one to generate conclusions mechanically, given initial premises. Computer Human Brain Computational units 1 CPU, 108 gates 1011 neurons Storage units 1010 bits RAM 1011 neurons 1011 bits disk 1014 synapses Cycle time 10-9 sec 10-3 sec Bandwidth 1010 bits/sec 1014 bits/sec Memory updates/sec 109 1014 Table 1.1 A crude comparison of the raw computational resources available to computers (circa 2003 ) and brain. The computer’s numbers have increased by at least by a factor of 10 every few years. The brain’s numbers have not changed for the last 10, 000 years. Brains and digital computers perform quite different tasks and have different properties. Table 1.1 shows that there are 10000 times more neurons in the typical human brain than there are gates in the CPU of a typical high- end computer. Moore’s Law predicts that the CPU’s gate count will equal the brain’s neuron count around 2020. Psycology(1879 – present) The origin of scientific psychology are traced back to the work of German physiologist Hermann von Helmholtz(1821-1894) and his student Wilhelm Wundt(1832 – 1920) In 1879, Wundt opened the first laboratory of experimental psychology at the University of Leipzig. In US, the development of computer modeling led to the creation of the field of cognitive science. The field can be said to have started at the workshop in September 1956 at MIT. Computer engineering (1940-present) For artificial intelligence to succeed, we need two things: intelligence and an artifact. The computer has been the artifact of choice. AI also owes a debt to the software side of computer science, which has supplied the operating systems, programming languages, and tools needed to write modern programs Control theory and Cybernetics (1948-present) Ktesibios of Alexandria (c. 250 B.C.) built the first self-controlling machine: a water clock with a regulator that kept the flow of water running through it at a constant, predictable pace. Modern control theory, especially the branch known as stochastic optimal control, has as its goal the design of systems that maximize an objective function over time. Linguistics (1957-present) Modem linguistics and AI, then, were "born" at about the same time, and grew up together, intersecting in a hybrid field called computational linguistics or natural language processing. 1.1.3 The History of Artificial Intelligence The gestation of artificial intelligence (1943-1955) There were a number of early examples of work that can be characterized as AI, but it was Alan Turing who first articulated a complete vision of AI in his 1950 article "Computing Machinery and Intelligence." Therein, he introduced the Turing test, machine learning, genetic algorithms, and reinforcement learning. The birth of artificial intelligence (1956) McCarthy convinced Minsky, Claude Shannon, and Nathaniel Rochester to help him bring together U.S. researchers interested in automata theory, neural nets, and the study of intelligence. They organized a two- month workshop at Dartmouth in the summer of 1956. Perhaps the longest-lasting thing to come out of the workshop was an agreement to adopt McCarthy's new name for the field: artificial intelligence. Early enthusiasm, great expectations (1952-1969) The early years of AI were full of successes-in a limited way. General Problem Solver (GPS) was a computer program created in 1957 by Herbert Simon and Allen Newell to build a universal problem solver machine. The order in which the program considered subgoals and possible actions was similar to that in which humans approached the same problems. Thus, GPS was probably the first program to embody the "thinking humanly" approach. At IBM, Nathaniel Rochester and his colleagues produced some of the first AI programs. Herbert Gelernter (1959) constructed the Geometry Theorem Prover, which was able to prove theorems that many students of mathematics would find quite tricky. Lisp was invented by John McCarthy in 1958 while he was at the Massachusetts Institute of Technology (MIT). In 1963, McCarthy started the AI lab at Stanford. Tom Evans's ANALOGY program (1968) solved geometric analogy problems that appear in IQ tests, such as the one in Figure 1.1 Figure 1.1 The Tom Evan’s ANALOGY program could solve geometric analogy problems as shown. A dose of reality (1966-1973) From the beginning, AI researchers were not shy about making predictions of their coming successes. The following statement by Herbert Simon in 1957 is often quoted: “It is not my aim to surprise or shock you-but the simplest way I can summarize is to say that there are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until-in a visible future-the range of problems they can handle will be coextensive with the range to which the human mind has been applied. Knowledge-based systems: The key to power? (1969-1979) Dendral was an influential pioneer project in artificial intelligence (AI) of the 1960s, and the computer software expert system that it produced. Its primary aim was to help organic chemists in identifying unknown organic molecules, by analyzing their mass spectra and using knowledge of chemistry. It was done at Stanford University by Edward Feigenbaum, Bruce Buchanan, Joshua Lederberg, and Carl Djerassi. AI becomes an industry (1980-present) In 1981, the Japanese announced the "Fifth Generation" project, a 10-year plan to build intelligent computers running
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