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I Sem Artificial Intelligence ARTIFICIAL INTELLIGENCE 4 YEAR SEM-1 BTECH MECHANICAL ENGINEERING (R18A1205) www.mrcet.ac.in UNIT-1 UNIT - I Introduction: AI problems, Agents and Environments, Structure of Agents, Problem Solving Agents Basic Search Strategies: Problem Spaces, Uninformed Search (Breadth-First, Depth-First Search, Depth-first with Iterative Deepening), Heuristic Search (Hill Climbing, Generic Best-First, A*), Constraint Satisfaction (Backtracking, Local Search) Introduction: • AI is the universal field of computer science. • It is one of the fascinating technology. • Greatest scope in future • It has a tendency to cause a machine to work as a human. • Artificial---------” man made” • Intelligence-------- the ability of making artificial thing----”thinking power” AI definition: • It is a branch of cs by which we can create an intelligent machine, which can behave like a human and think like a human and able to make decisions. • With AI, you do not need to pre-program a machine to do some work. • Inspite you can programm a machine, which can work with own intelligence • There are two ideas in the definition. Intelligence [Ability to understand, think & learn] Artificial device [Non Natural] What is AI • Artificial Intelligence (AI) is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion. • This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. • A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears Structure Programming Vs AI Structured Programming Artificial Intelligence A program without AI can answer AI answers any question belonging the "specific" questions it is meant to its "generic" type. to answer If you modify a structural program, AI programs are all about its entire structure changes. modifications. They keep absorbing the information provided to them as stimuli for future referencing like the human brain. Artificial intelligence can be viewed from a variety of perspectives. • From the perspective of intelligence artificial intelligence is making machines "intelligent" -- acting as we would expect people to act. • The inability to distinguish computer responses from human responses is called the Turing test. • Intelligence requires knowledge • Expert problem solving - restricting domain to allow including significant relevant knowledge • From a business perspective AI is a set of very powerful tools, and methodologies for using those tools to solve business problems. • From a programming perspective, AI includes the study of symbolic programming, problem solving, and search. • Typically AI programs focus on symbols rather than numeric processing. • Problem solving - achieve goals. • Search - seldom access a solution directly. Search may include a variety of techniques. Why AI: With the help of AI, • We can create such amazing software's ---- a device which can solve real-world problems accurately and easily. • We can create our personal virtual assistances. • We can build such robots which can work in a environment where survival of human can be at a risk . • AI opens path for new technologies, new devices and new opportunities. Goals: • Replicate human intelligence • Solve-knowledge intensive tasks • An intelligent connection of perception and action. • Building a machine which can perform tasks that requires human intelligence Providing theorem/algorithm Plan some surgical operation Playing chess Driving car in traffic • Creating some system which can exhibit intelligent behaviours. History of AI • 1943: Early Beginnings Boolean Circuit model of Brains • 1950: Turing Turing’s computing Machinery and Intelligence • 1956: Birth of AI Dartmouth Conference: Artificial Intelligence name Adopted • 1955-1965: Great Enthusiasm GPS Solver [General Problem Solver] History of AI • 1966: Reality Dawns Realization that many AI problems are intraceable • 1969-1985: Adding Domain Knowledge Development of knowledge based systems Success of rule based expert systems • 1986: Rise of Machine Learning Neural Networks return to popularity • 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 and data mining. Applications of AI • Gaming − AI plays important role for machine to think of large number of possible positions based on deep knowledge in strategic games. • Natural Language Processing − Interact with the computer that understands natural language spoken by humans • Expert Systems − Machine or software provide explanation and advice to the users. Applications of AI • Vision Systems − Systems understand, explain, and describe visual input on the computer • Speech Recognition − There are some AI based speech recognition systems have ability to hear and express as sentences and understand their meanings while a person talks to it. • Handwriting Recognition − The handwriting recognition software reads the text written on paper and recognize the shapes of the letters and convert it into editable text. • Intelligent Robots − Robots are able to perform the instructions given by a human. • Thinking Humanly: The Cognitive modeling Approach • We can say that given program thinks like a human, we must have some way of determining how humans think. i.e., we need to get inside the actual working of human minds. • There are 3 ways to do this; • Through Introspection Trying to catch our own Thoughts as they go by • Through psychological experiments Once we have a sufficiently precise theory of the mind,it becomes possible to express the theory as computer programs. If the programs input/output and timing behaviour matches human behaviour that is the evidence. • Brain Imaging Observing the brain in action • Acting Humanly: The Turing test Approach • The Turing Test, proposed by Alan Turing (195O), was designed to provide a satisfactory operational definition of intelligence. • Here the computer is asking some 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. Acting Humanly • The computer would need to possess the following capabilities: • Natural language processing: Enable it to communicate successfully in English. • Knowledge representation: Store what it knows or hears. • Automated reasoning: 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. • Computer vision: To perceive objects. • Robotics: To manipulate objects and move about. AI is composed of: • Reasoning: Set of process that enable us to provide logical thinking • Learning: It is an activity of gaining knowledge • Perception: It is a process of acquiring, interpreting and selecting and even organizing the sensor information. • Problem solving: It is the process of working through the details of a problem to reach the solution. • Linguistic intelligence: It is one’s ability to use comprehend speak and write the verbal and written languages. Importance of AI • Game Playing • Speech Recognition • Understanding Natural Language • Computer Vision(3D) • Expert Systems(medical) • Heuristic Classification(fraud detection) The applications of AI Consumer Marketing • Have you ever used any kind of credit/ATM/store card while shopping? • if so, you have very likely been “input” to an AI algorithm • All of this information is recorded digitally • Companies like Nielsen gather this information weekly and search for patterns • – general changes in consumer behavior • – tracking responses to new products • – identifying customer segments: targeted marketing, e.g., they find out that consumers with sports cars who buy textbooks respond well to offers of new credit cards. • Algorithms (“data mining”) search data for patterns based on mathematical theories of learning Applications of AI Identification Technologies • ID cards e.g., ATM cards • can be a nuisance and security risk: cards can be lost, stolen, passwords forgotten, etc • Biometric Identification, walk up to a locked door • – Camera • – Fingerprint device • – Microphone • – Computer uses biometric signature for identification • – Face, eyes, fingerprints, voice pattern • – This works by comparing data from person at door with stored library • – Learning algorithms can learn the matching process by analyzing a large library database off-line, can improve its performance. Applications of AI Intrusion Detection • Computer security • - we each have specific patterns of computer use times of day, lengths of sessions, command used, sequence of commands, etc • – would like to learn the “signature” of each authorized user • – can identify non-authorized users o How can the program automatically identify users? • – record user’s commands and time intervals • – characterize the patterns for each user • – model the variability in these patterns • – classify (online) any new user by similarity to stored patterns Applications of AI Machine Translation • Language problems in international business – e.g., at a meeting of Japanese, Korean, Vietnamese and Swedish investors, no common language • – If you are shipping your software manuals to 127 countries, the solution is ; hire translators to translate • – would
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