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Transcript
MSE614 – Intelligent Manufacturing Spring 2008 – Ileana Costea, Ph.D. Session #1 - Lecture Introduction to course Division in groups (4-5 students per group) Introduction to Artificial Intelligence (AI) o Lecture (no transparencies, lecture written on the b oard; some material from the lecture is given below; the rest was taken by students as notes in class) o Class Exercises Introduction to Artificial Intelligence (AI) (Read and study Preface and Chapter 1 in textbook Luger) What is AI, what are Expert Systems (ES), examples of ES and AI applications What is intelligence (natural vs. artificial) Natural intelligence does have several advantages over AI: Natural intelligence is creative, whereas AI is rather uninspired. The ability to acquire knowledge is inherent in human beings, but with AI, tailored knowledge must be built into a carefully constructed system. Natural intelligence enables people to benefit from and use sensory experience directly, whereas most AI systems must work with symbolic inp ut. Perhaps most important, human reasoning is able to make use at all times of a wide context of experience and bring that to bear on individual problems; in contrast, AI systems typically gain their power by having a very narrow focus. The advantages of natural intelligence over AI result in the many limitations of expert systems, and will be discussed later in the course. Computers can be used to collect information about objects, events, or processes; and, of course, computers can process large amounts of information more efficiently than people can. People, however, instinctively do some things that have been very difficult to program into a computer: they recognize relationships between things; they sense qualities; and they spot patterns that explain how various items relate to each other. Newspaper photographs are nothing more than collections of minute dots, yet without any conscious effort, people discover the patterns that reveal faces and other objects in those photos. Similarly, one of the ways that humans make sense of the world is by recognizing the relationships and patterns that help give meaning to the objects and events that they encounter. Some major differences between expert systems and other systems: AI mainly symbolic processing non-algorithmic emphasis heuristics are a key element emphasis is on knowledge Conventional Processing numeric processing algorithmic heuristics are seldom used emphasis is on models and data AI can be documented. Decisions made by a computer can b e easily documented by tracing the activities of the system. Natural intelligence is P( difficult to reproduce; for example , a person may reach a conclusion but at some later date may be unable to re-create the reasoning process that led to that conclusion or to even recall the assumptions that were a part of the decision. If computers are to become more intelligent, they must be able to make the same kinds of associations among the qualities of objects, events, and processes that come so naturally to people. K n ow le d ge in A I - Definitions In the field of information systems it is c ustomary to distinguish between Data T he t er m d a t a r e f e r s to n u mer ic (o r alp h a n u me ric) s tri n g s t ha t b y themselves do not have a mean ing. They can be facts or figures to be pro cessed. Information Information is data organized so that it is meaningful to the person receiving it. Knowledge (K) has several definitions. According to the Webster’s New World Dictionary of the American language, knowledge is: A clear and certain perception of something Understanding Learning All that has been perceived or grasped by the mind Practical experience, skill Acquaintance or familiarity Cognizance; recognition Organized information applicable to problem solving Knowledge is also information that has been organized and analyzed to make it understandable and applicable to problem solving or decision making. The collection of knowledge related to a problem (or an opportunity) to be used in an AI system is called a knowledge base (KB). Most knowledge bases are limited in that they typically focus on some specific subject or domain. Once a knowledge base is built, AI techniques are used to give the computer inference capability. The computer will then be able to make inferences and judgments based on facts and relationships contained in the KB. Key Terms Algorithm Artificial intelligence Common Sense Conventional Programming Data Expert system Inferencing Information Information overload Intelligent-computer-aided instruction Intuition Knowledge (K) Knowledge Base (KB) Machine learning Natural language Natural language processing Numeric processing Pattern matching Programming language Robotics Search Sensory system Speech recognition Speech understanding State Space Representation Symbol Symbol structure Symbolic processing Turing Test Visual recognition Vision Systems