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344-571 ปัญญาประดิษฐ์ ( Artificial Intelligence) PowerPoint Presentation
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344-571 ปัญญาประดิษฐ์ ( Artificial Intelligence)

344-571 ปัญญาประดิษฐ์ ( Artificial Intelligence)

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344-571 ปัญญาประดิษฐ์ ( Artificial Intelligence)

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  1. 344-571 ปัญญาประดิษฐ์(Artificial Intelligence) ดร.วิภาดา เวทย์ประสิทธิ์ ภาควิชาวิทยาการคอมพิวเตอร์ คณะวิทยาศาสตร์ มหาวิทยาลัยสงขลานครินทร์ ห้องทำงาน : CS 320โทรศัพท์ : 074-288596 E-mail :wwettayaprasit@yahoo.com Website : http://www.cs.psu.ac.th/wiphada

  2. วัตถุประสงค์ 1. ให้นักศึกษามีความรู้ความเข้าใจเกี่ยวกับปัญญาประดิษฐ์และสาขาต่างๆของปัญญาประดิษฐ์ 2. ให้นักศึกษาสามารถพัฒนางานทางด้านปัญญาประดิษฐ์ได้ 3. ให้นักศึกษาสามารถค้นคว้าเพิ่มเติมด้วยตนเองได้ วิธีการเรียนการสอน :การบรรยาย การสัมมนา การศึกษาค้นคว้าด้วยตัวเอง การวัดผล : สอบกลางภาค 35% สอบปลายภาค 40% สัมมนา 10%Assignment 15% เวลาเรียน : จันทร์ 1 - 4 ห้อง CS203 ตำรา :Artificial Intelligence second edition, Elaine Rich and Kevin Knight, McGraw-Hill Inc., 1991. Chapter 1

  3. เนื้อหาวิชา Chapter 1 : What is Artificial Intelligence? Chapter 2 : Problems and Spaces Chapter 3 : Heuristic Search Chapter 4 : Natural Language Processing Chapter 5 : Machine Learning Chapter 6 : Robotics Chapter 7 : Neural Networks Chapter 8 : Expert Systems Chapter 9 : Computer Vision Chapter 1

  4. Chapter 1What is Artificial Intelligence?

  5. Content • Artificial IntelligenceArtificial Intelligence FieldsHeuristicTic Tac ToeTuring Test

  6. Artificial Intelligence artificial intelligence n. (Abbr. AI) The ability of a computer or other machine to perform those activities that are normally thought to require intelligence. The branch of computer science concerned with the development of machines having this ability. Chapter 1

  7. Artificial Intelligence • The subfield of computer science concerned with understanding the nature of intelligence and constructing computer systems capable of intelligent action. • It embodies the dual motives of furthering basic scientific understanding and making computers more sophisticated in the service of humanity. Chapter 1

  8. Artificial Intelligence • Many activities involve intelligent action —problem solving, perception, learning, planning and other symbolic reasoning, creativity, language, and so forth—and therein lie an immense diversity of phenomena. Chapter 1

  9. Artificial Intelligence • Computer Encyclopedia • (Artificial Intelligence) Devices and applications that exhibit human intelligence and behavior including robots, expert systems, voice recognition, natural and foreign language processing. It also implies the ability to learn and adapt through experience. Chapter 1

  10. Artificial Intelligence Wikipedia The term Artificial Intelligence (AI) was first used by John McCarthy who considers it to mean "the science and engineering of making intelligent machines".[1] It can also refer to intelligence as exhibited by an artificial (man-made, non-natural, manufactured) entity. Chapter 1

  11. Artificial Intelligence Wikipedia AI is studied in overlapping fields of computer science, psychology, neuroscience and engineering, dealing with intelligent behavior, learning and adaptation and usually developed using customized machines or computers. Chapter 1

  12. History of Artificial Intelligence Chapter 1

  13. History of Artificial Intelligence Chapter 1

  14. Artificial Intelligence Chapter 1

  15. AI Areas • Artificial Intelligence (AI) : • the branch o f computer science that is concerned with the automation of intelligent behavior. • AI Areas : • Game Playing • Automated Reasoning and Theorem Proving • Expert Systems • Natural Language Understanding and Semantics Modeling • Modeling Human Performance • Planning and Robotics • Machine Leaning • Neural Networks Chapter 1

  16. Task Domain of AI Mundane Tasks mundane(มันเดน) adj. ทางโลก Perception : Vision, Speech Natural language : Understanding, Generation, Translation Commonsense reasoning Robot control Formal Tasks Games: Chess Mathematics : Logic, Geometry Expert Tasks Engineering : Design, Fault finding, Manufacturing planning Scientific analysis Medical diagnosis Financial analysis Chapter 1

  17. Artificial Intelligence Fields

  18. Robotics • Shakey the Robot Developed in 1969 by the Stanford Research Institute, Shakey was the first fully mobile robot with artificial intelligence. Seven feet tall, Shakey was named after its rather unstable movements. (Image courtesy of The Computer History Museum, www.computerhistory.org) Chapter 1

  19. Robotics • A legged game from RoboCup 2004 in Lisbon, Portugal • Team ENSCO's entry in the first Grand Challenge, DAVID Chapter 1

  20. Robotics • The DARPA Grand Challenge is a race for a $2 million prize where cars drive themselves across several hundred miles of challenging desert terrain without any communication with humans, using GPS, computers and a sophisticated array of sensors. In 2005 the winning vehicles completed all 132 miles of the course in just under 7 hours. Chapter 1

  21. Robotics • ro·bot A mechanical device that sometimes resembles a human and is capable of performing a variety of often complex human tasks on command or by being programmed in advance. • A machine or device that operates automatically or by remote control. • A person who works mechanically without original thought, especially one who responds automatically to the commands of others. Chapter 1

  22. Robotics • Computer Encyclopedia • robot • A stand-alone hybrid computer system that performs physical and computational activities. Capable of performing many different tasks, it is a multiple-motion device with one or more arms and joints. • Robots can be similar in form to a human, but industrial robots do not resemble people at all. Chapter 1

  23. Robotics • Huey, Dewey and Louie • Named after Donald Duck's famous nephews, robots at this Wayne, Michigan plant apply sealant to prevent possible water leakage into the car. Huey (top) seals the drip rails while Dewey (right) seals the interior weld seams. Louie is outside of the view of this picture. (Image courtesy of Ford Motor Company.) Chapter 1

  24. Robotics • Inspect Pipes from the Inside • Developed by SRI for Osaka Gas in Japan, this Magnetically Attached General Purpose Inspection Engine (MAGPIE) goes inside gas pipes and looks for leaks. This unit served as the prototype for multicar models that perform temporary repairs while capturing pictures. (Image courtesy of SRI International.) Chapter 1

  25. Robotics • Computers Making Computers • Robots, whose brains are nothing but chips, are making chips in this TI fabrication plant. (Image courtesy of Texas Instruments, Inc.) Chapter 1

  26. Robotics • How Small Can They Get? • By 2020, scientists at Rutgers University believe that nano-sized robots will be injected into the bloodstream and administer a drug directly to an infected cell. This robot has a carbon nanotube body, a biomolecular motor that propels it and peptide limbs to orient itself. Chapter 1

  27. Robotics • ASIMO, • a humanoid robot manufactured by Honda. Chapter 1

  28. Three Laws of Robotics • A robot may not injure a human being or, through inaction, allow a human being to come to harm. • A robot must obey orders given it by human beings except where such orders would conflict with the First Law. • A robot must protect its own existence as long as such protection does not conflict with the First or Second Law. Chapter 1

  29. Computer Vision Chapter 1

  30. Computer Vision • Computer vision • The technology concerned with computational understanding and use of the information present in visual images. • In part, computer vision is analogous (similar) to the transformation of visual sensation into visual perception in biological vision. Chapter 1

  31. Computer Vision • For this reason the motivation, objectives, formulation, and methodology of computer vision frequently intersect with knowledge about their counterparts in biological vision. However, the goal of computer vision is primarily to enable engineering systems to model and manipulate the environment by using visual sensing. Chapter 1

  32. Computer Vision • Field of robotics in which programs attempt to identify objects represented in digitized images provided by video cameras, thus enabling robots to "see." • Much work has been done on stereo vision as an aid to object identification and location within a three-dimensional field of view. Recognition of objects in real time. Chapter 1

  33. Computer Vision Vision based biological species identification systems Chapter 1

  34. Computer Vision • Artist's Concept of Rover on Mars, • an example of an unmanned land-based vehicle. Notice the stereo cameras mounted on top of the Rover. (credit: Maas Digital LLC) Chapter 1

  35. Neural Network • neural network also neural net n. • A real or virtual device, modeled after the human brain, in which several interconnected elements process information simultaneously, adapting and learning from past patterns Chapter 1

  36. Neural Network • Computer Encyclopedia • neural network • A modeling technique based on the observed behavior of biological neurons and used to mimic (imitate) the performance of a system. Chapter 1

  37. Neural Network • It consists of a set of elements that start out connected in a random pattern, and, based upon operational feedback, are molded into the pattern required to generate the required results. • It is used in applications such as robotics, diagnosing, forecasting, image processing and pattern recognition. Chapter 1

  38. Neural Network Chapter 1

  39. Machine Learning Chapter 1

  40. Neural Network • Accounting Dictionary • Neural Networks • Technology in which computers actually try to learn from the data base and operator what the right answer is to a question. Chapter 1

  41. Neural Network • The system gets positive or negative response to output from the operator and stores that data so that it will make a better decision the next time. • While still in its infancy, this technology shows promise for use in accounting, fraud detection, economic forecasting, and risk appraisals. • The idea behind this software is to convert the order-taking computer into a "thinking" problem solver. Chapter 1

  42. Neural Network • Britannica Concise Encyclopedia • neural network • Type of parallel computation in which computing elements are modeled on the network of neurons that constitute animal nervous systems. • This model, intended to simulate the way the brain processes information, enables the computer to "learn" to a certain degree. Chapter 1

  43. Neural Network • A neural network typically consists of a number of interconnected processors, or nodes. Each handles a designated sphere of knowledge, and has several inputs and one output to the network. Based on the inputs it gets, a node can "learn" about the relationships between sets of data, sometimes using the principles of fuzzy logic. Chapter 1

  44. Neural Network • Neural networks have been used in pattern recognition, speech analysis, oil exploration, weather prediction, and the modeling of thinking and consciousness. Chapter 1

  45. Machine Learning • Sci-Tech Dictionary • machine learning (mə′shēn ′lərn·iŋ) • (computer science) The process or technique by which a device modifies its own behavior as the result of its past experience and performance. Chapter 1

  46. Machine Learning • Wikipedia • machine learning is concerned with the development of algorithms and techniques that allow computers to "learn". • At a general level, there are two types of learning: inductive, and deductive. Inductive machine learningmethods extract rules and patterns out of massive data sets. Chapter 1

  47. Machine Learning • inductive, • Logic. • The process of deriving general principles from particular facts or instances. • Mathematics. • A two-part method of proving a theorem involving an integral parameter. First the theorem is verified for the smallest admissible value of the integer.Then it is proven that if the theorem is true for any value of the integer, it is true for the next greater value.The final proof contains the two parts. Chapter 1

  48. Machine Learning • inductive, • reasoning from detailed facts to general principles • Rule induction is an area of machine learning in which formal rules are extracted from a set of observations. Chapter 1

  49. Machine Learning • deductive. Logic. • The process of reasoning in which a conclusion follows necessarily from the stated premises;inference by reasoningfrom the general to the specific. • reasoning from the general to the particular • Deduction is the process of drawing conclusions from premises Chapter 1

  50. Machine Learning • Deduction The process of reaching a conclusion through reasoning from general premises to a specific premise. • An example of deduction is present in the following syllogism: • Premise:All mammals are animals. • Premise:All whales are mammals. • Conclusion:Therefore, all whales are animals. Chapter 1