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Introduction to Artificial Intelligence

Introduction to Artificial Intelligence. Filename: eie426-intro-0809.ppt . Contents. A typical definition of Artificial Intelligence (AI) The Turing test AI applications Topics Covered by AI Basic knowledge representation schemes Basic p roblem solving paradigms Brief history of AI

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Introduction to Artificial Intelligence

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  1. Introduction to Artificial Intelligence Filename: eie426-intro-0809.ppt EIE426-AICV

  2. Contents • A typical definition of Artificial Intelligence (AI) • The Turing test • AI applications • Topics Covered by AI • Basic knowledge representation schemes • Basic problem solving paradigms • Brief history of AI • The State of the Art EIE426-AICV

  3. The development of theories and techniques required to provide a computational engine the abilities to perceive, think and act, in an intelligent matter, in a complex environment. What is AI? EIE426-AICV

  4. Computer science and engineering psychology psychology What is AI? (cont.) • Engineering Goal: to solve real-world problems using AI ideas about representing knowledge, using knowledge, and assembling systems. • Scientific Goal: to determine which ideas about representing knowledge, using knowledge, and assembling systems explains various sorts of intelligence. Artificial intelligence EIE426-AICV

  5. The Turing Test • Computing machinery and intelligence, Turing 1950 • “Can machines think?”  “Can machines behave intelligently?” • Operational test for intelligent behavior: the Imitation Game EIE426-AICV

  6. The Turing Test (cont.) • The Turing test measures the performance of an allegedly intelligent machine against that of a human being. • Three important features of the test: • An objective notion of intelligence • Preventing us from being sidetracked by confusing and currently unanswerable questions • Eliminating any bias in favor of living organisms • The problem: Turing test is not reproducible, constructive, or amenable to mathematical analysis. EIE426-AICV

  7. Some Turing Test Questions • “What is the meaning of life?” • “Why is the sky blue?” • “How are you?” • “Is OJ guilty?” • “Which came first, the chicken or the egg?” • “What word rhymes with Orange?” • “What is love?” and “How does love feel?” • “Boxers or briefs?” • “Who is your best friend?” • “Do you prefer open minds or only open preferred minds?” • “Please respond only when I say 'Simon Says'.” • “Do you think that I should undergo an operation to remove my brain and install a computer in my head?” • “Tell me about your childhood.” • “How are you feeling today?” • “When were you born?” EIE426-AICV

  8. Perception Vision Speech Natural language Understanding Generation Translation Commonsense reasoning Robot control (http://www.cs.rochester.edu/u/jag/demos/demos.html) AI Applications Mundane Tasks demo EIE426-AICV

  9. AI Applications (cont.) Formal Tasks • Games • Chess • Backgammon • Checkers • Go • Mathematics • Geometry • Logic • Integral calculus • Proving properties of programs EIE426-AICV

  10. AI Applications (cont.) • Engineering • Design • Fault finding • Manufacturing planning • Scientific analysis • Medical diagnosis • Financial analysis Expert Tasks EIE426-AICV

  11. Example: Airport Resource Information System Arrange gates for flights considering the seat capacity, time to take off, connected flights, weather condition, emergent factors, etc. EIE426-AICV

  12. Topics Covered by AI • search and game-playing (√) • logical systems • knowledge (expert) systems (√) • planning systems (√) • uncertainty - probability and decision theory • machine learning (√) • computational intelligence - artificial neural networks (ANN’s) - fuzzy systems (FS’s), and - evolutionary algorithms (EA’s) (√) • natural language processing (NLP) (√) • perception (√) • robotics (√) • philosophical issues (√) √: to be taught EIE426-AICV

  13. Knowledge Representation Logic Search Machine Learning Planning Expert Systems NLP Vision Robotics Relationships among various topics EIE426-AICV

  14. Knowledge Representation A representation is a set of conventions about how to describe a class of things. A description makes use of the conventions of a representation to describe some particular thing. • Semantics nets (a subset: frames) √ • Predicate logic • Production rules √ Representation Techniques: EIE426-AICV

  15. Example: The Farmer, Fox, Goose, and Grain A farmer wants to move himself, a silver fox, a fat goose, and some tasty grain across a river. Unfortunately, his boat is so tiny he can take only one of his possessions across on any trip. Worse yet, an unattended fox will eat a goose, and an unattended goose will eat grain, so the farmer must not leave the fox alone with the goose or the goose alone with the grain. How can he do? EIE426-AICV

  16. Example: The Farmer, Fox, Goose, and Grain (cont.) EIE426-AICV

  17. Powerful Idea: • A lexical part - vocabulary • A structural part - constraints • A procedural part - access procedures • A semantic part - meaning Once a problem is described using an appropriate representation, the problem is almost solved. A Representation has Four Fundamental Parts: EIE426-AICV

  18. A semantic net is a representation In which Lexically, there are nodes, links, and application-specific link labels. Structurally, each link connects a tail node to a head node. Semantically, the nodes and links denote application-specific entities. With constructors that Construct a node Construct a link, given a link label and two nodes to be connected With readers that Produce a list of all links departing from a given node Produce a list of all links arriving at a given node Produce a tail node, given a link Produce a head node, given a link Produce a link label, given a link Semantic Nets EIE426-AICV

  19. Semantic Nets – Different Froms To be taught EIE426-AICV

  20. Problem Solving Paradigms • Describe and match √ • Generate and test √ • Means-ends analysis √ • Problem-reduction √ • Search √ • Rule-based systems √ • Predicate calculus EIE426-AICV

  21. The Describe-And-Match Method To identify an object using describe and match, • Describe the object using a suitable representation. • Match the object description against library descriptions until there is a satisfactory match or there are no more library descriptions. • If you find a satisfactory match, announce it; otherwise, announce failure. EIE426-AICV

  22. Feature-Based Object Identification EIE426-AICV

  23. Generate-And-Test EIE426-AICV

  24. Generate-And-Test (cont.) EIE426-AICV

  25. The Generate-And-Test Method To perform generate and test, • Until a satisfactory solution is found or no more candidate solutions can be generated, • Generate a candidate solution. • Test the candidate solution. • If an acceptable solution is found, announce it; otherwise, announce failure. Three properties for a good generator: complete, nonredundant, informed. EIE426-AICV

  26. The Means-Ends Analysis Method A state space is a representation That is a semantic net in which • The nodes denote states. • The links denote transitions between states. EIE426-AICV

  27. Current state EIE426-AICV

  28. Algorithm: Means-Ends Analysis(Simple version) To perform means-ends analysis, • Until the goal is reached or no more procedures are available, • Describe the current state, the goal state, and the difference between the two. • Use the difference between the current state and goal state, possibly with the description of the current state or goal state, to select a promising procedure. • Use the promising procedure and update the current state. • If the goal is reached, announce success; otherwise, announce failure. Key idea is to reduce difference between the current state and the goal state. EIE426-AICV

  29. The Task: A robot moves a desk with two things on it from one room to another. EIE426-AICV

  30. small(obj) A Robot’s Operators EIE426-AICV

  31. A Difference Table Operators differences EIE426-AICV

  32. The Progress of the Means-Ends Analysis Method EIE426-AICV

  33. Algorithm: Means-Ends Analysis(Advanced Version) • Compare CURRENT to GOAL. If there are no differences between them then return. • Otherwise, select the most important difference and reduce it by doing the following until success or failure is signaled: (a) Select an as yet untried operator O that is applicable to the current difference. If there are no such operators, then signal failure. (b)Attempt to apply O to CURRENT. Generate descriptions of two states: O-START, a state which O’s preconditions are satisfied and O-RESULT, the state that would result if O were applied in O-START. (c)If (FIRST-PART MEA(CURRENT, O-START)) and (LAST-PART MEA(O-RESULT, GOAL)) are successful, then signal success and return the result of concatenating FIRST-PART, O, and LAST-PART. EIE426-AICV

  34. The Problem-Reduction (Goal Reduction) Method EIE426-AICV

  35. A Decomposable Problem EIE426-AICV

  36. PolyU Bejing U PolyU HK Airport Means-ends analysis HK Airport Bejing Airport Problem reduction Walk Taxi Train Bejing Airport Bejing U Problem-Solving Methods Often Work Together EIE426-AICV

  37. AI Prehistory Philosophy logic, methods of reasoning, mind as physical system, foundations of learning, language, rationality Mathematics formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability Psychology adaptation, phenomena of perception and motor control, experimental techniques Linguistics knowledge representation, grammar Neuroscience physical substrate for mental activity Control theory stability, simple optimal agent designs EIE426-AICV

  38. History of AI Timeline of major AI events EIE426-AICV

  39. The State of the Art • Autonomous planning and scheduling: NASA’s Remote Agent • Game playing: IBM’s Deep Blue • Autonomous control: the ALVINN computer vision system, to steer a car to keep it following a lane (2850 miles, 98% of the time) • Medical diagnosis • Logistics planning: DRAT (Dynamic Analysis and Replanning Tool) • Robotics: HipNav (a system that uses computer vision techniques to create a 3-D model of a patient’s internal anatomy and then uses robotic control to guide the insertion of a hip replacement prosthesis. • Language understanding and problem solving: PROVERB (a computer program that solves crossword puzzles better than most humans) EIE426-AICV

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