Chapter 13 - PowerPoint PPT Presentation

chapter 13 n.
Skip this Video
Loading SlideShow in 5 Seconds..
Chapter 13 PowerPoint Presentation
play fullscreen
1 / 70
Chapter 13
Download Presentation
Download Presentation

Chapter 13

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript


  2. Learning Objectives • Understand machine-learning concepts • Learn the concepts and applications of case-based systems • Understand the concepts and applications of genetic algorithms • Understand fuzzy set theories and their applications in designing intelligent systems

  3. Learning Objectives • Understand the concepts and applications of natural language processing (NLP) • Learn the concepts, advantages, and limitations of voice technologies • Learn about integrated intelligent support systems

  4. Machine-Learning Techniques • Machine-learning concepts and definitions • Machine learning The process by which a computer learns from experience (e.g., using programs that can learn from historical cases)

  5. Machine-Learning Techniques • Human learning is a combination of many complicated cognitive processes including: • Induction • Deduction • Analogy • Other special procedures related to observing or analyzing examples

  6. Machine-Learning Techniques • How learning relates to intelligent systems • Learning systems demonstrate interesting learning behaviors • AI is not able to learn as well as humans or in the same way that humans • Machine learning cannot be applied in a creative way, although such systems can handle cases to which they have never been exposed • It is not clear why learning systems succeed or fail • A common thread running through most AI approaches to learning is the manipulation of symbols rather than numeric information

  7. Machine-Learning Techniques • Machine-learning methods • Supervised learning A method of training artificial neural networks in which sample cases are shown to the network as input and the weights are adjusted to minimize the error in its outputs • Unsupervised learning A method of training artificial neural networks in which only input stimuli are shown to the network, which is self-organizing

  8. Machine-Learning Techniques

  9. Machine-Learning Techniques Machine-learning methods and algorithms • Inductive learning • Case-based reasoning • Neural computing • Genetic algorithms • Natural language processing (NLP) • Cluster analysis • Statistical methods • Explanation-based learning A machine learning approach that assumes that there is enough existing theory to rationalize why one instance is or is not a prototypical member of a class

  10. Case-Based Reasoning (CBR) • Case-based reasoning (CBR) A methodology in which knowledge and/or inferences are derived from historical cases

  11. Case-Based Reasoning (CBR) • Analogical reasoning Determining the outcome of a problem with the use of analogies. A procedure for drawing conclusions about a problem by using past experience • Inductive learning A machine learning approach in which rules are inferred from facts or data

  12. Case-Based Reasoning (CBR) • The basic idea and process of CBR • Four-step process • Retrieve • Reuse • Revise • Retain

  13. Case-Based Reasoning (CBR) • Definition and concepts of cases in CBR • Ossified cases Cases that have been analyzed and have no further value • Paradigmatic cases A case that is unique that can be maintained to derive new knowledge for the future

  14. Case-Based Reasoning (CBR) • Definition and concepts of cases in CBR • Stories Cases with rich information and episodes. Lessons may be derived from this kind of cases in a case base

  15. Case-Based Reasoning (CBR)

  16. Case-Based Reasoning (CBR) • Benefits and usability of CBR • CBR makes learning much easier and the recommendation more sensible

  17. Case-Based Reasoning (CBR) • Advantages of using CBR • Knowledge acquisition is improved. • System development time is faster • Existing data and knowledge are leveraged • Complete formalized domain knowledge is not required • Experts feel better discussing concrete cases • Explanation becomes easier • Acquisition of new cases is easy • Learning can occur from both successes and failures

  18. Case-Based Reasoning (CBR)

  19. Case-Based Reasoning (CBR) • Uses, issues, and applications of CBR • Applications • CBR in electronic commerce • WWW and information search • Planning and control • Design • Reuse • Diagnosis • Reasoning

  20. Case-Based Reasoning (CBR) • Uses, issues, and applications of CBR • Implementation issues for designers • What makes up a case? How can we represent case memory? • Automatic case-adaptation rules can be very complex • How is memory organized? What are the indexing rules? • The quality of the results is heavily dependent on the indexes used

  21. Case-Based Reasoning (CBR) • Implementation issues for designers • How does memory function in relevant information retrieval? • How can we perform efficient searching (i.e., knowledge navigation) of the cases? • How can we organize the cases? • How can we design the distributed storage of cases? • How can we adapt old solutions to new problems? Can we simply adapt the memory for efficient querying, depending on context? What are the similarity metrics and the modification rules?

  22. Case-Based Reasoning (CBR) • Implementation issues for designers • How can we factor errors out of the original cases? • How can we learn from mistakes? That is, how can we repair and update the case base? • The case base may need to be expanded as the domain model evolves, yet much analysis of the domain may be postponed. • How can we integrate CBR with other knowledge representations and inferencing mechanisms? • Are there better pattern-matching methods than the ones we currently use? • Are there alternative retrieval systems that match the CBR schema?

  23. Case-Based Reasoning (CBR) • Success factors for CBR systems • Determine specific business objectives • Understand your end users and customers • Design the system appropriately • Plan an ongoing knowledge-management process • Establish achievable returns on investment (ROI) and measurable metrics • Plan and execute a customer-access strategy • Expand knowledge generation and access across the enterprise

  24. Genetic Algorithm Fundamentals • Genetic algorithms (GAs) Software programs that learn in an evolutionary manner similar to the way biological systems evolve

  25. Genetic Algorithm Fundamentals • Genetic algorithm process and terminology • Chromosome A candidate solution for a genetic algorithm • Reproduction The creation of new generations of improved solutions with the use of a genetic algorithm

  26. Genetic Algorithm Fundamentals • Genetic algorithm process and terminology • Crossover The combining of parts of two superior solutions by a genetic algorithm in an attempt to produce an even better solution • Mutation A genetic operator that causes a random change in a potential solution

  27. Genetic Algorithm Fundamentals

  28. Genetic Algorithm Fundamentals

  29. Genetic Algorithm Fundamentals • A few parameters must be set for the genetic algorithm • Number of initial solutions to generate • Number of offspring to generate • Number of parents and offspring to keep for the next generation • Mutation probability (very low) • Probability distribution of crossover point occurrence

  30. Genetic Algorithm Fundamentals • Limitations of genetic algorithms • Not all problems can be framed in the mathematical manner that genetic algorithms demand • Development of a genetic algorithm and interpretation of the results requires an expert who has both the programming and statistical/mathematical skills demanded by the genetic algorithm technology in use • In some situations, the “genes” from a few comparatively highly fit (but not optimal) individuals may come to dominate the population, causing it to converge on a local maximum

  31. Genetic Algorithm Fundamentals • Limitations of genetic algorithms • Most genetic algorithms rely on random number generators that produce different results each time the model runs • Locating good variables that work for a particular problem is difficult • Selecting methods by which to evolve the system requires thought and evaluation

  32. Developing Genetic Algorithm Applications • GAs are a type of machine learning for representing and solving complex problems

  33. Developing Genetic Algorithm Applications • Dynamic process control • Induction of optimization of rules • Discovery of new connectivity topologies (e.g., neural computing connections, i.e., neural network design) • Simulation of biological models of behavior and evolution • Complex design of engineering structures • Pattern recognition • Scheduling • Transportation and routing • Layout and circuit design • Telecommunication • Graph-based problems Applications of GAs include:

  34. Fuzzy Logic Fundamentals • Fuzzy logic Logically consistent ways of reasoning that can cope with uncertain or partial information; characteristic of human thinking and many expert systems. • Fuzzy sets A set theory approach in which set membership is less precise than having objects strictly in or out of the set

  35. Fuzzy Logic Fundamentals • Fuzzy Set for a Tall Person • If we survey people to define the minimum height a person must attain before being a tall man, the answer could be ranged from 5 to 7 feet(1 foot is about 30cm, 1 inch is about 2.54cm). The distribution of answers might look like this:

  36. Fuzzy Logic Fundamentals • Fuzzy Set for a Tall Person • Suppose Jack’s height is 6 feet. From probability theory, we can use the cumulative probability distribution and say there is a 75% chance that Jack is tall. • In fuzzy logic, we say that Jack’s degree of membership in the set of tall people is 0.75. • The difference: in probability term, Jack is perceived as either tall or not tall. But we could not completely sure whether he is tall. In fuzzy logic, we agree that Jack is more or less tall. We can assign a membership function to show the relationship of Jack to the set of tall people (ie. The fuzzy logic set): • <Jack, 0.75 = Tall> • This can be repressed in a knowledge-based systems as “Jack is tall” (CF=0.75). • An important difference from probability theory is that related memberships in fuzzy sets do not have to total 1.

  37. Fuzzy Logic Fundamentals • Fuzzy Set for a Tall Person • The statement “Jack is short” (CF=0.1) indicate that the combination is only 0.90. In probability theory, if the probability that Jack is tall is 0.75, then the probability that he is not tall (i.e., if only two events, he is short) must be 0.25. • In contrast to certainty factors that includes two values (e.g., the degree of belief or disbelief), fuzzy sets use a spectrum of possible values called belief functions. We express our belief that a particular item belongs to a set through a membership function, as shown in Figure 13.7. • At a height of 69 inches, a person starts to be considered tall, and at 74 inches, he or she is definitely tall. Between 69 and 74 inches, the person’s membership function value varies from 0 to 1. Likewise, a person has a membership function value in the set of short people and medium-height people, depending on his or her height. The medium range spans both the short and tall ranges, so a person has a belief of potentially being a member of more than one fuzzy set at a time. • This is a critical strength of fuzzy sets: no crispness.

  38. Fuzzy Logic Fundamentals

  39. Fuzzy Logic Fundamentals • Fuzzy logic applications in manufacturing and management • Selection of stocks to purchase (e.g., the Japanese Nikkei stock exchange) • Retrieval of data (because fuzzy logic can find data quickly) • Inspection of beverage cans for printing defects • Matching of golf clubs to customers’ swings • Risk assessment • Control of the amount of oxygen in cement kilns • Accuracy and speed increases in industrial quality-control applications • Sorting problems in multidimensional spaces

  40. Fuzzy Logic Fundamentals • Fuzzy logic applications in manufacturing and management • Enhancement of models involving queuing (i.e., waiting lines) • Managerial decision support applications • Project selection • Environmental control building • Control of the motion of trains • Paper mill automation • Space shuttle vehicle orbiting • Regulation of water temperature in shower heads

  41. Natural Language Processing (NLP) • Natural language processing (NLP) Using a natural language processor to interface with a computer-based system • Two types of NLP • Natural language understanding • Natural language generation

  42. Natural Language Processing (NLP) • Some problems that make NLP difficult • Word boundary detection • Word sense disambiguation • Syntactic ambiguity • Imperfect or irregular input • Speech acts and plans

  43. Natural Language Processing (NLP) • The current NLP technology • Search and information retrieval • A person enters a certain phrase, word, or sentence on which to search the Internet or some database, and NLP is then used to construct the best query possible

  44. Natural Language Processing (NLP) • Applications of NLP • Human–computer interfaces • Abstracting and summarizing text • Analyzing grammar • Understanding speech

  45. Natural Language Processing (NLP) • Applications of NLP • Front ends for other software packages—querying a database that allows the user to operate the applications programs with everyday language • Text mining • FAQs and query answering

  46. Natural Language Processing (NLP) • Machine translation • Translation of content to other languages • Criteria used to assess machine translation • Intelligibility • Accuracy • Speed

  47. Voice Technologies • Voice technologies fall into three broad categories: • Voice (or speech) recognition • Voice (or speech) understanding • Text-to-voice (or voice synthesis)

  48. Voice Technologies • Voice (speech) recognition Translation of the human voice into individual words and sentences understandable by a computer • Speech understanding An area of AI research that attempts to allow computers to recognize words or phrases of human speech

  49. Voice Technologies • Advantages of voice technologies • Ease of access • Speed • Manual freedom • Remote access • Accuracy • Communicating while driving • Quick selection • Security • Cost benefit

  50. Voice Technologies • Limitations of speech recognition and understanding • Inability to recognize long sentences, or the excessive length of time needed to accomplish that understanding • High cost • Speech may need to be combined with keyboard entry, which slows communication