ADVANCED INTELLIGENT SYSTEMS

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Chapter 13

• ADVANCED INTELLIGENT SYSTEMS

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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

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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

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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)

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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

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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

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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

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Machine-Learning Techniques
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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

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Case-Based Reasoning (CBR)

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

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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

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Case-Based Reasoning (CBR)

• The basic idea and process of CBR
• Four-step process
• Retrieve
• Reuse
• Revise
• Retain

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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

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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

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Case-Based Reasoning (CBR)
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Case-Based Reasoning (CBR)

• Benefits and usability of CBR
• CBR makes learning much easier and the
  recommendation more sensible

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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

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Case-Based Reasoning (CBR)
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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

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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

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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?

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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?

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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

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Genetic Algorithm Fundamentals

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

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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

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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

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Genetic Algorithm Fundamentals
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Genetic Algorithm Fundamentals
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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

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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

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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

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Developing Genetic Algorithm Applications

• GAs are a type of machine learning for
  representing and solving complex problems

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Developing Genetic Algorithm Applications
Applications of GAs include

• 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

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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

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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

Height Proportion voted for
510 0.05
511 0.10
6 0.60
6.1 0.15
6.2 0.10
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Fuzzy Logic Fundamentals

• Fuzzy Set for a Tall Person
• Suppose Jacks 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 Jacks 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)
• ltJack, 0.75 Tallgt
• This can be repressed in a knowledge-based
  systems as Jack is tall (CF0.75).
• An important difference from probability theory
  is that related memberships in fuzzy sets do not
  have to total 1.

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Fuzzy Logic Fundamentals

• Fuzzy Set for a Tall Person
• The statement Jack is short (CF0.1) 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
  persons 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.

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Fuzzy Logic Fundamentals
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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

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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

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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

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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

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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

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Natural Language Processing (NLP)

• Applications of NLP
• Humancomputer interfaces
• Abstracting and summarizing text
• Analyzing grammar
• Understanding speech

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Natural Language Processing (NLP)

• Applications of NLP
• Front ends for other software packagesquerying a
  database that allows the user to operate the
  applications programs with everyday language
• Text mining
• FAQs and query answering

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Natural Language Processing (NLP)

• Machine translation
• Translation of content to other languages
• Criteria used to assess machine translation
• Intelligibility
• Accuracy
• Speed

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Voice Technologies

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

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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

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Voice Technologies

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

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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

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Voice Technologies

• Voice synthesis
• The technology by which computers convert
  text-to-voice (speak)
• A text-to-speech system is composed of two parts
  
• Front end takes input in the form of text and
  outputs a symbolic linguistic representation
• Back end takes the symbolic linguistic
  representation as input and outputs the
  synthesized speech waveform

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Voice Technologies

• Voice technology applications
• Call center
• Contact of customer care center
• Computer/telephone integration (CTI)
• Interactive voice response (IVR)
• Voice portal
• Voice over IP (VoIP)

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Voice Technologies

• Voice portals
• Web sites, usually portals, with audio interfaces

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Intelligent Systems over the Internet

• Web-Based Intelligent Systems

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Intelligent Systems over the Internet

• Web-Based Intelligent Systems
• Small systems that perform very specific tasks
  are often called agents
• Information agent take a request and navigate to
  the appropriate page on a Web site, locate the
  required information, and return it as an XML
  document for processing by another agent
• Monitoring agents are built on top of the
  information agent to keep track of previously
  returned results
• Recommender or recommendation agents assist in
  customization and personalization services that
  are critical to maintaining good customer
  relationships

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Intelligent Systems over the Internet

• Intelligent Agents An Overview
• Intelligent agent (IA) An expert or
  knowledge-based system embedded in computer-based
  information systems (or their components) to make
  them smarter
• The term agent is derived from the concept of
  agency, referring to employing someone to act on
  your behalf
• Types of agents
• Software agents
• Wizards
• Software daemons
• Softbots Intelligent software agents an
  abbreviation of robots. Usually used as part of
  another term, as in knowbots, softbots, or
  shopbots

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Intelligent Systems over the Internet

• Intelligent Agents An Overview
• Features of intelligent agents
• Reactivity Agents perceive their environment and
  respond in a timely fashion to changes that occur
  in it
• Proactiveness Agents are able to exhibit
  goal-directed behavior by taking initiative
• Social ability
• Autonomy
• Intelligence levels
• Level 0Agents retrieve documents for a user
  under straight orders
• Level 1Agents provide a user-initiated searching
  facility for finding relevant Web pages
• Level 2Agents maintain users profiles
• Level 3Agents have a learning and deductive
  component to help a user who cannot formalize a
  query or specify a target for a search

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Intelligent Systems over the Internet

• Intelligent Agents An Overview
• Components of an agent
• Owner
• Author
• Owner
• Goal
• Subject description
• Creation and duration
• Background
• Intelligent subsystem

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Intelligent Systems over the Internet

• Classification and Types of Intelligent Agents

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Intelligent Systems over the Internet

• DSS Agents and Multiagents
• Five types of DSS agents
• Data monitoring
• Data gathering
• Modeling
• Domain managing
• Preference learning
• Multiagent system
• A system with multiple cooperating software
  agents
• Distributed artificial intelligence (DAI)
• A multiple-agent system for problem solving.
  Splitting of a problem into multiple cooperating
  systems in deriving a solution

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Intelligent Systems over the Internet

• DSS Agents and Multiagents

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Intelligent Systems over the Internet

• The Semantic Web Representing Knowledge for
  Intelligent Agents

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Intelligent Systems over the Internet

• Web-Based Recommendation Systems
• A major application of intelligent systems in
  e-commerce is to recommend products to customers
• The major motivation for using recommendation
  agents is that personalization is a major trend
  in marketing and customer services
• Recommendation systems (agents)
• A computer system that can suggest new items to a
  user based on his revealed preference. It may be
  content-based or collaborative filtering to
  suggest items that match the preference of the
  user. An example is that Amazon.com's function of
  Other people bought this book also bought . . .
  function
• Collaborative filtering A method for generating
  recommendations from user profile. It uses
  preferences of other users of similar behavior to
  predict the preference of the particular user.

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Intelligent Systems over the Internet

• Web-Based Recommendation Systems
• Content-based filtering A method that recommends
  items for the user based on the description of
  previously evaluated items and information
  available from the content (such as keywords)
• Demographic filtering
• A method that uses the demographic data of the
  user to determine which item may be appropriate
  for recommendation.

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Intelligent Systems over the Internet

• Web-Based Recommendation Systems

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Intelligent Systems over the Internet

• Web-Based Recommendation Systems

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Developing Integrated Advanced Systems

• Fuzzy neural networks
• Fuzzification
• A process that converts an accurate number into
  a fuzzy description, such as converting from an
  exact age into young or old
• Defuzzification
• Creating a crisp solution from a fuzzy logic
  solution

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Developing Integrated Advanced Systems
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Developing Integrated Advanced Systems
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Developing Integrated Advanced Systems

• Genetic algorithms and neural networks
• The genetic learning method can perform rule
  discovery in large databases, with the rules fed
  into a conventional ES or some other intelligent
  system
• To integrate genetic algorithms with neural
  network models use a genetic algorithm to search
  for potential weights associated with network
  connections
• A good genetic learning method can significantly
  reduce the time and effort needed to find the
  optimal neural network model