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Lecture5 Expert Systems And Artificial Intelligence

Nov 22, 2014

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Technology

 

  • 1. Expert Systems and Artificial Intelligence Modified from Marakas 2003 Reading: Chapter 7 the expert is the one who has made the most mistakes in the domain of interest
  • 2. The Concept of Expertise
    • Expertise: extensive knowledge in a narrow field
      • Gained by making mistakes
      • Arrive at solutions through logic
      • Logic establishes the True/Falsity of assertions
      • IF-THEN to arrive at conclusions
      • Derive new information from existing to arrive at conclusions
      • Deductions and inference are used to establish how well facts fit a scenario
      • Experts use this information fit to arrive at their decisions
  • 3. Example of information fit
    • Known information
      • John is Sams son
      • John is the eldest child
      • Mary is Sams daughter
      • John and Marys mother is called Anna
      • Sam has been married to Anna for 50 years
    • Derived information
      • If John is Sams son, THEN John must be a boy
      • If Sam and Anna have been married for 50 years, THEN John and Mary are their children by either birth or adoption
  • 4. Expert Sytems & Artificial Intelligence
    • Expert systems: a computer application that employs a set of rules based on human knowledge to solve problems that require human expertise
      • Imitates reasoning of experts on information fit
      • A non-expert simulates a dialog with an expert to solve complex problems
    • Artificial Intelligence: practical mechanisms that enable computers to simulate the human reasoning process
      • Interface of compute science/cognitive psychology
      • the study of how to make computers do things which humans do better the Turing Test?
  • 5. The Intelligence of Artificial Intelligence
    • How do people reason? (the realm of cog. psych.)
      • Categorization
        • Can reason at levels of abstraction
        • Rules derived from relationships among categories
      • Specific Rules
      • Heuristics
        • Less rigorous rules of thumb
      • Past Experience
        • Use a similar scenario to model new one (benefit of 20/20 hindsight)
      • Expectations
        • Frequency, pattern recognition
    So can computers simulate these processes of reasoning? car bike personal Motor bike commercial Land transport bus taxi
  • 6. How Do Computers Reason?
    • Rule-based reasoning: IF-THEN statements represent knowledge encoded as rules
      • If TRUE rule is instantiated, otherwise ignored
    • Pattern recognition: detecting sounds, shapes or long sequences
      • Analogous to Expectation, similar conditions
    • Frames: Object-oriented approach of creating hierarchical data structures analogous to categorisation databasey!!
    • Case-based reasoning: adapting previous solutions to a current problem
  • 7. Types of IF Then
    • Inferential
      • If premise THEN conclusion
      • If snowing THEN drive with caution
    • Procedural
      • If situation THEN action
      • If Average grade =A THEN award 1 st class degree
    • Declarative
      • If Antecedent THEN consequent
      • If student has mitigating circumstances THEN award incomplete grade
  • 8. The CBR Cycle
  • 9. Other Forms of AI
    • Machine learning neural networks and genetic algorithms (learning mechanisms)
    • Automatic programming mechanisms that generate a program to do a specific task (allows non-programmers to program)
      • User describes inputs and generates a program
    • Artificial life attempts to recreate biological phenomena within computer-based systems
      • As opposed to dissecting frogs!
      • Transfer to design of engineering projects (software, spacecraft, robotics etc)
  • 10. Neural Network Train it? supervised Or just let it get on with it? - unsupervised
  • 11. Genetic Algorithm mutation crossover Used in scheduling (timetabling?), design, marketing
  • 12. The Concept and Structure of Expert Systems
    • Basic structure of an ES follows the generic structure of a DSS
      • User interface, Knowledge base, inference engine
    • The knowledge base is specific to a particular problem domain associated with the ES
    • The main difference between an ES and DSS is that the ES contains knowledge acquired from experts in the application domain
  • 13. Common Expert System Architecture User Knowledge Engineer User Interface Inference Engine Knowledge Base User Environment KE Tool Kit KE Interface Development Environment Organization Systems Interface
  • 14. The User Interface in an ES
    • Design of the UI focuses on human concerns such as ease of use, reliability and reduction of fatigue
      • Critical to its success
      • Balance with storage capacity / hardware constraints
    • Design should allow for a variety of methods of interaction (input, control and query)
    • UI should allow for a variety of interactive mechanisms:
      • touch screen, keypad, light pens, voice command, hot keys
  • 15. The Knowledge Base the Brains
    • Contains the domain-specific knowledge acquired from the domain experts
    • Can consist of object descriptions, problem-solving behaviors, constraints, heuristics and uncertainties
    • The success of an ES relies on the completeness and accuracy of its knowledge base
      • Distinguish a database (data facts) from a knowledge base (experts rules, cases, etc)
  • 16. The Inference Engine the brawn!
    • Here, the knowledge is put to use to produce solutions
    • The engine is capable of performing deduction or inference based on rules or facts
    • Also capable of using inexact or fuzzy reasoning based on probability or pattern matching
    • Cycle consists of:
      • Match rules with given facts
      • Select the rule that is to be executed
      • Execute the rule by adding the deduced fact to the working memory
  • 17. Chaining
    • Simple methods used by most inference engines to produce a line of reasoning
    • Two methods are possible depending on the direction of reasoning
    • Forward chaining: the engine begins with th