Expert systems from rk
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Expert systems from rk



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Expert systems from rk Expert systems from rk Presentation Transcript

  • Expert systems M.S.Rama krishna (0458-703 )
  • Definition
    • What are Expert systems ?
      • Knowledge based systems , & , Part of the Artificial Intelligence field.
      • The idea is to inject expert knowledge in to a computer system.
      • The primary purpose is to automate DECISION MAKING
      • Computer programs (if and then rules) that contain some subject-specific knowledge of one or more human experts
      • Made up of a set of rules that analyze user supplied information about a specific class of problems.
      • Systems that utilize reasoning capabilities and draw conclusions
      • The most commonly used are
      • 1) LISP.
      • 2) Prolog.
  • Components of an Expert System (1)
    • Knowledge base :
      • IT is the Nucleus of the expert system structure
      • Stores all relevant information, data, rules, cases, and relationships used by the expert system
      • Knowledge engineers : who translate the knowledge of real human experts into rules and strategies, create it.
      • These rules and strategies can change depending on the prevailing problem scenario
  • Components of an Expert System(2)
    • Inference Engine :
      • Seeks information and relationships from the knowledge base and provides answers, predictions, and suggestions in the way a human expert would do
      • Match the premise patterns of the rules against elements in the working memory .
      • Explanation facility :
      • part of the expert system that allows a user or decision maker to understand how the expert system arrived at certain conclusions or results
    • Set of Rules :
    • A conditional statement that links given conditions to actions or outcomes with if and then rules
  • Expert systems /Rule Based systems
    • Rule-based or Expert systems - Knowledge bases consisting of hundreds or thousands of rules of the form:
      • IF (condition) THEN (action).
      • Use rules to store knowledge (“rule-based”).
      • The rules are usually gathered from experts in the field being represented (“expert system”).
    • IF ‘ x is A’
    • THEN ‘ y is B’
    antecedent or premise, consequent(or) conclusion. uent
  • Main Participants in Expert Systems
    • Domain expert
      • Who makes the system as expert by gathering or taken all the information from the expert , group , etc.. According to the different problem’s (or) scenario’s.
    • Knowledge engineer
      • Who makes the system as expert by injecting all the gathered information from the domain expert by implementing , developing , designed and maintained
    • Knowledge user
      • The individual or group who uses and benefits from the expert system
  • Domain expert Knowledge engineer Knowledge user EXPERT SYSTEM
  • Knowledge Acquisition Facility
      • Knowledge acquisition facility
        • Provides a convenient and efficient means of capturing and storing all components of the knowledge base
  • Methods used in the Expert system
    • The basic types are:
    • 1)Decision trees :
    • A tree is formed with the series of questions , responses where the answer is taken from the responses generated according to the scenario as end point.
    • 2)Forward chaining : (data-driven)
    • A method of reasoning that starts with the facts and works forward to the conclusions
    • 3)Back ward chaining : ( goal-driven )
    • A method of reasoning that starts with conclusions and works backward to the supporting facts
    • 4)State machines
    • 5)Bayesian networks
    • 6)Black board systems
    • 7)Case based reasoning
    • Example :
    • Suppose we have three rules:
    • R1: If A and B then D
    • R2: If B then C
    • R3: If C and D then E
    • Forward chaining system : 1) facts are processed first,
    • 2) keep using the rules to draw new conclusions given those facts
    • Rule 1 :
    • Rule 2:
    • Backward chaining :
    A B B E D C Rule 3 E D C B B A
    • State machine :
    • object :: number of different ‘states’,
    • How the object behaves,
    • switches between the states,
    • depends up on the current state it’s in state
    • Case based reasoning :
    • cases or instances of the problem, with solution that was found in a result that took place.
    • Rather than creating a set of rules, you just write an inference Engine
    • Bayesian networks: (probability based)
    • Gives a best-fit answer with probabilities
    • Consists of a table of inputs and outputs with the probability
    • particular input :: corresponding output
    • Black board systems: concept of neural network
  • Expert Systems Development Determining requirements Identifying experts Construct expert system components Implementing results Maintaining and reviewing system
    • Domain
    • The area of knowledge addressed by the expert system.
  • Loan application
    • Loan application for a loan for $100,000 to $200,000
    • If Month net income is greater than 4x monthly loan payment, and
    • If down payment is 15% of total value of property, and
    • If net income of borrower is > $25,000, and
    • If employment is > 3 years at same company
    • If There are no previous credits problems
    • Then accept the applications
    • Else check other credit rules
  • Applications of Expert Systems DENDRAL: Used to identify the structure of chemical compounds. First used in 1965 MYCIN: Medical system for diagnosing blood disorders. First used in 1979
  • Applications of Expert Systems PROSPECTOR: Used by geologists to identify sites for drilling or mining PUFF: Medical system for diagnosis of respiratory conditions
  • Applications of Expert Systems LITHIAN: Gives advice to archaeologists examining stone tools DESIGN ADVISOR: Gives advice to designers of processor chips
  • Evolution of Expert Systems Software
    • Expert system shell
      • Collection of software packages & tools to design, develop, implement, and maintain expert systems
    Ease of use low high Before 1980 1980s 1990s Traditional programming languages Special and 4 th generation languages Expert system shells
  • Need for expert systems
    • 1. Human expertise is very scarce.
    • 2. Humans get tired from physical or mental workload.
    • 3. Humans forget crucial details of a problem.
    • 4. Humans are inconsistent in their day-to-day decisions.
    • 5. Humans have limited working memory.
    • 6. Humans are unable to comprehend large amounts of data quickly.
    • 7. Humans are unable to retain large amounts of data in memory.
    • 8. Humans are slow in recalling information stored in memory.
  • Expert System Limitations
    • Limited focus
    • Inability to learn
    • Maintenance problems
    • May have high development costs
    • Can only solve specific types of problems in a limited domain of knowledge
  • Advantages
    • Reduce employee training costs
    • Centralize the decision making process.
    • Create efficiencies and reduce the time needed to solve problems.
    • Combine multiple human expert intelligences
    • Reduce the amount of human errors.
    • Expert-system approaches provide the added flexibility
    • (Easy for modification) with the ability to model rules as data rather than as code
  • Problems with expert system
    • Limited domain because , Domain experts cannot always clearly explain their logic and reasoning.
    • Systems are not always up to date, and
    • Don’t learn
    • Experts needed to setup and maintain system
    • No “common sense” , “ creative responses ” , give in unusual circumstances by humans.
    • Lack of flexibility and ability to adapt to changing environments
  • History
    • Edward Albert Feigenbaum  is the " Father of expert systems .“
    • Mid-1960’s : Problem Solver (A. Newell and H.Simon )
    • few laws of reasoning
    • +
    • powerful computers = expertise performance (initial situation desired goal set of operators )
    • Early Expert Systems (1970s)
      • DENDRAL infers molecular structure from the unknown compounds
      • MYCIN medical diagnosing (bacterial infections of the blood )
  • “ A Hangover From AI Winter"
    • AI winter  Is a period of reduced funding and interest in  ARTIFICIAL INTELLIGENCE research. The process of HYPE , disappointment and funding cuts are common in many emerging technologies 
    • The worst times for AI were 1974−80 and 1987−93
    • 1966: The failure of  Machine Translation
    • 1970: The abandonment of connectionism
    • 1973: In the United Kingdom LIGHTHILL REPORT
    • 1973−74: DARPA's cutbacks to academic AI research in general,
    • 1987: The collapse of the LISP MACHINE market,
    • 1988: The cancellation of new spending on AI by the STRATEGIC COMPUTING INITIATIVE.
    • 1993:  EXPERT SYSTEMS slowly reaching the bottom,
    • 1990s: The quiet disappearance of the FIFTH GENERATION COMPUTER project's original goals.
  • Conclusion
    • They require a lot of collaboration between a knowledge engineer and a domain expert.
    • When implemented correctly, to over come the human error expert systems remove
  • References: