Mycin
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Mycin

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Mycin Mycin Presentation Transcript

  • An Expert System MYCIN
  • Definition of Expert System
    • A computing system capable of representing and reasoning about some knowledge rich domain, which usually requires a human expert, with a view toward solving problems and/or giving advice. Such systems are capable of explaining their reasoning.
    • Does not have a psychological model of how the expert thinks, but a model of the expert’s model of the domain.
  • What is:
    • Expertise?
    • Expertise  consists of those characteristics, skills and knowledge of a person (that is, expert) or of a system, which distinguish experts from novices and less experienced people.
    • An expert?
    • often outperforming human beings at particular tasks are generally termed as expert.
  • Distinction Between an Expert System and a Knowledge-Based System
    • To be classified as an ‘expert system’ the system must be able to explain the reasoning process.
    • This is often accomplished by displaying the rules that were applied to reach a conclusion.
  • Rule-Based Expert Systems: Suitable Domains
    • Many Rules
    • No Unifying Theorem
    • Knowledge can be easily separated from the way it is used
    • Updating the knowledge base has to be easy
    • The knowledge base can be the only [indirect] communication channel among rules
    • Clinical/psychological and other domains, rather than mathematical/physical domains
  • MYCIN: The Problem
    • Roberts & Visconti [1972]:
      • Only 13% of patients are treated rationally
      • 66% are being given irrational treatment
      • 21% are being given questionable treatment
    • Irrationality means, for example:
      • Using a contra-indicated combination
      • Using the wrong agent for a specific organism
      • Not taking the required cultures
  • Design Parameter
    • Program must be competent & easy to use
    • Must handle a large, changing body of knowledge
    • Interact with human users
    • Must take time into account
    • Work with incomplete or uncertain information
  • System Components
    • Consultation system
      • Asks questions
      • Draws conclusions
      • Gives advice
    • Explanation system
      • Translates rule to English before display
    • Rule acquisition/modification system
  • Expert System Structure User Interface Environment Language/Shell Explanation Facility Inference Engine Knowledge Base Blackboard
  • Stages in Diagnosis and Treatment
    • Decide if there is a significant infection
    • Identify the causing organism(s) by clinical and laboratory evidence
    • Decide what antibiotic agent the organisms are sensitive to
    • Prescribe the optimal drug combination for the particular case
  • A MYCIN Runtime Example
  • The MYCIN Architecture Consultation program Explanation program Knowledge-acquisition program Dynamic patient data Static factual & judgmental knowledge Physician user Infectious diseases expert
  • A Sample Context Tree
  • Rule Grammar
    • <rules. ::=
    • <premise> <action>
    • <premise> ::=
    • ($AND <condition> … <condition>)
    • <condition> ::=
    • (<predicate> <context> <parameter> <value>) |
    • ($OR <condition> … <condition>)
    • <action> ::=
    • <conclusion> | <instruction>
  • English Rule
    • IF strain of org is gramneg and
    • morphology of org is rod and
    • aerobicity of org is aerobic
    • THEN
    • there is strongly suggestive evidence
    • (0.8) that the class of org is
    • enterobacteriacae
  • The Knowledge Base
    • Inferential knowledge stored in decision rules
      • If Premise then Action ( Certainty Factor [ CF ])
      • If A&B then C (0.6)
      • The CF represents the inferential certainty
    • Static knowledge :
      • Natural language dictionary
      • Lists (e.g., Sterile Sites)
      • Tables (e.g., gram stain, morphology, aerobicity)
    • Dynamic knowledge stored in the context tree
      • Patient specific
      • Hierarchical structures: Patient, cultures, organisms
      • < Object , Attribute , Value > triples: <Org1, Identity, Strep>
      • A CF used for factual certainty <Org1, Identity, Staph, 0.6>
  • Static Data Structures
    • Simple lists
      • enumerate organisms and sterile sites known to system
    • Knowledge tables
      • contain clinical parameters and their values under various circumstances
    • Classification system
      • clinical parameters according the contexts in which they apply
  • Dynamic Data Structures
    • Context tree
      • serves to organize information relating to a particular patient
      • used to structure clinical problem and relate contexts to one another
      • rules are related to the context tree (although the rules themselves are not organized into either a decision tree or inference network)
  • Control Structure
    • Backward chaining
      • helps to keep it focuses
      • facilitates backward reasoning from top level goal for all queries
  • Inference Strategy
    • Subgoals are generalized (i.e. match with variables) when possible
    • All applicable rules are evaluated before reaching a decision
    • Facts with certainities between –0.2 and +0.2 are treated as unknown
    • Mycin asks for lab for some facts before attempting a deduction
    • A list of rules that fail under the current context is maintained to avoid re-evaluation
    • Premises are evaluated based on known fact before search is allowed
  • Goal Rule
    • IF
    • there is an org requiring therapy and
    • consideration has been given to possibilty of other orgs requiring therapy
    • THEN
    • compile a list of possible therapies and
    • select the best alternative from list
  • Consultation Procedure
    • Create a patient context as the top level node in the context tree
    • Attempt to apply the goal rule to this particular patient context
    • Context tree is fleshed out in an effort to accumulate evidence from user query or inference
    • Each node contains accumulated evidence including “lab data” to allow alternation between question selection and rule invocation
  • Mutually Recursive Procedures
    • Monitor
      • attempts to evaluate premise of current rule
      • if it fails rule is discarded and next rule from list is examined (restricted by context)
    • Findout
      • gathers evidence for and against rule premise
      • if question can be asked control returns to Monitor with answer
      • if no question new list of rules to determine truth of rule premise is returned to Monitor
  • Mycin Rules
    • Had 200 rules in 1976
    • Meta-rules
      • rule pruners similar to alpha/beta cutoffs
      • rules to reorder relevant domain rules
      • general (domain free) problem solving heuristics
      • some forward (antecedent) reasoning to cut stupid questions (i.e. skip pregnancy questions for males)
  • Explanation System
    • Can display rule being invoked at any point in consultation
    • Record rule invocation and associates them with questions asked and rules invoked
    • Use rule index to retrieve particular rules in answer to questions
    • Why and how questions answered using goal tree
  • Rule Acquisition System
    • Domain experts allowed to enter and change rules
    • Rules translated to Lisp and rule numbers added to “Look-ahead” and “Updated-by” lists
    • Does not catch contractions and inconsistencies in large rule-bases
  • Evaluation
    • 1974
    • Panel of 5 experts approve 72% of Mycin’s recommendations for 15 patients
    • 1976
    • 8 experts (5 faculty, 1 resident, 1 med student, 1 research fellow) made drug recommendations for 10 patients
    • Mycin had best match (52%) with actual drug recommendations used by attending physician
  • The Rule Interpreter
    • Control structure: goal driven , backward chaining
    • Attempt to establish values of clinical parameters at the leaf nodes
    • The interpreter retrieves a list of rules whose conclusions bear on current goals, and tries to evaluate these rules
    • Questions are asked only when the rules fail to deduce the necessary information
    • If the user cannot supply the information, the rule is ignored
  • A MYCIN Reasoning Tree
    • The Main MYCIN Algorithm
    • Uses Monitor and FindOut to recursively invoke each rule when relevant
  • The Monitor Mechanism
  • The FindOut Mechanism
  • Certainty Factors
    • Not a Bayesian probability measure, but rather a Certainty Factor ( CF ) with its update functions
    • A Conclude function uses
      • The CF of the rule used for making the inference
      • The minimal CF of the premises (using the Tally function)
      • The context node about which the conclusion is made
      • The clinical parameter whose value is added to the dynamic DB
      • The value of the clinical parameter
    • Conclude derives a conclusion including the CF of the result
      • E.g., “ There is suggestive evidence (0.7) that the identity of the organism is streptococcus ”
      • The CF is mapped into English
    • The CF of a context is updated by other evidence (relevant rules)
    • It is always true that -1 ≤ CF ≤ +1
    • If CF = +1 then all other hypotheses are rejected
  • The Evaluation Method
    • 15 patients with positive blood cultures (at least one organism)
    • 5 Stanford infectious disease experts
    • 5 experts from other hospitals
    • All data recorded and given, if asked for, by the computer or a human expert
    • All decisions by the computer or the experts recorded, including the majority opinion
  • Summary
    • Mycin combines the advantages of general rule-based system with the advantages of an “inexact” reasoning system
    • Mycin has not addressed
      • how to convert from human terms to certainties
      • how to normalize across different people’s
      • how far to propagate certainty factor changes based on new evidence
      • how to provide feedback to database to improve certainty factor accuracy
    • Thank You