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

The MYCIN Architecture Consultation program Explanation program Knowledge-acquisition program Dynamic patient data Static factual & judgmental knowledge Physician user Infectious diseases expert

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

The Main MYCIN Algorithm Uses Monitor and FindOut to recursively invoke each rule when relevant

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

Mycin

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Mycin