COMP 4200: Expert Systems

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COMP 4200 Expert Systems

• Dr. Christel Kemke
• Department of Computer Science
• University of Manitoba

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Reasoning in Expert Systems

• knowledge representation in Expert Systems
• shallow and deep reasoning
• forward and backward reasoning
• alternative inference methods
• metaknowledge

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Experts and Expert Systems

• Human Experts achieve high performance because of
  extensive knowledge concerning their field
• Generally developed over many years

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Types of Knowledge

• Knowledge Representation in XPS can include
• conceptual knowledge
• terminology, domain-specific terms
• derivative knowledge
• conclusions between facts
• causal connections
• causal model of domain
• procedural knowledge
• guidelines for actions

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Knowledge Modeling in XPS

• Knowledge Modeling Technique in XPS
• mostly rule-based systems (RBS)
• rule system models elements of knowledge
  formulated independently as rules
• rule set is easy to expand
• often only limited deep knowledge, i.e. no
  explicit coherent causal or functional model of
  the domain

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Shallow and Deep Reasoning

• shallow reasoning
• also called experiential reasoning
• aims at describing aspects of the world
  heuristically
• short inference chains
• complex rules
• deep reasoning
• also called causal reasoning
• aims at building a model that behaves like the
  real thing
• long inference chains
• simple rules that describe cause and effect
  relationships

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Dilbert on Reasoning 1
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Dilbert on Reasoning 2
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Dilbert on Reasoning 3
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General Technology of XPS

• Knowledge Inference
• core of XPS
• Most often Rule-Based Systems (RBS)
• other forms Neural Networks, Case-Based
  Reasoning

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Rule-Based Expert Systems

• Work with
• a set of facts describing the current world state
  
• a set of rules describing the expert knowledge
• inference mechanisms for combining facts and
  rules in reasoning

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Inference Engine
Knowledge Base (rules)
Working Memory (facts)
Agenda
Explanation Facility
Knowledge Acquisition Facility
User Interface
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Architecture of Rule-Based XPS 1

• Knowledge-Base / Rule-Base
• stores expert knowledge as condition-action-rules
  (or if-then- or premise-consequence-rules)
• objects or frame structures are often used to
  represent concepts in the domain of expertise,
  e.g. club in the golf domain.
• Working Memory
• stores initial facts and generated facts derived
  by the inference engine
• additional parameters like the degree of trust
  in the truth of a fact or a rule (? certainty
  factors) or probabilistic measurements can be
  added

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Architecture of Rule-Based XPS 2

• Inference Engine
• matches condition-part of rules against facts
  stored in Working Memory (pattern matching)
• rules with satisfied condition are active rules
  and are placed on the agenda
• among the active rules on the agenda, one is
  selected (see conflict resolution, priorities of
  rules) as next rule for
• execution (firing) consequence of rule can
  add new facts to Working Memory, modify facts,
  retract facts, and more

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Architecture of Rule-Based XPS 3

• Inference Engine additional components
• might be necessary for other functions, like
• calculation of certainty values,
• determination of priorities of rules
• and conflict resolution mechanisms,
• a truth maintenance system (TMS) if reasoning
  with defaults and beliefs is requested

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Rule-Based Systems- Example Grades -

• Rules to determine grade
• study ? good_grade
• not_study ? bad_grade
• sun_shines ? go_out
• go_out ? not_study
• stay_home ? study
• awful_weather ? stay_home

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Example Grades
Rule-Base to determine the grade

• study ? good_grade
• not_study ? bad_grade
• sun_shines ? go_out
• go_out ? not_study
• stay_home ? study
• awful_weather ? stay_home

Q1 If the weather is awful, do you get a good or
bad grade? Q2 When do you get a good grade?
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Forward and Backward Reasoning

• forward reasoning
• Facts are given. What is the conclusion?
• A set of known facts is given (in WM) apply
  rules to derive new facts as conclusions (forward
  chaining of rules) until you come up with a
  requested final goal fact.
• backward reasoning
• Hypothesis (goal) is given. Is it supported by
  facts?
• A hypothesis (goal fact) is given try to derive
  it based on a set of given initial facts using
  sub-goals (backward chaining of rules) until goal
  is grounded in initial facts.

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Example Grades

• study ? good_grade
• not_study ? bad_grade
• sun_shines ? go_out
• go_out ? not_study
• stay_home ? study
• awful_weather ? stay_home

forward reasoning rule chain given fact
awful_weather 6,5,1 backward
reasoning hypothesis/goal good_grade 1,5,6
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Example Grades
Working Memory Agenda
awful weather
Rule 6
Select and apply Rule 6
awful weather stay home
Rule 5
Select and apply Rule 5
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Example Grades
Working Memory Agenda
awful weather stay home study
Rule 1
Select and apply Rule 1
awful weather stay home study good grade
empty
DONE!
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Example Police Reasoning Tree
forward reasoning Shield AND Pistol ?
Police backward reasoning Police ? Badge AND gun
Police
Bad Boy
Badge
Gun
AND
OR
Pistol
Revolver
Shield
Q What if only Gun is known?
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Example Grades Reasoning Tree
good grade
bad grade
not study
study
go out
stay home
sun shines
awful weather
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Example Police Reasoning Tree
Police
Bad Boy
Badge
Gun
AND
OR
Pistol
Revolver
Shield
Q What if only Pistol is known as ground fact?
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Example Police Reasoning Tree
Bad Boy
Police
Badge
Gun
AND
OR
Shield
Pistol
Revolver
Task Write down the Rule-Base for this example!
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Forward vs. Backward Chaining
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Alternative Reasoning Methods

• Theorem Proving
• emphasis on mathematical proofs and correctness,
  not so much on performance and ease of use
• Probabilistic Reasoning
• integrates probabilities into the reasoning
  process
• Certainty Factors
• Express subjective assessment of truth of fact or
  rule
• Fuzzy Reasoning
• allows the use of vaguely defined predicates and
  rules

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Metaknowledge

• deals with knowledge about knowledge
• e.g. reasoning about properties of knowledge
  representation schemes, or inference mechanisms
• usually relies on higher order logic
• in (first order) predicate logic, quantifiers are
  applied to variables
• second-order predicate logic allows the use of
  quantifiers for function and predicate symbols
• may result in substantial performance problems
• CLIPS uses meta-knowledge to define itself, i.e.
  CLIPS constructs, classes, etc. - in a
  bootstrapping form

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Expert Systems Task Areas
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Task Areas of Expert Systems

• System-based Problem or Task Description
• Analysis Tasks (Interpretation)
• Diagnosis
• Classification
• Synthesis Tasks (Construction)
• Construction
• Configuration
• Design
• Planning

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Analysis Tasks

• Analysis Tasks (Diagnosis, Classification)
• determine specific solution element (diagnosis)
  based on a description of the system (symptoms or
  other descriptive facts)
• rules formulate connections between symptoms etc.
  and diagnostic class
• e.g. the medical expert system MYCIN for
  diagnosing bacterial infections
• e.g. tutoring systems like GUIDEON for diagnosing
  students mistakes

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Synthesis Tasks

• Synthesis Tasks (Construction, Configuration,
  Design, Planning)
• combine elements from a component (solution)
  space and check consistency of complete solution
• rules formulate constraints and extensions for
  partial solution, similar to planning
• e.g. the technical expert system R1/XCON to
  configure computer systems

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Expert Systems Tasks

• Interpretation
• Prediction
• Diagnosis
• Design
• Planning
• Monitoring
• Debugging
• Instruction
• Control
• (after Hayes-Roth et al. (1983), Waterman (1986),
  cited from Luger and Stubblefield Artificial
  Intelligence, 1998, see Jackson, p.208)

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Expert Systems Tasks 1

• Interpretation
• forming high-level conclusions from raw data
• Prediction
• projecting probable consequences of given
  situations
• Diagnosis
• determining the cause of malfunctions in complex
  situations based on observable symptoms
• Design
• finding a configuration of system components
  that meets performance goals while satisfying a
  set of design constraints

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Expert Systems Tasks 2

• Planning
• devising a sequence of actions that will achieve
  a set of goals given certain starting conditions
  and run-time constraints
• Monitoring
• comparing a systems observed behavior to its
  expected behavior
• Debugging and Repair
• prescribing and implementing remedies for
  malfunctions
• Instruction
• detecting and correcting deficiencies in
  students understanding of a subject domain
• Control
• governing the behavior of a complex environment