Knowledge representation and rule based systems

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Knowledge representation and rule based systems
Interviews Protocol analysis Repertory grid
Induction etc
Semantic/networks Influence diagrams Rule
matrices etc
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Knowledge representation

• There are four basic techniques for representing
  the acquired knowledge in a knowledge base
• logic.
• semantic networks.
• frames.
• production rules.
• see course readings p52-75
• In particular the sections on Logic 5.2,
  semantic networks 5.3
• production rules 5.8 and frames 5.9.

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Knowledge representation

• Logic
• Knowledge and reasoning could be represented
  using predicate logic.
• This is the basis of the programming language
  PROLOG.
• Semantic networks
• ('concept maps', 'conceptual graphs' are very
  similar concepts)
• This approach has also been introduced as an
  example of an analysis representation technique.
  However a more formal approach can lead to a
  representation scheme which may be able to be
  implemented

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Knowledge representation

• Semantic networks can
• show natural relationships between
  objects/concepts
• be used to represent declarative/descriptive
  knowledge
• Semantic networks are constructed using nodes
  linked by directional lines called arcs
• A node can represent a fact description
• physical object
• concept
• event
• An arc (or link) represents relationships between
  nodes

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Knowledge representation

• Semantic networks
• There are some 'standard' relationship types
• 'Is-a' (instance relationship)
• 'Has-a' (part-subpart relationship)
• ltwill depend on the contextgt ( definitional
  link)
• lt again will depend on contextgt
  (heuristic link)

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Knowledge representation

• An example

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Knowledge representation

• A semantic net can be represented as a set of
  triples
• from the above example
• (Olesek)(is-a)(Male)
• (Harding)(is-a)(Male)
• (Male)(is-a)(Person)
• (Person)(wears)(Apparel)
• (Uniform)(is-a)(Apparel)
• (Uniform)(has-a)(Shirt Sleeve)
• etc
• A knowledge base (storing facts/relationship) can
  be built up using this scheme.
• In a semantic network inferencing involves search
  and deductive reasoning.

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Knowledge representation

• Disadvantages of a semantic network table 5.1,
  p60 readings
• incomplete (no explicit operational/procedural
  knowledge)
• lack of standards, ambiguity in node/link
  descriptions
• not temporal (i.e. doesn't represent time or
  sequence)
• Finally
• semantic networks are mainly used as an aid to
  analysis to visually represent parts of the
  problem domain. The knowledge' can be
  transformed into rules or frames for
  implementation

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Knowledge representation

• A Frame-Based Approach sec 5.9, course readings
  p68
• A frame is a data structure containing typical
  knowledge about a concept or object (Marvin
  Minsky (mid 1970s))
• A frame can encompass both semantic and
  procedural knowledge.
• A frame represents knowledge about real world
  things (or entities).
• A frame contains both descriptions of attributes
  and procedural details
• Expert (or knowledge base) systems that use
  frames as their fundamental knowledge
  representation scheme are referred to as
  frame-based systems
• frames are an application of the object-oriented
  approach to knowledge-based systems

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Knowledge representation

• A Frame-Based Approach
• A frame contains two key elements
• slot
• set of attributes for specific object being
  described
• facet
• extended knowledge about the attribute in a
  frame
• This knowledge may be expressed in many ways
• value a simple attribute value, can be
  symbolic, numeric, boolean etc
• default the value taken if the attribute is not
  otherwise described
• a range describes what type of information can
  appear in the attribute
• if-added procedural knowledge i.e. what action
  to take when a value is added to the attribute
• if needed procedural knowledge i.e. attribute
  is empty, but a value is needed --- so a
  procedure runs to obtain a value
• ..............................
• ...............................

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Knowledge representation

• A Frame-Based Approach
• Note
• if-changed, if-needed are examples of demons
• In general a demon has an IF -THEN structure
• they allow procedural knowledge to be combined
  with the declarative knowledge stored in the
  frame.
• instantiation --- an instance of a frame is
  created see figure 5-16, course readings
• For some general examples of frames see figure
  5.13, 5.16 p 70 course readings

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Knowledge representation

• Advantages and Disadvantages of a Frame-Based
  Approach
• see table 5.6, course notes p. 74
• Disadvantages
• complex
• reasoning (inferencing) is difficult
• explanation is difficult
• Advantages
• knowledge domain can be naturally structured a
  similar motivation as for the O-O approach.
• handles both semantic/procedural knowledge.
• easy to include the idea of default values,
  detect missing values, include specialised
  procedures and to add further slots to the frames

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Knowledge representation

• Production Rules sec 5.8, course readings
• a representation scheme implementable by many ES
  shells.
• they consist of conditional statements.
• expresses relationships b/w parameters and
  variables.
• easy to understand (ie knowledge' is visible).
• can express heuristic knowledge.
• can incorporate uncertainty and can be expressed
  in modules.
• Expert systems that use a knowledge base
  consisting of production rules are called
  rule-based systems see course readings p 76 -
  95

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Knowledge representation

• syntax
• IF ltpremisegt THEN ltactiongt
• ltpremisegt is boolean. The AND, and to a lesser
  degree OR and NOT, logical connectives are
  possible.
• ltactiongt a series of statements
• Notes
• The rule premise can consist of a series of
  clauses and is sometimes referred to as the
  antecedent
• The actions are sometimes referred to as the
  consequent
• A rule that contains two clauses in the
  antecedent connected by logical OR can re-written
  as two rules
• IF weather is hot OR temperature is high THEN
  ice-cream sales are high
• could be re-written as
• IF weather is hot THEN ice-cream sales are high
  
• IF temperature is high THEN ice-cream sales are
  high
• see table 5-3, course readings for some rule
  characteristics

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Knowledge representation

• Differences b/w production rules and the
  conditional statements found in conventional
  programming languages
• Production rules are relatively independent of
  one another.
• Production rules can be based on heuristics or
  experiential reasoning.
• Production rules can accept uncertainty in the
  reasoning process.
• Conditional statements combine the knowledge'
  and the reasoning' in the one structure.
• Production rules simply represent the knowledge
  --- the inferencing or reasoning is separate.
• A simple example part of a small KBS to help in
  weather prediction
• R1 IF the ambient temperature is above 35 C
• THEN the weather is hot
• R2 IF the relative humidity is greater than
  65
• THEN the atmosphere is humid
• R3 IF the weather is hot AND the atmosphere is
  humid
• THEN thunderstorms are likely (CF 9/10)
  
• an alternative is to write one rule (R1 R2 are
  placed directly in the premise of R3). However we
  may wish use the statements, the weather is hot
  and the atmosphere is humid, for other purposes
  in the knowledge base.

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Knowledge representation

• Advantages and disadvantages of a rule-based
  approach
• Advantages
• easy to understand the knowledge content'.
• explanation for the reasoning is easily shown
  i.e. a list of which rules fired (and in which
  order) during reasoning.
• maintenance (or modification) is easy, provided
  the rule base structured well.
• uncertainty can incorporated into the knowledge
  base.
• Disadvantages (or limitations)
• rule bases can be very large (thousands of
  rules)
• rules may not reflect the actual decision
  making
• the only structure in the KB is through the
  rule chaining

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Next time

• A visualisation of the rule base the AND/OR
  graph
• Reasoning with a rule base
• Forward chaining
• Backward chaining