Knowledge Representation

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Knowledge Representation

• CIS 479/579
• Bruce R. Maxim
• UM-Dearborn

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Representation

• Set of syntactic and semantic conventions which
  make it possible to describe things
• Syntax
• specific symbols allowed and rules allowed
• Semantics
• how meaning is associated with symbol
  arrangements allowed by syntax

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Representation Types

• Relational databases
• Constraints
• Predicate logic
• Concept hierarchies
• Semantic networks
• Frames
• Conceptual Dependency
• Scripts

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Types of Knowledge

• Objects
• both physical concepts
• Events
• usually involve time
• maybe cause effect relationships
• Performance
• how to do things
• META Knowledge
• knowledge about how to use knowledge

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Stages of Knowledge Use

• Acquisition
• structure of facts
• integration of old new knowledge
• Retrieval (recall)
• roles of linking and chunking
• means of improving recall efficiency

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Stages of Knowledge Use

• Reasoning
• Formal reasoning
• deductive theorem proving
• Procedural Reasoning
• expert system
• Reasoning by Analogy
• very hard for machines
• Generalization
• reasoning from examples
• Abstraction
• simplification

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Knowledge Representation Issues

• Grain size or resolution detail
• Scope or domain
• Modularity
• Understandability
• Explicit versus implicit knowledge
• Procedural versus declarative knowledge

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Advantages

• Declarative representation
• Store each fact once
• Easy to add new facts
• Procedural representation
• Easy to represent "how to do things"
• Easy to represent any knowledge not fitting
  declarative format
• Relatively easy to implement heuristic stuff on
  doing thing efficiently

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Attributes of Good KR Schemes

• Representational Adequacy
• works for all knowledge in problem domain
• Inferential Adequacy
• provides ability to manipulate structures to
  desire new structures
• Inferential Adequacy
• ability to incorporate additional information in
  knowledge structures to help focus attention of
  promising new directions

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Attributes of Good KR Schemes

• Acquisitional Efficiency
• easy to add new knowledge
• Semantic Power
• Supports truth theory
• Provides for constraint satisfaction
• Can cope with incomplete or uncertain knowledge
• Contains some commonsense reasoning capability

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Broad KR Questions

• Are there properties of objects so basic that
  they occur in every domain?
• If so what are they?
• At what level should knowledge be represented?
• Is there a good set of primitives into which all
  knowledge can be broken down?
• How can the relevant parts of a large knowledge
  base be accessed when needed?

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State Space Representation

1. How can individual objects and facts be
   represented?
2. How do you combine individual object descriptions
   to form a representation of the complete problem
   state?
3. How can the sequences of problem states that
   arise be represented efficiently?

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Two Approaches

• Use complete object descriptions that include
  relations to other objects in the environment
• Use predicate logic to express these kind of
  relations
• on(plant,table).
• under(table,window).
• in(table,room).

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Frame Problem

• What (or how much) should be stored at each
  node?
• How do you distinguish between facts that change
  from facts that do not change between frames?
• Stated and another way, how do you decide how
  much information to record as you move from
  problem state to problem state?

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Frame Problem

• The naive approach is to store complete state
  descriptions and make changes to them each time a
  node is updated
• Disadvantage
• takes time to do
• descriptions can become large
• what happens when algorithm needs to backtrack
  and undo changes?

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Frame Problem

• Better solution is to not physically modify the
  state description but merely record a list of
  changes that should be made at this node
• To get to the current state, you start at the
  initial state and then apply the changes recorded
• Backtracking will be easy, but state description
  is complicated

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Frame Problem

• Another alternative would be to modify the state
  description but mark the changes made so they can
  be undone when backtracking is required
• If temporal relations (time) are involved things
  will become regardless of the approach used

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Searching Rule-bases

• Sequential search of a large rule-base is time
  consuming and should be avoided if possible
• Making use of rule indices and hash tables would
  improve the efficiency
• Using variables in rules can reduce the number of
  rules