Knowledge Representation

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Knowledge Representation

• CS 171/271
• (Chapter 10)
• Some text and images in these slides were drawn
  fromRussel Norvigs published material

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Using Logic forKnowledge Representation

• Propositional and First-Order Logic describe the
  technology for knowledge-based agents
• What gets into these knowledge bases?
• Categories, objects, substances
• Agent actions, situations, events
• Beliefs
• Uncertain information
• Dynamic information

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Categories

• Representing categories
• As predicates Singer( Madonna)
• As objects Member( Madonna, Singers ) or
  Madonna ? Singers
• Related notions
• Subclasses/subcategories ( ? )
• Categories versus properties
• Categories of categories

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Relationships between Categories

• Disjoint categories
• Disjoint( Animals, Vegetables )
• Exhaustive decomposition
• ExhaustiveDecomposition( Faculty,Staff,Administra
  tors, UniversityPersonnel )
• Partition
• Partition( Males,Females, Persons )

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Physical Composition

• Part-of relationship
• Composite objects
• With structural properties (e.g., car as
  something with wheels and other things attached
  to it)
• Bunches (e.g., apples in a bag)

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Measurements

• Measures as objects
• Measure a number with units
• Example
• Length(L1) Inches(1.5)

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Substances and Objects

• World not necessarily individuated
• Not always divided into distinct objects
• In the English language
• Count nouns versus mass nouns
• Has special properties
• Examplex ? Butter ? PartOf( y,x ) ? y ? Butter

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Actions

• In the context of an agent, we need to represent
  actions and consequences
• Need to aslo worry about percepts, time, changing
  situations, and many others
• Situation calculus or event calculus

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Situation Calculus

• Situations
• Objects/terms that stand for the states between
  actions carried out (initial situation and
  generated situations after an action)
• Result( a, s ) names the resulting state when
  action a is executed in situation s
• Fluents
• Predicates/functions that vary across situations
• Eternal predicates
• Not dependent on situation

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Actions in Situation Calculus

• Possibility Axiom
• preconditions ? Poss( action, situation )
• Examplecan move to a square if it is adjacent
• Effect Axiom
• Poss( action, situation ) ? changes
• Examplemoving updates agent position

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Frame Problem

• In the real world, most things stay the same from
  one situation to the next
• Change occurs for a tiny fraction of the fluents
• Note effect action would often only note those
  changes
• Frame problem problem of representing those that
  stay the same
• Efficiency/compactness issue
• Representational versus Inferential

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Event Calculus

• Time as objects
• Fluents hold at points in time
• Reasoning can be made over time intervals

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Other Challenges

• Beliefs
• Uncertain Information
• Dynamic Information
• Read Chapter 10