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Knowledge Representation

Knowledge Representation. CS 171/271 (Chapter 10) Some text and images in these slides were drawn from Russel & Norvig’s published material. Using Logic for Knowledge Representation. Propositional and First-Order Logic describe the technology for knowledge-based agents

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Knowledge Representation

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  1. Knowledge Representation CS 171/271 (Chapter 10) Some text and images in these slides were drawn fromRussel & Norvig’s published material

  2. 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

  3. 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

  4. Relationships between Categories • Disjoint categories • Disjoint( {Animals, Vegetables} ) • Exhaustive decomposition • ExhaustiveDecomposition( {Faculty,Staff,Administrators}, UniversityPersonnel ) • Partition • Partition( {Males,Females}, Persons )

  5. 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)

  6. Measurements • Measures as objects • Measure: a number with units • Example • Length(L1) = Inches(1.5)

  7. 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 • Example:x  Butter  PartOf( y,x )  y  Butter

  8. 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

  9. 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

  10. Actions in Situation Calculus • Possibility Axiom • preconditions  Poss( action, situation ) • Example:“can move to a square if it is adjacent” • Effect Axiom • Poss( action, situation )  changes • Example:“moving updates agent position”

  11. 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

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

  13. Other Challenges • Beliefs • Uncertain Information • Dynamic Information • Read Chapter 10

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