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Knowledge Representation

Knowledge Representation. Use of logic. Artificial agents. need Knowledge and reasoning power Can combine GK with current percepts Build up KB incrementally Logic primary vehicle K always definite ( T/F). Problem for a robot.

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Knowledge Representation

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  1. Knowledge Representation Use of logic

  2. Artificial agents • need Knowledge and reasoning power • Can combine GK with current percepts • Build up KB incrementally • Logic primary vehicle • K always definite ( T/F)

  3. Problem for a robot • If red light is ON or it is morning shift or supervisor absent then door is locked. • If door is locked it implies that the red light is turned ON or it is morning shift or the supervisor is absent • If load is small in size or load is light then the conveyor belt moves • If the conveyor belt is moving then it means the load has a small size or load is light • The Red light is off, the Conveyor belt is not moving and the Door is locked. • The robot wants to know if the load is heavy (not light).

  4. Robot needs a Knowledge Base and reasoning ability

  5. Knowledge base • Central component of a K based agent • Set of sentences • INFERENCE • Deriving new info from old • Language to enable building KB

  6. Interpretations • Language semantics defines TRUTH of each sentence w.r.t. each possible world

  7. Similarity with CSP • Constraint solving is a form of Logical reasoning • Constraint languages: LOGICS

  8. Wff and logical reasoning • Entailment: • Sentence follows logically from another sentence • KB |= s • iff in every model in which KB is true, s is also true

  9. Inference algorithm • Enumerate the models • Check if s is true in every model (interpretation) for which KB is also true • Backtracking search – recursively assign values to variables • Exponential complexity

  10. definitions • Validity • Tautology • Deduction theorem • Satisfiability • inconsistancy

  11. Reasoning patterns in Propositional logic

  12. Inference rules • Modus Ponens • And Elimination • Standard logical equivalances • De Morgan • Contra positive • Distributive laws • Associative laws

  13. Deduction • With the knowledge base that the robot has, and what it currently perceives (more knowledge added to the KB), the robot wants to deduce that the load is not light

  14. Knowledge that robot has • If red light is ON or it is morning shift or supervisor absent then door is locked. • If door is locked it implies that the red light is turned ON or it is morning shift or the supervisor is absent • If load is small in size or load is light then the conveyor belt moves • If the conveyor belt is moving then it means the load has a small size or load is light

  15. Observations by the robot • Red light is off • Conveyor belt is not moving • Door is locked

  16. What the robot wants to establish? • The load is not light ( or in other words it is heavy)

  17. Knowledge + Observation (K.B.) • If red light is ON or it is morning shift or supervisor absent then door is locked. • If door is locked it implies that the red light is turned ON or it is morning shift or the supervisor is absent • If load is small in size or load is light then the conveyor belt moves • If the conveyor belt is moving then it means the load has a small size or load is light • Red light is off • Conveyor belt is not moving • Door is locked

  18. Propositions • P: red light is ON • M: it is morning shift • N: supervisor absent • D: door is locked. • Q: load is small in size • R: load is light • B: the conveyor belt is moving

  19. Next? • Now generate wffs and start the inference process

  20. Steps to help the robot (inferencing) • Consider a relevant rule for conveyor belt • Use And-elimination • Use contra-positive relation • Use modus ponens • Use de morgan’s law

  21. PROOF? • PROOF: Sequence of application of Inference rules. • Finding proofs is like finding solutions to search problems. • Successor function generates all possible application of inference rules • In worst case, search for proof would be as bad as enumerating all the models • Some irrelevant propositions can be ignored to speed up search.

  22. Monotonicity • Set of entailed sentences can only increase as info is added to KB. • Rules can be applied wherever suitable

  23. Resolution • What about completeness? • Can everything be inferred? • Resolution rule forms basis for a family of complete inference procedures.

  24. Refutation completeness • Resolution can be used to either CONFIRM or REFUTE a sentence

  25. Artificial Intelligence

  26. Intelligent?

  27. What is intelligence? • computational part of the ability to achieve goals in the world

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