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For Monday

For Monday. Finish chapter 12 Homework: Chapter 13, exercises 8 and 15. Program 2. Any questions?. Dinner Date example. Initial Conditions: (and (garbage) ( cleanHands ) (quiet)) Goal: (and (dinner) (present) (not (garbage)) Actions: Cook :precondition ( cleanHands )

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For Monday

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  1. For Monday • Finish chapter 12 • Homework: • Chapter 13, exercises 8 and 15

  2. Program 2 • Any questions?

  3. Dinner Date example • Initial Conditions: (and (garbage) (cleanHands) (quiet)) • Goal: (and (dinner) (present) (not (garbage)) • Actions: • Cook :precondition (cleanHands) :effect (dinner) • Wrap :precondition (quiet) :effect (present) • Carry :precondition :effect (and (not (garbage)) (not (cleanHands)) • Dolly :precondition :effect (and (not (garbage)) (not (quiet)))

  4. Dinner Date example

  5. Dinner Date example

  6. Observation 1 p ¬q ¬r p q ¬q ¬r p q ¬q r ¬r p q ¬q r ¬r A A A B B Propositions monotonically increase (always carried forward by no-ops)

  7. Observation 2 p ¬q ¬r p q ¬q ¬r p q ¬q r ¬r p q ¬q r ¬r A A A B B Actions monotonically increase

  8. Observation 3 p q r … p q r … p q r … A Proposition mutex relationships monotonically decrease

  9. Observation 4 A A A p q r s … p q r s … p q r s … p q … B B B C C C Action mutex relationships monotonically decrease

  10. Observation 5 Planning Graph ‘levels off’. • After some time k all levels are identical • Because it’s a finite space, the set of literals never decreases and mutexes don’t reappear.

  11. Valid plan A valid plan is a planning graph where: • Actions at the same level don’t interfere • Each action’s preconditions are made true by the plan • Goals are satisfied

  12. GraphPlan algorithm • Grow the planning graph (PG) until all goals are reachable and not mutex. (If PG levels off first, fail) • Search the PG for a valid plan • If none is found, add a level to the PG and try again

  13. Searching for a solution plan • Backward chain on the planning graph • Achieve goals level by level • At level k, pick a subset of non-mutex actions to achieve current goals. Their preconditions become the goals for k-1 level. • Build goal subset by picking each goal and choosing an action to add. Use one already selected if possible. Do forward checking on remaining goals (backtrack if can’t pick non-mutex action)

  14. Plan Graph Search • If goals are present & non-mutex: • Choose action to achieve each goal • Add preconditions to next goal set

  15. Termination for unsolvable problems • Graphplan records (memoizes) sets of unsolvable goals: • U(i,t) = unsolvable goals at level i after stage t. • More efficient: early backtracking • Also provides necessary and sufficient conditions for termination: • Assume plan graph levels off at level n, stage t > n • If U(n, t-1) = U(n, t) then we know we’re in a loop and can terminate safely.

  16. Dinner Date example • Initial Conditions: (and (garbage) (cleanHands) (quiet)) • Goal: (and (dinner) (present) (not (garbage)) • Actions: • Cook :precondition (cleanHands) :effect (dinner) • Wrap :precondition (quiet) :effect (present) • Carry :precondition :effect (and (not (garbage)) (not (cleanHands)) • Dolly :precondition :effect (and (not (garbage)) (not (quiet)))

  17. Dinner Date example

  18. Dinner Date example

  19. Dinner Date example

  20. Knowledge Representation • Issue of what to put in to the knowledge base. • What does an agent need to know? • How should that content be stored?

  21. Knowledge Representation • NOT a solved problem • We have partial answers

  22. Question 1 • How do I organize the knowledge I have?

  23. Ontology • Basically a hierarchical organization of concepts. • Can be general or domain-specific.

  24. Question 2 • How do I handle categories?

  25. Do I need to? • What makes categories important?

  26. Defining a category • Necessary and sufficient conditions

  27. Think-Pair-Share • What is a chair?

  28. Prototypes

  29. In Logic • Are categories predicates or objects?

  30. Important Terms • Inheritance • Taxonomy

  31. What does ISA mean?

  32. Categories • Membership • Subset or subclass • Disjoint categories • Exhaustive Decomposition • Partitions of categories

  33. Other Issues • Physical composition • Measurement • Individuation • Count nouns vs. mass nouns • Intrinsic properties vs. extrinsic properties

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