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Planning

Planning. Planning is a special case of reasoning We want to achieve some state of the world Typical example is robotics. Many thanks to Robin Burke, University of Chicago, for many of these ideas: http://people.cs.uchicago.edu/~burke/cs250/lectures/lecture1107.html. Search vs. Planning.

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Planning

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  1. Planning • Planning is a special case of reasoning • We want to achieve some state of the world • Typical example is robotics Many thanks to Robin Burke, University of Chicago, for many of these ideas:http://people.cs.uchicago.edu/~burke/cs250/lectures/lecture1107.html

  2. Search vs. Planning

  3. Example: Taking a Bath • Goal: Take A Bath • Initial State: • Final State:

  4. Actions you take that affect state Examples: RemoveClothes PutOnClothes TurnOnWater TurnOffWater GetInTub GetOutOfTub Can use search to find the right combination of actions InsertPlug RemovePlug Scrub Lather PourShampoo PlayWithDuckie Operators

  5. Problems with Search • Need a good heuristic – may not have one • No direct sense of which operators are relevant to the problem • Other constraints • Don’t want solutions where we take off clothes and put them on again several times • Search might find one • There is a directionality to what we want to do • Once the plug is in, we don't even want to consider removePlug • Blind search doesn't let us make these distinctions

  6. How does planning improve on search? • Explicitly represent the goal and the way it is achieved using logic • Make "important" decisions first and fill in the details later • Take advantage of independence • Puzzles are puzzles because subgoals are interlocking (cannot do each tile of 8-puzzle separately) • The real world is more forgiving in many cases: taking off my clothes will not affect the state of the plug in the tub • Allows divide and conquer strategies

  7. STRIPS Planning • STRIPS = STanford Research Institute Problem Solver • one of the first planning systems • one of the first formalisms • considerably extended now, but still in use • an entire subfield of planning is planning using the STRIPS representation • good place to start

  8. STRIPS Representation • States: Conjunctions of predicates applied to constants • Goals: Conjunctions of predicates applied to constants or simple variables

  9. STRIPS Representation • Actions: Get agent from one state to another • Action description: What to do, e.g. forward, vacuum, etc. • Precondition: what must be true to do action • PlugIn(Tub) would be a precondition for a Fill(Tub) action • Effect: what changes occur due to action • Full(Tub) would be effect of Fill(Tub)

  10. Action Example • Precondition automatically refers to situation before, effects to situation after • Negation = retraction • Actions can have variables

  11. Integrating Search • Still going to need to do some seaching • Could go forward from initial state • Progressive • High branching factor problem • Backward from goal • Regressive • Typically goals are conjunctive, works much like backward chaining

  12. The STRIPS Planner • Start with a goal • TakingBath(Robot) • Need to know what conditions achieve this goal for backward chaining • Each conjunct is called a sub-goal • Need to achieve all subgoals • Want to know steps on how you got there

  13. Algorithm • Start with goal • See if achieved already • If not, see if there’s some operator that can achieve it • Unclothed(x) • In KB, I have Clothed(Robot) • Unclothed(Robot) is achieved with the plan Undress(Robot) • If no such operator, try and decompose by backward chaining • If unable to do so, fail and backtrack

  14. Full Example • Start with goal • Unclothed(Robot) & Full(Tub) & In(Robot,Tub) & Plugged(Tub) • Operator Undress(x): • Precond: Clothed(x) • Effect: Unclothed(x) & ~Clothed(x) • So plan is, for starters, • Undress(Robot) • Remaining goals: • Full(Tub) & In(Robot, Tub) & Plugged(Tub) • Next subgoal: Full(Tub)

  15. Example continued • We have an operator Fill(x) • Precond: Plugged(x) • Effect: Full(x) & ~Empty(x) • So backward chain over precondition • Try operator Plug(x) • Precond: Unplugged(x) • Effect: Plugged(x) & ~Unplugged(x) • So add it to plan: • Undress(Robot), Plug(Tub)

  16. Example continued • Now that precondition of Fill satisfied, add to plan • Plan: Undress(Robot), Plug(Tub), Fill(Tub) • Remaining goals: In(Robot,Tub) & Plugged(Tub) • Use operator GetIn(x,y) to get in tub • Plan: Undress(Robot), Plug(Tub), Fill(Tub), GetIn(Robot,Tub) • Remaining goals: Plugged(Tub) – it’s true

  17. What’s the catch? • Test the solution at the end: You may clobber a condition along the way • E.g. You have to leave bathroom to fill tub (get dressed) • Done by keeping track of threats • Unclothed(Robot) is a precondition for final goal • Do not allow an operator which inserts Clothed(Robot) between Undress(Robot) and goal

  18. Actual algorithm • Select subgoal state Sneed to resolve • Choose operator c to resolve it • Record link from previous state to final state

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