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Chap.12 Practical Planning

Chap.12 Practical Planning. CS570 Artificial Intelligence Kwang-hyung Lee. 12.1 Practical Planners. 12.1 Practical Planners. Spacecraft assembly, integration, and verification 1. Hierarchical plans 2. Complex conditions 3. Time 4. Resources Job Shop Scheduling

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Chap.12 Practical Planning

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  1. Chap.12 Practical Planning CS570 Artificial Intelligence Kwang-hyung Lee

  2. 12.1 Practical Planners 12.1 Practical Planners • Spacecraft assembly, integration, and verification 1. Hierarchical plans 2. Complex conditions 3. Time 4. Resources • Job Shop Scheduling • Scheduling for space missions • Buildings, aircraft carriers and beer factories

  3. 12.2 Hierarchical Decomposition 12.2 Hierarchical Decomposition • Solution at a high level abstraction [Go(Supermarket),Buy(Milk),Buy(Bananas),Go(Home)] It is a long way from instruction fed to the agent’s effectors • A low level plan [Forward(1 cm),Turn(1 deg),Forward(1 cm), ……] • Hierarchical decomposition : an abstract operator can be decomposed into a group of steps ex) Abstract operator: Build(House) decomposed operators : obtain Permit,Hire Builder,Construction, Pay Builder • Primitive operator:executed by the agent

  4. 12.2 Hierarchical Decomposition • Hierarchical planning work (1) provide an extension to the STRIPS for nonprimitive operator (2) modify the planning algorithm to allow the replacement of a nonprimitive operator with its decomposition

  5. 12.2 Hierarchical Decomposition *Extending STRIPS (1) partition operators into primitive and nonprimitive operators nonprimitive : Install(FloorBoards) primitive : Hammer(Nail) (2) decomposition method Decompose(o,p) : An operator o is decomposed into a plan p

  6. 12.2 Hierarchical Decomposition • Decomposition of o into p The decomposed plan p correctly implements an operator if it is complete and consistent : 1. p must be consistent (no contradiction) 2. Every effect of o must be asserted by at least one step of p 3. Every precondition of the steps in p must be achieved by a step in p or be one of the preconditions of o

  7. 12.2 Hierarchical Decomposition *Modifying the planner • Modification of planner POP into HD-POP (1) a way to decompose nonprimitive operators (2) the algorithm takes a plan as input, rather than just a goal

  8. 12.2 Hierarchical Decomposition • SELECT-NONPRIMITIVE:selects a nonprimitive • CHOOSE-DECOMPOSITION:picks a decomposition method • The fields of the plan are altered : • STEPS :Add steps, remove Snonprimitive • BINDINGS :Add variable binding constants • Ordering:Call RESOLVE-THREATS • Links: Sic Snonprim Sic Sm : a step of method

  9. 12.3 Analysis of Hierarchical Decomposition 12.3 Analysis of Hierarchical Decomposition • Abstract solution : a plan containing abstract operators, but consistent and complete • downward solution:if p is an abstract solution and there is a primitive solution • upward solution:if an abstract plan is inconsistent then no primitive sol.

  10. 12.3 Analysis of Hierarchical Decomposition • if a planner(nonhierarchical) has to generate n-step plan(where b is branching factor), it takes time O(bn) • Hierarchical planning, sb steps at d=1 bs2 at d=2 ibs2 = O(bsd) (from i=1 to d)

  11. 12.3 Analysis of Hierarchical Decomposition • The Gift of the Magic • A poor couple:he has a gold watch, she has long hair. • Plan b is inconsistent , but it can be into a consistent plan • The upward solution property does not hold

  12. 12.3 Analysis of Hierarchical Decomposition *Decomposition and Sharing • Merge each step of the decomposition into existing plan • Divide-and-conquer approach:solve each subproblem and then combine it into the rest • Sharing steps while merging • Ex) enjoy a honeymoon and raise a baby (1) decomposition • get married and go on honeymoon • get married and have a baby (2) merge • share the step “get married”

  13. 12.3 Analysis of Hierarchical Decomposition *Decomposition and approximation • Hierarchical decomposition nonprimitive operator => primitives • Hierarchical planning(approximation hierarchy, abstraction hierarchy) • It takes an operator and partitions its precondition according to their criticality levelOp(ACTION:Buy(x), EFFECT : Have(x)  Have(Money), PRECOND:1:Sells(store,x)  2:At(store)  3:Have(Money))

  14. 12.4 More Expressive Operator Description 12.4 More Expressive Operator Description *Conditional effects • ex) block world in section 11.8 Two operators were needed Op(ACTION:Move(b,x,y),PRECOND : On(b,x)  Clear(b)  Clear(y), EFFECT:On(b,y)  Clear(x)  On(b,x)  Clear(y)) Op(ACTION:MoveToTable(b,x),PRECOND : On(b,x)  Clear(b), EFFECT:On(b,Table)  Clear(x)  On(b,x)) • initial situation:On(A,B)goal :clear(B)

  15. 12.4 More Expressive Operator Description • Move A to the table or to somewhere else? : premature commitment in Move(b,x,y) • To eliminate it, we include conditional effect“effect when condition” : Q when POp(ACTION:Move(b,x,y),PRECOND : On(b,x)  Clear(b)  Clear(y), EFFECT:On(b,y)  Clear(x)  On(b,x)  Clear(y) when yTable)

  16. 12.4 More Expressive Operator Description *Universal quantification • ex) block world clear(b)x Block(x)  On(x,b) • ex) shopping problem Carry(bag, x, y) : (effect) all objects that are in the bag are at y and are no longer at x.Op(ACTION:Carry(bag,x,y),PRECOND:Bag(bag)  At(bag,x), EFFECT:At(bag,y)  At(bag,x)  I Item(i)  (At(i,y)  At(y) when In(I,bag))

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