Artificial Intelligence II

1. Artificial Intelligence II S. Russell and P. Norvig Artificial Intelligence: A Modern Approach Chapter 11: Planning

2. Planning • Planning versus problem solving • Situation calculus • Plan-space planning

3. Planning as Problem Solving • Planning • Start state (S) • Goal state (G) • Set of actions • Find sequence of actions that get you from start to goal

4. What is Planning (more generally)? • Generate sequences of actions to perform tasks and achieve objectives. • Search for solution over abstract space of plans. • Assists humans in practical applications • design and manufacturing • military operations • games • space exploration

5. Planning versus Problem Solving • Problem solving is basically state-space search, i.e., primarily an algorithm—not a representation. • initial state, goal-test, successor function • Planning is a combination of an algorithm (search) and a set of representations, usually situational calculus. • provides a way to “open up” the representation of initial state, goal-test, and successor function • planner free to add actions to plan wherever needed • most parts of world independent from other parts, i.e. nearly decomposable

6. Assumptions • Atomic time • No concurrent actions • Deterministic actions • Agent is sole cause of change • Agent is omniscient • Closed World Assumption

7. Simple Planning Agent Algorithm • Generate a goal to achieve • Construct a plan to achieve goal from current state • Execute plan until finished • Begin again with new goal

8. Planning Emphasizes what’s inside the goal and operators • “Open up” the representation of state, goals and operators • State/Situation Representation: A conjunction of “facts” • Ex: pyramid(C), on(A,B), on(B,Table), clear(A), handempty • Goal Description: a conjunction of positive literals. • Ex: on(A,B), on(B,C) • Operators: given as “STRIPS-type” if-then rules. STRIPS is the STanford Research Institute Problem Solver. • Action name: Description of action • Preconditions: Conjunction of positive literals representing what must be true beforehand. • Effects: Conjunction of positive or negative literals • Name: pickup(x) • Preconditions: ontable(x), clear(x), handempty • Add: holding(x) • Del: ontable(x), clear(x), handempty

9. Representing States of the World C A B on(c,a),ontable(a),clear(c),ontable(b),clear(b),handempty() Goal: on(b,c)

10. Strips Example • Action • Buy(x, store) • Pre: At(store), Sells(store, x) • Eff: Have(x) • Go(x, y) • Pre: At(x) • Eff: At(y), notAt(x) • Goal • Have(Milk) and Have(Banana) and Have(Drill) • Start • At(Home) and Sells(SM, Milk) and Sells(SM, Banana) and Sells(HW, Drill)

11. Planning as Search • Situation-Space Search • Plan-Space Search

12. Situation-Space Search • Search space: All possible world states or situations. • Initial state defines one node. • Goal node is a state where all goals in goal state are satisfied. • Solution: Sequence of actions in path from start to goal.

13. Plan-Space Search • Search space: All possible (partial) plans • Partial plan defines a node; an initial plan is one node • Goal node contains a plan which is complete, satisfying all goals in goal state • The node itself contains all of the information for determining a solution plan

14. At(Home)… Go(SM) Go(HW) … Planning Algorithms • Progression planners (forward chaining): consider the effect of all possible actions in a given state. • Regression planners (backword chaining):to achieve a goal, what must have been true in previous state. Have(M) and Have(B) and Have(D) Buy(M,store) At(store) and Sells(store,M) and Have(B) and Have(D) • Both have problem of lack of direction – what action or goal to pursue next.

15. Forward and Backward State-Space Search

16. Algorithm for Progression-Based Planner 1. Start from initial state 2. Find all operators whose preconditions are true in the initial state 3. Compute effects of operators to generate successor states 4. Repeat 2 and 3 until new state satisfies goal conditions

17. A B C Progression Planning Example • Initial State S = {clear(A), clear(B), clear(C), ontable(A), ontable(B), ontable(C), handempty} • Operator = pickup(x) with  = {x/A} • Successor state T = {clear(B), clear(C), ontable(B), ontable(C), holding(A)} • Operator instance associated with the action from S to T is pickup(A). • Name: pickup(x) • Preconditions: ontable(x), clear(x), handempty • Add: holding(x) • Del: ontable(x), clear(x), handempty

18. Algorithm for Regression-Based Planner 1. Start with goal node corresponding to goal to be achieved 2. Choose an operator that will add one of the goals 3. Replace the goal with the operator’s preconditions 4. Repeat 2 and 3 until you have reached the initial state

19. STRIPS Algorithm • A Goal-Stack Based Regression Planner: uses goal stack to maintain set of conjunctive goals yet to be achieved • Maintains a description of the current state, which is initially the given initial state • Approach: • Pick an order for achieving each of the goals • When each goal is popped from stack, if it is not already true in current state, push onto the stack an operator that adds that goal. • Push onto the stack each precondition of that operator in some order, and solve each of these sub-goals in the same fashion. • When all preconditions are solved (popped from stack), re-verify that all of the preconditions are still true and apply the operator to create a new successor state description.

20. A C C B A STRIPS Example Pickup(x) P: ontable(x), clear(x), handempty E: holding(x), not ontable(x), notclear(x), not handempty Putdown(x) P: holding(x) E: ontable(x), clear(x), handempty, not holding(x) Stack(x,y) P: holding(x), clear(y) E: on(x,y), clear(x), handempty, not holding(x), not clear(y) Unstack(x,y) P: clear(x), on(x,y), handempty E: holding(x), clear(y), not clear(x), not on(x,y), not handempty B Goal: on(a,c), on(c,b) Initial State: clear(b), clear(c), on(c,a), ontable(a), ontable(b), handempty

21. STRIPS Example Goal Stack = [achieve(on(C,B), on(A,C))] State = {clear(B), clear(C), on(C,A), ontable(A), ontable(B), handempty}

22. STRIPS Example Goal Stack = [achieve(on(C,B)), achieve(on(A,C)), achieve(on(C,B), on(A,C))] State = {clear(B), clear(C), on(C,A), ontable(A), ontable(B), handempty}

23. STRIPS Example Goal Stack = [achieve(clear(B),holding(C)), apply(Stack(C,B), achieve(on(A,C)), achieve(on(C,B), on(A,C))] State = {clear(B), clear(C), on(C,A), ontable(A), ontable(B), handempty} Stack(x,y) P: holding(x), clear(y) E: on(x,y), clear(x), handempty, not holding(x), not clear(y)

24. STRIPS Example Goal Stack = [achieve(holding(C)), achieve(clear(B)), achieve(clear(B),holding(C)), apply(Stack(C,B), achieve(on(A,C)), achieve(on(C,B), on(A,C))] State = {clear(B), clear(C), on(C,A), ontable(A), ontable(B), handempty}

25. STRIPS Example Goal Stack = [achieve(handempty, clear(C), on(C,y)), apply(Unstack(C,y)), achieve(clear(B)), achieve(clear(B),holding(C)), apply(Stack(C,B), achieve(on(A,C)), achieve(on(C,B), on(A,C))] State = {clear(B), clear(C), on(C,A), ontable(A), ontable(B), handempty} Unstack(x,y)P: clear(x), on(x,y), handempty E: holding(x), clear(y), not clear(x), not on(x,y), not handempty

26. STRIPS Example Goal Stack = [achieve(clear(B)), achieve(clear(B),holding(C)), apply(Stack(C,B), achieve(on(A,C)), achieve(on(C,B), on(A,C))] State = {clear(B), ontable(A), ontable(B), holding(C), clear(A)} Plan so far = [Unstack(C,A)] Unstack(x,y)P: clear(x), on(x,y), handempty E: holding(x), clear(y), not clear(x), not on(x,y), not handempty

27. STRIPS Example Goal Stack = [achieve(on(A,C)), achieve(on(C,B), on(A,C))] State = {ontable(A), ontable(B), clear(A), on(C,B), clear(C), handempty} Plan so far = [Unstack(C,A), Stack(C,B)] Stack(x,y) P: holding(x), clear(y) E: on(x,y), clear(x), handempty, not holding(x), not clear(y)

28. STRIPS Example Goal Stack = [achieve(clear(C), holding(A)), apply(Stack(A,C)), achieve(on(C,B), on(A,C))] State = {ontable(A), ontable(B), clear(A), on(C,B), clear(C), handempty} Plan so far = [Unstack(C,A), Stack(C,B)] Stack(x,y) P: holding(x), clear(y) E: on(x,y), clear(x), handempty, not holding(x), not clear(y)

29. STRIPS Example Goal Stack = [achieve(holding(A)), achieve(clear(C)), achieve(clear(C), holding(A)), apply(Stack(A,C)), achieve(on(C,B), on(A,C))] State = {ontable(A), ontable(B), clear(A), on(C,B), clear(C), handempty} Plan so far = [Unstack(C,A), Stack(C,B)]

30. STRIPS Example Goal Stack = [achieve(ontable(A), clear(A), handempty), apply(Pickup(A)), achieve(clear(C)), achieve(clear(C), holding(A)), apply(Stack(A,C)), achieve(on(C,B), on(A,C))] State = {ontable(A), ontable(B), clear(A), on(C,B), clear(C), handempty} Plan so far = [Unstack(C,A), Stack(C,B)] Pickup(x) P: ontable(x), clear(x), handempty E: holding(x), not ontable(x), not clear(x), not handempty

31. STRIPS Example Goal Stack = [achieve(clear(C)), achieve(clear(C), holding(A)), apply(Stack(A,C)), achieve(on(C,B), on(A,C))] State = {ontable(B), on(C,B), clear(C), holding(A)} Plan so far = [Unstack(C,A), Stack(C,B), Pickup(A)] Pickup(x) P: ontable(x), clear(x), handempty E: holding(x), not ontable(x), not clear(x), not handempty

32. STRIPS Example Goal Stack = [achieve(on(C,B), on(A,C))] State = {ontable(B), on(C,B), on(A,C), clear(A), handempty} Plan so far = [Unstack(C,A), Stack(C,B), Pickup(A), Stack(A,C)] Stack(x,y) P: holding(x), clear(y) E: on(x,y), clear(x), handempty, notholding(x), not clear(y)

33. STRIPS Example Goal Stack = [] State = {ontable(B), on(C,B), on(A,C), clear(A), handempty} Plan so far = [Unstack(C,A), Stack(C,B), Pickup(A), Stack(A,C)]