1. CSE573 Autumn 1997 1 02/11/98 Planning Administrative –PS3 due 2/23 –Midterm Friday Last time –regression planning algorithm –introduction to plan-space (least-commitment) planning This time –plan-space least-commitment planning algorithm –midterm review

2. CSE573 Autumn 1997 2 World-State Orientation and Premature Commitment In state-space planning, there is close relationship between “states of the world” and “states in the search space” –in progression planning a search node corresponds to exactly one world state –in regression planning a search node corresponds to a set of world states This emphasis on states causes the premature commitment: the planner always needs to know what state(s) it is in, so actions have to be totally ordered, and all variables must be bound

3. CSE573 Autumn 1997 3 Searching in a space of partial plans Suppose we concentrate on “what we do” rather than on “where we are” –there is no explicit state, only some actions –the actions might be unordered with respect to each other, and might have unbound variables The planning process –the initial plan (search node) is one in which the agent does nothing –every iteration of the search adds more structure to the plan: actions, orderings, variable bindings –planning stops when the agent does enough to prove it achieves the goal every action’s precondition is true (established and not clobbered) every goal proposition is achieved

4. CSE573 Autumn 1997 4 An Representation Trick Since we’re talking about doing things rather than states, we transform the initial state and goal state description into “pseudo-actions” The action initial has no preconditions, and its postconditions are exactly what is true initially The action goal has the goal as a precondition, and no postconditions There is exactly one instance of initial in every plan, and it must be “executed” first There is exactly one instance of goal in every plan, and it must be “executed” last

5. CSE573 Autumn 1997 5 Plan Space Example: Initial Plan Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) One node in the search space Open preconditions in red

6. CSE573 Autumn 1997 6 The initial choice Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) What open precondition should I try to satisfy? How should I satisfy it? with an existing step in the plan by adding a new step

7. CSE573 Autumn 1997 7 A Single Refinement Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) A causal link from initial to goal fuel(FULL) is true initially and no action in the plan can change it

8. CSE573 Autumn 1997 8 A Second Refinement: Adding a Step Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) travel(?x, B, ?l) at(truck, ?x) connected(?x, B) fuel(?l) (?l ~= EMPTY) +at(truck, B) +fuel(EMPTY) -at(truck, A) -fuel(?l) Note the unbound variables (least-commitment) There is a potential threat to the fuel(FULL) link

9. CSE573 Autumn 1997 9 Three more refinements Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) travel(A, B, FULL) at(truck, A) connected(A, B) fuel(FULL) +at(truck, B) +fuel(EMPTY) -at(truck, A) -fuel(FULL) All variables are bound No open conditions But an actual threat

10. CSE573 Autumn 1997 10 The three ways of resolving a threat P(A) Producer P(A) Consumer ~P(?x) Clobberer Promotion: order Clobberer after Consumer Demotion: order Clobberer before Producer Separation: constrain ?x’s binding, ?x ~= A

11. CSE573 Autumn 1997 11 Alternate Choice: Adding a Second Operator Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) travel(A, B, FULL) at(truck, A) connected(A, B) fuel(FULL) +at(truck, B) +fuel(EMPTY) -at(truck, A) -fuel(FULL) refuel(B) at(truck, B) refuel-at(B) +fuel(FULL) -fuel(EMPTY) -fuel(HALF) At this point, travel and refuel are unordered with respect to each other.

12. CSE573 Autumn 1997 12 Resolving the threat by demotion Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(FULL) Goal at(TRUCK,B) fuel(FULL) travel(A, B, FULL) at(truck, A) connected(A, B) fuel(FULL) +at(truck, B) +fuel(EMPTY) -at(truck, A) -fuel(FULL) refuel(B) at(truck, B) refuel-at(B) +fuel(FULL) -fuel(EMPTY) -fuel(HALF)

13. CSE573 Autumn 1997 13 A Solution: No Open Conditions or Threats Initial at(TRUCK,A) connected(A,B) refuel-at(B) fuel(HALF) Goal at(TRUCK,B) fuel(FULL) travel(A, B, FULL) at(truck, A) connected(A, B) fuel(FULL) +at(truck, B) +fuel(EMPTY) -at(truck, A) -fuel(FULL) refuel(B) at(truck, B) refuel-at(B) +fuel(FULL) -fuel(?z)

14. CSE573 Autumn 1997 14 The POCL (SNLP/McAllester&Rosenblitt) Algorithm Initialize P  the NULL plan Solution check: if P has no flaws, succeed –a flaw is an open condition or potential threat Otherwise –Choose some flaw in P, F –Choose some method to repair F; if none, fail if F is an open condition, methods are the existing plan steps or any operator that asserts the condition can be added to the plan if F is a threat, then promote, demote, or separate

15. CSE573 Autumn 1997 15 Formal properties of POCL Planners Soundness: if the algorithm returns a plan, that plan is a solution to the planning problem. Completeness: if there is a solution plan, the algorithm will eventually find it –assuming a complete search algorithm Systematicity: the algorithm will consider each partial plan only once

16. CSE573 Autumn 1997 16 Midterm Review Material covered: through today’s lecture –introductory material on AI, intelligence, agents –reactive agent and Lisp programming –search optimizing versus satisficing informed versus blind single-agent versus adversarial –planning planning as search STRIPS operators, states and state descriptions POCL planning Reading for midterm –Chapter 1, skim –Chapter 2 as required –Chapter 4, skip 4.4, 4.6 –Chapter 7, 7.1 and 7.2 only