1. SNS College of Engineering Department of Computer Science and Engineering AI Planning Presented By S.Yamuna AP/CSE
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2. General-Purpose Planning: State & Goals
Initial state: (on A Table) (on C A) (on B Table) (clear B) (clear C)
Goals: (on C Table) (on B C) (on A B) (clear A)
Initial state
Goals C B A B C
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3. General-Purpose Planning: Operators
No block on top of ?x
No block on top of ?y nor ?x ?x ?y
transformation ?y ?x … …
On table
Operator: (Unstack ?x)
Preconditions: (on ?x ?y) (clear ?x)
Effects:
Add: (on ?x table) (clear ?y)
Delete: (on ?x ?y)
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4. Planning: Search SpaceB C A B C B A B A C B A C B A B C C B A B A C A C A A B C B C C B A A B C
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5. Some Examples
Which of the following problems can be modeled as AI planning problems?
Route search: Find a route between Lehigh University and the Naval Research Laboratory
Project management: Construct a project plan for organizing an event (e.g., the Musikfest)
Military operations: Develop an air campaign
Information gathering: Find and reserve an airline ticket to travel from Newark to Miami
Game playing: plan the behavior of a computer controlled player
Resources control: Plan the stops of several of elevators in a skyscraper building.
Answer: ALL!
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6. FSM vs AI Planning FSM: Planning Operators A resulting plan:
Patrol
Preconditions:
No Monster
Effects:
patrolled
Fight
Monster in sight
Patrol
Fight
Monster In Sight
No Monster
FSM:
A resulting plan:
Patrol
patrolled
Fight
No Monster
Monster in sight
Neither is more powerful than the other one
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7. But Planning Gives More Flexibility
“Separates implementation from data” --- Orkin
reasoning
knowledge
Many potential plans:
Patrol
Fight …
Planning Operators
Patrol
Preconditions:
No Monster
Effects:
patrolled
Fight
Monster in sight …
If conditions in the state change making the current plan unfeasible: replan!
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8. But… Does Classical Planning Work for Games?
F.E.A.R. not!
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9. General Purpose vs. Domain-Specific
Planning: find a sequence of actions to achieve a goal
General purpose: symbolic descriptions of the problems and the domain. The plan generation algorithm the same
Domain Specific: The plan generation algorithm depends on the particular domain
Advantage: opportunity to have clear semantics
Disadvantage: - symbolic description requirement
Advantage: can be very efficient
Disadvantage: - lack of clear semantics
- knowledge-engineering for plan generation
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10. Classes of General-Purpose Planners
General purpose planners can be classified according to the space where the search is performed:
We are going to discuss these forms
Hierarchical
Disjunctive plans
state
plan
SAT
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11. State- and Plan-Space Planning
State-space planners transform the state of the world. These planners search for a sequence of transformations linking the starting state and a final state
State of the world
(total order)
Plan-space planners transform the plans. These planners search for a a plan satisfying certain conditions
(partial-order, least-commitment)
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12. Why Plan-Space Planning?
1. Motivation: “Sussman Anomaly”
Two subgoals to achieve:
(on A B) (on B C) A C B A B C
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13. Why Plan-Space Planning?
Problem of state-space search:
Try (on A B) first:
put C on the Table, then put A on B
Accidentally wind up with A on B when B is still on the Table
We can not get B on C without taking A off B
Try to solve the first subgoal first appears to be mistaken A A B C A B C B C
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14. Hierarchical (HTN) Planning
Principle: Complex tasks are decomposed into simpler tasks. The goal is to decompose all the tasks into primitive tasks, which define actions that change the world.
Travel(UMD, Lehigh)
Travel from UMD to Lehigh University
Fly(National, L.V. International)
Travel(L.V. Int’nal,Lehigh)
Travel(UMD,National)
Seats available
Travel by plane
Enough money for air
fare available
Travel by car
Enough money
for gasoline
Roads are passable
Taxi(UMD,UMD-Metro)
Metro(UMD-Metro,National)
alternative
methods
Taxi(L.V. Int’nal,Lehigh)
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15. Application to Computer Bridge
Chess: better than all but the best humans
Bridge: worse than many good players
Why bridge is difficult for computers
It is an imperfect information game
Don’t know what cards the others have (except the dummy)
Many possible card distributions, so many possible moves
If we encode the additional moves as additional branches in the game tree, this increases the number of nodes exponentially
worst case: about 6x1044 leaf nodes
average case: about 1024 leaf nodes
Not enough time to search the game tree
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16. How to Reduce the Size of the Game Tree?
Bridge is a game of planning
Declarer plans how to play the hand by combining various strategies (ruffing, finessing, etc.)
If a move doesn’t fit into a sensible strategy, then it probably doesn’t need to be considered
HTN approach for declarer play
Use HTN planning to generate a game tree in which each move corresponds to a different strategy, not a different card
Reduces average game-tree size to about 26,000 leaf nodes
Bridge Baron: implements HTN planning
Won the 1997 World Bridge Computer Challenge
All commercial versions of Bridge Baron since 1997 have include an HTN planner (has sold many thousands of copies)
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17. Universal Classical Planning (UCP) (Khambampati, 1997)
Loop:
If the current partial plan is a solution, then exit
Nondeterministically choose a way to refine the plan
Some of the possible refinements
Forward & backward state-space refinement
Plan-space refinement
Hierarchical refinements
partially instantiated steps, plus constraints
add steps & constraints
State-space
Plan-space
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18. Abstract Example Initial plan: Plan-space refinement Initial final
state
final
state
State-space
refinement
Plan-space
refinement
State-space
refinement
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19. Why “Classical”? Classical planning makes a number of assumptions:
Symbolic information (i.e., non numerical)
Actions always succeed
The “Strips” assumption: only changes that takes place are those indicated by the operators
Despite these (admittedly unrealistic) assumptions some work-around can be made (and have been made!) to apply the principles of classical planning to games
Neoclassical planning removes some of these assumptions
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20. THANK Y OU
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