(Classical) AI Planning
-
Upload
adena-sanders -
Category
Documents
-
view
35 -
download
2
description
Transcript of (Classical) AI Planning
![Page 1: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/1.jpg)
(Classical)AI Planning
![Page 2: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/2.jpg)
Some Examples
• 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.
Which of the following problems can be modeled as AI planning problems?
Answer: ALL!
![Page 3: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/3.jpg)
General Purpose vs. Domain-Specific
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 semanticsDisadvantage: - symbolic description requirement
Advantage: - can be very efficientDisadvantage: - lack of clear semantics - knowledge-engineering for plan generation
Planning: find a sequence of actions to achieve a goal
![Page 4: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/4.jpg)
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)
A
C
B C
B
AInitial state Goals
(Ke Xu)
![Page 5: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/5.jpg)
General-Purpose Planning: Operators
?y
?x
No block on top of ?x
transformation
?y
?x…
…
No block on top of ?y nor ?x
Operator: (Unstack ?x)• Preconditions: (on ?x ?y) (clear ?x) • Effects:
– Add: (on ?x table) (clear ?y)– Delete: (on ?x ?y)
On table
![Page 6: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/6.jpg)
Planning: Search Space
AC
B A B C A CB
CBA
BA
C
BAC
B CA
CAB
ACB
BCA
A BC
AB
C
ABC
(Michael Moll)
![Page 7: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/7.jpg)
Classes of General-Purpose Planners
General purpose planners can be classified according to the space where the search is performed:
• SAT
• Hierarchical
• Disjunctive plans
• state
• plan We are going to discuss these forms
![Page 8: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/8.jpg)
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)
![Page 9: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/9.jpg)
Hierarchical (HTN) Planning
Travel(UMD, Lehigh)
Fly(National, L.V. International)Travel(L.V. Int’nal,Lehigh)
Travel(UMD,National)
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.
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.
alternativemethods
Travel from UMD to Lehigh University
Travel by car
Enough money for gasoline
Roads are passable Seats available
Travel by plane
Enough money for air fare available
Taxi(UMD,UMD-Metro)
Metro(UMD-Metro,National)
Taxi(L.V. Int’nal,Lehigh)
![Page 10: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/10.jpg)
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 increasesthe number of nodes exponentially– worst case: about 6x1044 leaf nodes– average case: about 1024 leaf nodes
Not enough time to search the game tree
(Dana S. Nau)
![Page 11: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/11.jpg)
How to Reduce the Sizeof 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)
(Dana S. Nau)
![Page 12: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/12.jpg)
• 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
Universal Classical Planning (UCP) (Khambampati, 1997)
Plan-space
partially instantiated steps, plus constraints
add steps & constraints
State-space
![Page 13: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/13.jpg)
Abstract Example
Initialstate
finalstate
Initial plan:
Plan-spacerefinement
State-spacerefinement
Plan-spacerefinement
State-spacerefinement
![Page 14: (Classical) AI Planning](https://reader036.fdocuments.in/reader036/viewer/2022082407/56812b92550346895d8fae6f/html5/thumbnails/14.jpg)
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