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Lecture 21 State-Space Search vs. Constraint-Based Planning. CSE 573 Artificial Intelligence I Henry Kautz Fall 2001. Road Map. Today Plan graphs Planning as state space search Comparison of the two approaches. Graphplan. Planning as graph search (Blum & Furst 1995) - PowerPoint PPT Presentation
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CSE 573 1
Lecture 21State-Space Search vs. Constraint-
Based Planning
CSE 573Artificial
Intelligence IHenry Kautz
Fall 2001
CSE 573 2
Road Map
• Today• Plan graphs• Planning as state space search• Comparison of the two approaches
CSE 573 3
Graphplan
Planning as graph search (Blum & Furst 1995)Set new paradigm for planning
Like SATPLAN...• Two phases: instantiation of propositional
structure, followed by searchUnlike SATPLAN...
• Interleaves instantiation and pruning of plan graph
• Employs specialized search engineGraphplan - better instantiationSATPLAN - better search
CSE 573 4
Graph Pruning
Graphplan instantiates in a forward direction, pruning unreachable nodes • conflicting actions are mutex• if all actions that add two facts are mutex, the facts
are mutex• if the preconditions for an action are mutex, the
action is unreachable!
In logical terms: limited application of resolution where one clause is negative binary• given: P V Q, P V R V S V ...• infer: Q V R V S V ...
CSE 573 5
The Plan Graph
Facts FactsActions
... ...
Facts FactsActions
... ...preconditions
mutually exclusive
add effectsdelete effects
CSE 573 6
The Plan Graph
Facts FactsActions
... ...
Facts FactsActions
... ...preconditions
CSE 573 7
The Plan Graph
Facts FactsActions
... ...
Facts FactsActions
... ...preconditions add effects
delete effects
CSE 573 8
Translation of Plan Graph
Fact Act1 Act2Act1 Pre1 Pre2
¬Act1 ¬Act2
Act1
Act2
Fact
Pre1
Pre2
CSE 573 9
Improved Encodings
Translations of Logistics.a:STRIPS Axiom Schemas SAT
(Medic system, Weld et. al 1997)• 3,510 variables, 16,168 clauses• 24 hours to solve
STRIPS Plan Graph SAT(Blackbox)• 2,709 variables, 27,522 clauses• 5 seconds to solve!
CSE 573 10
Blackbox = Reachability + Satisfiability
• Blackbox Planner (Kautz 1997) uses the first part of Graphplan (reachability analysis) to determine which propositions to instantiate
• Then formula is generated (up to a bounded length K) and checked for SAT
– can specify Walksat, various kinds of DP– current best: CHAFF (version DP)– can also run Graphplan on reachability graph for a
few seconds to catch “easy” cases
• If a solution found, then model is translated back to a parallel plan
• Else max length K is incremented, and repeat
CSE 573 11
Results: Logistics Planning
> 24 hours28 seclogistics.d
> 24 hours9 seclogistics.c
13 minutes7 seclogistics.b
31 minutes5 seclogistics.a
55 sec5 secrocket.b
GraphplanBlackbox
CSE 573 12
How Well Does it Work?1992 – first incarnation of SATPLAN (Kautz & Selman),
competitive with other planners (UCPOP) at the time1995 – Graphplan (Blum & Furst) best planning algorithm
– Constraint-satisfaction style solver, but no explicit translation to SAT
– Blew everything previous out of the water!1996 – SATPLAN with new SAT solvers (walksat+new local
search heuristics, satz-rand, etc.)– competitive with Graphplan – sometimes much faster – but
requires hand-written axioms1998 – Debut of Blackbox
– Generates axioms automatically from STRIPS operators– Beats Graphplan when size & cost of generating formula
small compared to graph search cost– Some domains kill it by blowing up size of formula:
Blocks World, “Gripper”– Overall “winners” at AIP-98 competition were all constraint-
based approaches (variants of SATPLAN and Graphplan)
CSE 573 13
AIPS-2000
Another planning competition at the AI and Planning Systems Conference 2000 provided a big surprise:
• Fastest planners were all based on A* search!• Heuristics derived automatically from STRIPS
encodingIssues:
• How to derive a search heuristic• How does A* really compare with constraint-
based planning (Graphplan / SATPLAN / Blackbox)?
CSE 573 14
Planning as A* Search
Simple formulation:• State = node in search tree• Action = arc in search tree• Distance to goal = number of actions in
plan • Note: purely sequential plans (no
parallelism)Search heuristic: estimate of distance to goal
• How to estimate? Ideas?
CSE 573 15
Search Heuristics
1. Count number of false goal propositions in current state
Admissible?2. Delete all preconditions from actions, solve
easy relaxed problem, use lengthAdmissible?
3. Delete negative effects from actions, solve easier relaxed problem, use length
Admissible?
CSE 573 16
AIPS-2000 Planning Competition
Fast-Forward (FF)• Joerg Hoffmann & Bernhard Nebel
(Albert-Ludwigs-University Freiburg, Germany)• “Delete negative effects” heuristic• Competed in fully automated track of the 2nd
International Planning Systems Competition (AIPS 2000 conference in Breckenridge, CO)
– Granted ``Group A distinguished performance Planning System'‘
– Schindler Award for the best performing planning system in the Miconic 10 Elevator domain
AIPS 2002 – Toulouse, FranceNow, don’t you wish you were doing research on planning?
CSE 573 17
BB vs FF
problem BB FFtime flights time flights
log-a 1.20 (3,4) 0.08 (4,0)
log-b 2.06 (4,2) 0.09 (5,0)
log-c 3.08 (4,5) 0.09 (6,0)
log-d 7.75 (5,3) 0.25 (7,0)
CSE 573 18
Hardness of Planning
• FF (and other state-space planners) find solutions with unbalanced use of airplanes – little opportunities for post-facto parallelization
• Logistics domain is actually polytime solvable if parallel plan length not considered!
• NP-hard to find a solution with minimum parallel length
Huang, Kautz, Selman 2002 – modify STRIPS operators to force solutions to be ones that can be parallelized!
CSE 573 19
Modified STRIPS Logistics
(:action FLY-AIRPLANE :parameters (?airplane ?loc-from ?loc-to ?r) :precondition (and (AIRPLANE ?airplane) (AIRPORT ?loc-from) (AIRPORT ?loc-to) (at ?airplane ?loc-from) (can_use ?airplane ?r) (resource ?r)) :effect (and (not (at ?airplane ?loc-from)) (not (resource ?r)) (at ?airplane ?loc-to))))
(:init (at package bos-po) ... (resource r1) (resource r2) (resource r3) (resource r4) (can_use airplane1 r1) (can_use airplane1 r2) (can_use airplane2 r3) (can_use airplane2 r4) ...)(:goal (at package la-po) ...)
CSE 573 20
BB vs FF (modified logistics)
problem BB FFtime time
log-a(3,2)
1.71 0.12
log-b(3,1)
2.37 1.61
log-c(3,2)
9.96 > 4 hours
log-d(3,4)
155.1 > 4 hours
CSE 573 21
Coming Up
• Wednesday• Prob(Prob) = 100%• Ch 14 – Review of basic probability
theory• Ch 15 – start on Bayesian networks
• Change in schedule• Only one more homework (not two),
distributed Nov 28th