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AAAI-2006 1 of 20
Deconstructing Planning as SatisfiabilityA Revised Report
Henry KautzUW University of Rochester
in collaboration with Bart Selman and Jöerg Hoffmann
AAAI-2006 2 of 20
AI Planning
• Two traditions of research in planning:– Planning as general inference (McCarthy 1969)
• Important task is modeling
– Planning as human behavior (Newell & Simon 1972)
• Important task is to develop search strategies
AAAI-2006 3 of 20
Satplan• Model planning as Boolean satisfiability
– (Kautz & Selman 1992): Hard structured benchmarks for SAT solvers
– Pushing the envelope: planning, propositional logic, and stochastic search (1996)
• Can outperform best current planning systems
Satplan (satz) Graphplan (IPP)
log.a 5 sec 31 min
log.b 7 sec 13 min
log.c 9 sec > 4 hours
AAAI-2006 4 of 20
Satplan in 15 Seconds• Time = bounded sequence of integers• Translate planning operators to propositional
schemas that assert:
1 2
1 2
1 2
0
negates a precondition
action( ) pre( ) effect( 1)
( ) ( ) if interfering
fact( ) fact( 1) ( )
initial_state ,
o
goal_stat
f
frame
e
axioms
n
i i i
action i action i
i i action i acti
action action
on
AAAI-2006 5 of 20
Plan Graph Based Instantiation
initial state: p
action a:precondition: p
effect: p
action b:precondition: p
effect: p q
a0 a1
p0 p1 p2
b1
m0 m1
q2
= =
AAAI-2006 6 of 20
International Planning Competition
• IPC-1998: Satplan (blackbox) is competitive
AAAI-2006 7 of 20
International Planning Competition• IPC-2000: Satplan did poorly
Satplan
AAAI-2006 8 of 20
International Planning Competition
• IPC-2002: we stayed home.
Jeb Bush
AAAI-2006 9 of 20
International Planning Competition
• IPC-2004: 1st place, Optimal Planning– Best on 5 of 7 domains– 2nd best on remaining 2 domains
PROLEMA /
philosophers
AAAI-2006 10 of 20
International Planning Competition
• IPC-2006: Tied for 1st place, Optimal Planning– Other winner, MAXPLAN, is a variant of Satplan!
CPT2 MIPS-BDD SATPLAN Maxplan FDP
Propositional Domains(1st / 2nd Places)
0 / 1 1 / 1 3 / 2 3 / 2 0 / 3
Temporal Domains(1st / 2nd Places)
2 / 0
AAAI-2006 11 of 20
What Changed?
• Small change in modeling– Modest improvement from 2004 to 2006
• Significant change in SAT solvers!
AAAI-2006 12 of 20
What Changed?
• In 2004, competition introduced the optimal planning track– Optimal planning is a very different beast from non-
optimal planning!– In many domains, it is almost trivial to find poor-
quality solutions by backtrack-free search!• E.g.: solutions to multi-airplane logistics planning problems
found by heuristic state-space planners typically used only a single airplane!
– See: Local Search Topology in Planning Benchmarks: A Theoretical Analysis (Hoffmann 2002)
AAAI-2006 13 of 20
Why Care About Optimal Planning?
• Real users want (near)-optimal plans!– Industrial applications: assembly planning, resource
planning, logistics planning…– Difference between (near)-optimal and merely
feasible solutions can be worth millions of dollars
• Alternative: fast domain-specific optimizing algorithms – Approximation algorithms for job shop scheduling– Blocks World Tamed: Ten Thousand Blocks in Under
a Second (Slaney & Thiébaux 1995)
AAAI-2006 14 of 20
Domain-Independent Feasible Planning Considered Harmful
Solution Quality?
Speed?
General optimizing planning algorithms
Best Moderate
Domain-specific optimizing planning algorithms
High Fast
Domain-independent feasible planning
? ?
AAAI-2006 15 of 20
Objections
• Real-world planning cares about optimizing resources, not just make-span, and Satplan cannot handle numeric resources– We can extend Satplan to handle numeric constraints– One approach: use hybrid SAT/LP solver (Wolfman &
Weld 1999)
– Modeling as ordinary Boolean SAT is often surprisingly efficient! (Hoffmann, Kautz, Gomes, & Selman, under review)
AAAI-2006 16 of 20
Projecting Variable Domains
initial state: r=5
action a:precondition: r>0
effect: r := r-1
• Resource use represented as conditional effects
a1
r=5 r=5 r=5
r=4 r=4
a0
r=4
AAAI-2006 17 of 20
2002 ICAPS Benchmarks
AAAI-2006 18 of 20
Large Numeric Domains
Directly encode binary arithmetic
action: aprecondition: r keffect: r := r-k
a1
r11
+
-k
r21
r31
r41
r12
r22
r32
r42
AAAI-2006 19 of 20
Objections
• If speed is crucial, you still must use feasible planners– For highly constrained planning problems,
optimal planners can be faster than feasible planners!
AAAI-2006 20 of 20
Constrainedness: Run Time
AAAI-2006 21 of 20
Constrainedness: Percent Solved
AAAI-2006 22 of 20
Further Extensions to Satplan
• Probabilistic planning– Translation to stochastic satisfiability
(Majercik & Littman 1998)– Alternative untested idea:
• Encode action “failure” as conditional resource consumption
• Can find solutions with specified probability of failure-free execution
• (Much) less general than full probabilistic planning (no fortuitous accidents), but useful in practice
AAAI-2006 23 of 20
Encoding Bounded Failure Free Probabilistic Planning
plan failure free probability 0.90
action: afailure probability: 0.01
preconditions: p
effects: q
action: aprecondition: p
s log(0.89)
effect: q s := s + log(0.99)
AAAI-2006 24 of 20
One More Objection!
• Satplan-like approaches cannot handle domains that are too large to fully instantiate– Solution: SAT solvers with lazy instantiation– Lazy Walksat (Singla & Domingos 2006)
• Nearly all instantiated propositions are false• Nearly all instantiated clauses are true• Modify Walksat to only keep false clauses and a
list of true propositions in memory
AAAI-2006 25 of 20
Summary
• Satisfiability testing is a vital line of research in AI planning– Dramatic progress in SAT solvers– Recognition of distinct and important nature of
optimizing planning versus feasible planning
• SATPLAN not restricted to STRIPS any more!– Numeric constraints– Probabilistic planning– Large domains