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8th. June 2016Michaja Pressmar
Master Thesis 16.11.2015 – 16.5.2016 Examiner: Prof. Dr. Malte Helmert Supervisor: Dr. Martin Wehrle
Analysing and Combining Static Pruning Techniques for Classical Planning Tasks
1. Introduction
Overview
2
2. Pruning Techniques3. Synergies4. Experiments5. Conclusion
1. Introduction
3
● Variables
driveA B → driveB A → loadA unloadA loadB unloadB
A A
B B
1. Introduction
A B
4
● Operators
● Initial state
● Goal state
Planning Task
Plan:
A T B TA A B BB A A BA T B TA AB A B B
1. Introduction
A B
5
loadA driveA->B unloadB
1. Introduction
A BC
A A AB A A
C A A
C T AB T A
A T A
C A TA A T
B A T
C B AA B A
B B AA B TB B T
C B T
C B CA B C
B B C
B A BA A B
C A BA T BB T B
C T B
B B BA B B
C B B
A C AB C A
C C A
C C TA C T
B C T
C C BB C B
A C B
C C C
A C C B C C
B A CC A C
A A C
C T CA T C
B T C
C C BB C B
A C B
B T TA T T
6
State Explosion Problem
State Explosion Problem
Dynamic Pruning
Static Pruning
● Variables
● Operators
● Initial state
● Goal state
A A
B B
unloadB ...
1. Introduction 7
2. Pruning Techniques
Safe Abstraction
8
Redundant Operator Reduction Dominance Pruning
2. Pruning Techniques 9
Safe Abstraction
Original version by Helmert (2006)
Variant by Haslum (2007)
General idea:1. Abstract safe variables
2. Solve abstract task
3. Refine plan
2. Pruning Techniques 10
Safe Abstraction
● Variables
driveA B → driveB A → loadA unloadA loadB unloadB
A
B B
● Operators
● Initial state
● Goal state
Planning Task
A A
B
Abstract Planning Task
TA B
2. Pruning Techniques
Plan: loadA unloadB
11
Safe Abstraction
A B
driveA B→
TA BA T B TA A B B
Abstract Planning Task Planning TaskOriginal
2. Pruning Techniques 12
Redundant Operator Reduction
Researched by Haslum and Jonsson (2000)
General idea: More operators than necessary
Remove redundant operators
2. Pruning Techniques 13
Redundant Operator Reduction
A B
C
2. Pruning Techniques 14
Dominance Pruning
Optimality-preserving
Torralba and Hoffmann (2015)
Compute state simulation
Kissmann and Torralba (2015)
Identify subsumed transitions
Remove „bad“ operators
2. Pruning Techniques 15
Dominance Pruning
A T B TA A B BB A A B
driveA B → driveB A → loadA unloadA loadB ● Operators unloadB
16
3. Synergies
17
Synergy Effects
P P' P''
Pruning
Method 1
Pruning
Method II
3. Synergies
Which synergies exist? → Proven theorems
positive Synergy
negative
Synergy
18
Synergy Effects
SafeAbstraction
RedundantOperators
DominancePruning
3. Synergies
4. Experiments
Implementation in Fast Downward
19
IPC Benchmarks
20
Roadmap Experiments
1. Techniques separately
2. Combinations and Synergies
4. Experiments
Configurations
Best performance
Synergies separately
Synergies compared
4. Experiments 21
Safe Abstraction
Compared 2 Variants:
1886
1413+80
50%22%
Original Version
Coverage:
35% 58%Variant (Haslum)
Instances abstractable
Safe Variables per instance
Expanded States Planning Time
baseline (Greedy + FF)
Safe
Abs
trac
tion
4. Experiments 22
Redundant Operator Reduction
Coverage-24
1886
1413
Expanded States Planning Time
baseline (Greedy + FF)
Red
unda
nt O
pera
tor
Red
uctio
n
No Coverage Increase
4. Experiments 23
Dominance Pruning
Powerful
up to 50 – 80% operators pruned
, but comparatively slow
Incremental Computation & Early-Abort
Expanded States Planning Time
baseline (A* + LMCut)
Dom
inan
ce P
runi
ng
4. Experiments 24
Synergies separately
Single technique vs combination
All synergies occur on IPC domains
ROR vs SA+ROR
Pruned Operators
ROR
ROR
(afte
r SA
)
4. Experiments 25
Synergies compared
ROR+SA vs SA+ROR
Combination vs Combination
No coverage increase
Operators left Planning Time
ROR + SA
SA +
RO
R
5. Conclusion
26
less strict Safe Abstraction more fine-grained ROR satisficing Dominance Pruning
Static Pruning can improve coverage Synergies exist & occur on IPC domains
Further research:
Thank you!