Efficient Automated Planning with New Formulations Ruoyun Huang Advisors: Dr. Yixin Chen and Dr. Weixiong Zhang Washington University in St. Louis Efficient Automated Planning with New Formulations Ruoyun Huang Advisors: Dr. Yixin Chen and Dr. Weixiong Zhang Washington University in St. Louis
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Efficient Automated Planning with New Formulations
Efficient Automated Planning with New Formulations. Ruoyun Huang Advisors : Dr. Yixin Chen and Dr. Weixiong Zhang Washington University in St. Louis. Efficient Automated Planning with New Formulations. Ruoyun Huang Advisors : Dr. Yixin Chen and Dr. Weixiong Zhang - PowerPoint PPT Presentation
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Efficient Automated Planning with New Formulations
Ruoyun HuangAdvisors: Dr. Yixin Chen and Dr. Weixiong Zhang
Washington University in St. Louis
Efficient Automated Planning with New Formulations
Ruoyun HuangAdvisors: Dr. Yixin Chen and Dr. Weixiong Zhang
Washington University in St. Louis
Dr. Yixin Chen (co-advisor)
Dr. Christopher D. Gill
Dr. Norman I. Katz
Dr. Yinjie Tang
Dr. Kilian Weinburger
Dr. Weixiong Zhang(co-advisor)
Committee Members
Classical Planning (STRIPS)loc1 loc2
Init State: (AT pkg loc1); (AT truck loc2);
loc1 loc2
Goal: (AT pkg loc2)
Applicable actions: MOVE; LOAD;UNLOAD;
?
One solution with time step N=4: Time Step 1: MOVE truck loc2 loc1 Time Step 2: LOAD pkg truck loc1Time Step 3: MOVE truck loc1 loc2 Time Step 4: UNLOAD pkg truck loc2
3
Each action is consisted of a set of preconditions and a set of effects
NASA Mars rover High speed printer in PARC Hubble space telescope[Zweben94]
Mobile robots[Talamadupula10]
Anti-air defense system[Benaskeur10]
Biological network[Bryce10]
Natural language processing[Geib07]
…
Research in “Automated” planning provides a basis for any domain specific planning applications
Applications
4
High complexity
Key Challenge
… Branching factor: O(2A)Number of states: O(2AN)…
state
……
action
…… … …
… … …
… … …
5
A: Number of actionsN: Length of plan
Expressiveness Complicated real world scenarios
Summay of DissertationClassical Planning Temporally Expressive
PlanningSTRIPS SAS+
Search
Lots of existing works.
A abstraction search method. Most existing temporal planners fall into this category.
SAT
All the existing SAT-based planners are STRIPS based.
1. Utilize SAS+ for stronger mutex constraints;
2. A new encoding scheme based on SAS+.
1. Two new encoding schemes (STRIPS and SAS+, respectively);
2. Extended to handle action costs.
Motivation: Formulation/Representation is vital to problem solving
The SAS+ formulation SAS+ on planning algorithms
Abstraction search SAS+ formulation as Satisfiability(SASE)Why SASE works?
Temporally expressive planning A planning as SAT approachApply SASE to temporal planningFurther handle action costs
Agenda
SAS+ Representation[Backstrom:96]
loc1 loc2
in truck
at loc1 at loc2
V(pkg)
at loc1 at loc2
V(truck)
AT pkg loc1 AT pkg loc2 IN pkg truck AT truck loc1 AT truck loc2
Strips SAS+
Transition: Change between values in a multi-valued variable
F: number of STRIPS factsAssume avg. facts/value per DTG is 10
fact… fact
…
10
SAS+ also captures more hidden problem structures
11
Causal Dependency and Causal Graphloc1 loc2
at loc1 at loc2
in truck
at loc1 at loc2
pkg’s DTG
truck’s DTG
pkg’s DTG
truck’s DTG
LOAD pkg truck loc2:Pre: (AT truck loc2)
Causal Graph
12
Example of Hierarchy
To build a house
1. Drive to the city hall2. Fill out an application3. Drive to the bank4. Ask for money5. Drive somewhere6. Look for a builder7. Sign a Contract8. Build foundation9. Build frame10. Build roof11. Build wall13. Buy furniture … …
1. Get a permit2. Get enough money3. Construction4. Interior … …
1.a Drive to the city call1.b Fill out an application
2.a Drive to bank2.b Fill out an application
3.a Find a builder3.b Sign a contract3.c Build foundation3.d Build frame3.e Build roof3.f build wall
SAT Instance (Part 2):matching actions and transitions (multiple independent ones)… … …
Transitions
Actions
t = 1 t =2 t =3
SAT Instance:Facts and actionsActions&
Facts
Planning graph
… … …
t = 1 t =2 t =3, 4, …
Strips Based Encoding
Transition Based Encoding26
27
Constraintstruck:loc2
Time step 1 Time step 2
……………
Time step 3,4,5,…
pkg: loc1
truck:loc1 truck:loc1
truck:loc2
truck:loc1
truck:loc2
pkg: loc1pkg: truckpkg: loc2
pkg: loc1pkg: truckpkg: loc2
Find matchings
set of actions set of actions … …
pkg: truckpkg: loc2
1. Progression of transitions over time steps (blue one implies green ones)2. Initial state and goal (Bold ones)3. Matching actions and transitions4. Action mutual exclusions and transition mutual exclusions
Results of SASE
Results of SASE
Worst Case State Space Size
…………
State Space (Part 1):O((2T)N)State Space (Part 2):O(NK)
… … …
Transitions
Actions
t = 1 t =2 t =3
State Space:O((2A)N)
ActionsPlanning graph
… … …
t = 1 t =2 t =3, 4, …
Strips Based Encoding
Transition Based Encoding
30
Total: O((2T)NNK)
K K K
Note: T < A
A Number of actionsN Number of Time Step
T Number of transitions
Worst case analysis has limitations Real SAT solvers do not enumerate Real SAT solvers may not favor transition variables
We want better understanding
Reason: significance of transition variables Why more significant? How much? Correlates to the speedup?
Why SASE works?
32
The DPLL procedure Depth First Search and Inference Used by almost all SAT solvers
SAT Algorithms
V1
-
V2
… …… …
No Solution
V5
V10
… …No
solution
Solution Found
at certain point
V7
Which variable to pick matters
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VSIDS Heuristic Widely used, very effective h(), number of times a variable appears in all clauses EMA of all variables’ h(), clause database changes over
Action variables even do not exist in top percentage of variables
37
If we ranking all variables by h Transition variables dominates the head of the list More likely to be picked as decision variables
earlier (statistically)
But VSIDS heuristic dynamically updates … We need evidence that reflects the dynamic
updating
Significance of Transition Variables
38
A measurement of how often transition variables (or action variables) are chosen as decision variable
Branching Frequency
V1
-
V2
… …
V10
… …
V7
V5
… …
For every 1000 consecutive decision variables
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Branching Frequency
Zenotravel #15 Speedup = 4.21
Openstack #5Speedup = 7.88
Transition variables have higher branching frequency in most instances
40
Branching Frequency
Storage #12Speedup = 0.83
Sokoban #8Speedup = 0.62
SASE could be less efficient in a few domains
41
Transitions variables are more likely to be decision variables
The more branching on transition variables, the higher speedup
Transition Index : An indicator of the significance
Transition Index
p
pt
VVThe ratio of transition
variables in the top ranked p percent variables
VVt Normalized by the ratio
of transition variables among all variables
42
Transition Index versus Speedup
Transition index of 1%
Transition index of 10%
43
Transition Index versus Speedup
Transition index of 50% Transition index of 90%
44
Transition variables occurs more frequently
Transition index correlates with the speedup that SASE can achieve
Conclusions
45
OutlineAutomated Planning
Classical Planning Temporally Expressive Planning
STRIPS SAS+ STRIPS
Search
SAT Search
SAT Search
SAT
4
SAS+
SAT
Actions have durations
Temporal Planning
Light a Match
find a manual
e.g. got a blackout:
Repair a fuse
Time
47
Temporally Expressive (Required Concurrency) Two actions need to be executed in parallel, in any
valid plan.
Temporally Expressive[W. Cushing et al. 2007]
adds fact (has light)
Light a match
deletes fact (has light)
Time
Read Manualcondition conditionrequires condition
(has light)
48
A planning algorithm is temporally expressive if it can handle required concurrency, otherwise it is temporally simple
Required Concurrency is important in the real world PDDL2.1 supports temporally expressiveness e.g. Firework, Missile launch
Most state of the art temporal planners are not able to handle temporally expressive
Action cost is in addition an important feature No planner can handle both temporal
expressiveness and action costs
Facts about State-of-the-art
49
Overview of Our WorkTemporally Expressive planning
STRIPS
A STRIPS based encoding [Huang09]
SAS+
SASE for temporal planning
PDDL2.1 Spec
Cost Sensitive Temporally Expressive Planning
Extend to support action costs
MaxSAT solvers A Branch & Bound Algorithm
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A MinCost SAT Formulation
Durative Actions as SAS+
adds fact (has light)
Light a match
deletes fact (has light)
Time
Light a match
Time
51
SASE with Durative Actions
Experimental Results
PET and SET are our SAT-based approach, with different encoding schemes;Crikey2, Crikey3, TFD, LPG-c are several heuristic search based planners53
Cost Sensitive Temporally Expressive Planning
MaxSAT solvers A Branch & Bound Algorithm
MinCost
54
Extend the encoding into MinCost SAT problem MinCost: variables have costs, to be minimized
1. Convert MinCost instances into MaxSAT MaxSAT is widely accept
2. A planning specialized branch and bound algorithm
Use relaxed planning graph to estimate a heuristic function
Cost Sensitive Temporally Expressive
55
Results
56
More efficient algorithmSearchPlanning as SAT
More expressive featuresTemporal expressivenessAction costs
Summary
57
Other planning algorithmscausal link search
Other planning modelsprobabilistic, uncertainty, etc
Beyond planning researchBounded model checking[Clarke01]
Answer set programming[Lin02]
Significance of Research
58
Integrate existing techniques into SASE
More understanding on what makes an SAT instance easy or hard Backdoor set / Backbone set Goal: improve general SAT solving
More advanced planning features Preferences, numerics, etc.
SAS+ for other planning techniques
Future Directions
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R. Huang, Y. Chen, and W. Zhang, “SAS+ planning as Satisfiability”. Journal of Artificial Intelligence Research, invited and submitted
R. Huang, Y. Chen, and W. Zhang, “A Novel Transition Based Encoding Scheme for Planning as Satisfiability”. AAAI’10
R. Huang, Y. Chen, and W. Zhang, “An Optimal Temporally Expressive Planner: Initial Results and Application to P2P Network Optimization”. ICAPS’09
Y. Chen, R. Huang, Z. Xing and W. Zhang, “Long-distance Mutual Exclusion for Planning”. Artificial Intelligence Journal, 2009
Y. Chen, R. Huang, and W. Zhang, “Fast Planning by Search in Domain Transition Graphs”. AAAI’08
C. Hsu, B. Wah, R. Huang, and Y. Chen, “ Constraint Partitioning for Solving Planning Problems with Trajectory Constraints and Goal Preferences”. IJCAI’07