Post on 22-Feb-2016
description
Nitin Kumar YadavRMIT University, Melbourne
nitin.yadav@student.rmit.edu.au
Minor thesis - semester 2, 2009, under the supervision of Dr. Sebastian Sardina, RMIT university
An empirical evaluation of regression and progression approaches for behavior composition
Behavior CompositionRegression and Progression techniquesRegression approach ImplementationFramework for behavior composition experimentsExperimentsConclusion
Contents
An empirical evaluation of regression and progression approaches for behavior composition
Behavior Composition
3
What is a behavior ?
• Behavior – Logic of a machine– Web service– Stand alone component
• Abstracted as finite transition systems• Available behaviors can be non-deterministic
1. Sardina,Patrizi & De Giacomo, Proceedings of Principles of Knowledge Representation and Reasoning (KR),pages 640-650, September 2008. AAAI Press.
Painting Blocks Example1
Behavior Composition
4
Behaviors perform actions in a shared environment
ArmB: prepare
Behavior Composition
5
Behaviors perform actions in a shared environment
ArmA: clean
Behavior guards over the environments must be satisfied.
Behavior Composition
6
Behaviors perform actions in a shared environment
ArmA: dispose
Behavior Composition
7
Behaviors perform actions in a shared environment
ArmC: recharge
8
Behavior CompositionWhat is behavior composition ?
• Can the behaviors, executing one at a time realize a virtual target ?
• Does there exist a controller which can guarantee that the available behaviors can always do the actions that the target can request.
Behavior Composition
9
Enacted system of available behaviors
All what the system can do
a1 b1 c1
e1
a1 b2 c1
e2
a1 b1 c1
e2
a1 b3 c1
e2
a2 b1 c1
e2
a2 b1 c1
e3
a2 b3 c1
e2
a2 b3 c1
e3
a1 b1 c2
e1
a1 b1 c2
e2
B: prepare
B: paint
B: paint
A: clean
A: clean
A: clean
A: clean
C: recharge
C: prepare
A: recharge
a2 b2 c1
e2
a2 b2 c1
e3
A: clean
A: clean
Behavior Composition
10
Enacted target system
All what the target can request
t1
e1
t2
e2
t3
e2
t3
e3
t4
e3
t4
e2
t5
e1
t5
e4
recharge
prepare
recharge
clean
paintclean
paint
dispose
dispose
paint
• Does the enacted system ‘behave’ like the enacted target ?
11
Behavior Composition
Techniques
12
Two approaches for behavior composition based on simulation
• Regression based approach [Sardina,Patrizi & De Giacomo, KR 2008]
• Progression based approach [Stroeder & Pagnucco, 2009, IJCAI 2009]
Simulation
13
• A transition system T1 simulates another transition system T2 iff T1 can ‘mimic’ all the states of T2
• A state in the available system mimics another state in the target system if:– It can do all the actions that the target state can do– The successor state in the available system as a result of such
an action simulates the resulting state in the target system• Simulation is a relation of states of the enacted system
and the states of the enacted target which can be ‘mimicked’.
Progression approach – IJCAI ‘09
• Salient features– Works by expanding ‘decision nodes’ and marking
‘bad’ states and propagating marking backwards.– Expands states in relation to the target– Similar to how we reason– Implementation available
• Java based• Uses recursion to expand and mark states
18
Regression approach
• Step1 – Build the enacted system and enacted target
independent of each other• Step2
– Assume each system states simulates every target state. i.e. add a potential simulation link from each system state to every target state.
19
Regression approach
• Step3– Iteratively remove the links that violate the
simulation definition i.e.• Can the state simulate the target state ?• Is the resulting state in simulation with the resulting state
of the target ?• If the target state is final but the behavior states are not
• Stop when no more links can be removed• A solution exists if the initial state is in the
simulation relation
20
21
Example
Enacted Target
Regression approach
a1 b1 c1
e1
t1
e1
t2
e2
prepare
a1 b2 c1
e2
B:prepare
t4
e2
paint
a1 b1 c1
e2
a1 b3 c1
e2
B: paint
a2 b2 c1
e2
a2 b2 c1
e3
A: clean
dispose t5
e1
A: dispose
A: dispose
a1 b1 c1
e2
a1 b3 c1
e2
recharge t1
e1
a1 b3 c1
e2
a1 b1 c1
e2
A: RechargeB: Recharge
C: Recharge a1 b1 c2
e2
a1 b3 c1
e2
C: Recharge
a1 b1 c1
e2
A: Recharge
a1 b1 c2
e1
C:recharge
Enacted System
XX X
22
Example
Enacted Target
Regression approach
a1 b1 c1
e1
t1
e1
t2
e2
prepare
a1 b2 c1
e2
B:prepare
t4
e2
paint
a1 b1 c1
e2
a1 b3 c1
e2
B: paint
a2 b2 c1
e2
a2 b2 c1
e3
A: clean
dispose t5
e1
A: dispose
A: dispose
a1 b1 c1
e2
a1 b3 c1
e2
recharge t1
e1
a1 b3 c1
e2
a1 b1 c1
e2
A: RechargeB: Recharge
C: Recharge a1 b1 c2
e2
a1 b3 c1
e2
C: Recharge
a1 b1 c1
e2
A: Recharge
a1 b1 c2
e1
C:recharge
Enacted System
23
Example
Enacted Target
Regression approach
a1 b1 c1
e1
t1
e1
t2
e2
prepare
a1 b2 c1
e2
B:prepare
t4
e2
paint
a1 b1 c1
e2
a1 b3 c1
e2
B: paint
a2 b2 c1
e2
a2 b2 c1
e3
A: clean
dispose t5
e1
A: dispose
A: dispose
a1 b1 c1
e2
a1 b3 c1
e2
recharge t1
e1
a1 b3 c1
e2
a1 b1 c1
e2
A: RechargeB: Recharge
C: Recharge a1 b1 c2
e2
a1 b3 c1
e2
C: Recharge
a1 b1 c1
e2
A: Recharge
a1 b1 c2
e1
C:recharge
Enacted System
XXXXX
Assume
• Salient features– Enacted system and the enacted target are built
independent of each other– Allows recovery from behavior failure and any
uncontrolled action– The enacted system can be stored in memory and
different targets can then be checked for solution
24
Regression approach
• Related tools available– Symfony, Opus [De Giacomo et all, 2009, University of Rome ‘La Sapienza’]
• Used for web services composition• Do not have a concept of environment and guards
• Our implementation– First serious and complete implementation– Optimizations added on top of the original approach
25
Regression approachImplementation
• Step2 : Original– Starts with links from
every system state connected to every target state
– In each iteration check
• If the state is in simulation ?
• Is the resulting state in simulation ?
• Is the state final ?26
Regression approachOptimizations
• Step2: Optimization 1:- Starts with links between
the ‘similar’ states. i.e. add link only if the system state can do all what a target state can request.
- In each iteration• Is the resulting state in
simulation ?
27
Step2: Original
Enacted Target
Regression approach
a1 b1 c1
e1
t1
e1
t2
e2
prepare
a1 b2 c1
e2
B:prepare
t4
e2
paint
a1 b1 c1
e2
a1 b3 c1
e2
B: paint
a2 b2 c1
e2
a2 b2 c1
e3
A: clean
dispose t5
e1
A: dispose
A: dispose
a1 b1 c1
e2
a1 b3 c1
e2
recharge t1
e1
a1 b3 c1
e2
a1 b1 c1
e2
A: RechargeB: Recharge
C: Recharge a1 b1 c2
e2
a1 b3 c1
e2
C: Recharge
a1 b1 c1
e2
A: Recharge
a1 b1 c2
e1
C:recharge
Enacted System
28
Step2: Optimized
Enacted Target
Regression approach
a1 b1 c1
e1
t1
e1
t2
e2
prepare
a1 b2 c1
e2
B:prepare
t4
e2
paint
a1 b1 c1
e2
a1 b3 c1
e2
B: paint
a2 b2 c1
e2
a2 b2 c1
e3
A: clean
dispose t5
e1
A: dispose
A: dispose
a1 b1 c1
e2
a1 b3 c1
e2
recharge t1
e1
a1 b3 c1
e2
a1 b1 c1
e2
A: RechargeB: Recharge
C: Recharge a1 b1 c2
e2
a1 b3 c1
e2
C: Recharge
a1 b1 c1
e2
A: Recharge
a1 b1 c2
e1
C:recharge
Enacted System
• Parent state checking.– If a link is removed in the iteration, we need to check
ONLY the parent states.– Consider the chain example.
29
Regression approachOptimization 2
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
a s3
e1
a a s4
e1
b s5
e1
Enacted Target
Enacted System
• Original approach (also optimization 1)
• Optimization 2
30
Regression approachOptimization 2
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
a s3
e1
a a s4
e1
b s5
e1System
Target
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
a s3
e1
a a s4
e1
b s5
e1
System
Target
NO SOLUTION
• Original approach (also optimization 1)
• Optimization 2
31
Regression approachOptimization 2
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
a s3
e1
a a s4
e1
b s5
e1System
Target
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
a s3
e1
a a s4
e1
b s5
e1
System
Target
NO SOLUTION
NO SOLUTION
• 3rd optimization– Check if the initial state of the system is still in
simulation– Reduces the number of iterations if no solution
exists– Consider the modified chain example
32
Regression approachOptimization 3
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
b s3
e1
a a s4
e1
b s5
e1System
Target
• Original approach (also optimization 1)
• Optimization 3
33
Regression approachOptimization 3
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
b s3
e1
a a s4
e1
b s5
e1System
Target
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
b s3
e1
a a s4
e1
b s5
e1
System
Target
NO SOLUTION
NO SOLUTION
123
1
Benchmarking Framework
• Benchmarking in Java.– compilation
• First generation of JVM entirely interpreted• Just In Time compilation• Hot Spot dynamic compilation
– Class loading– Garbage Collection– Accuracy of system.currentTimeMillis() is platform
dependent.
34http://www.ibm.com/developerworks/library/j-jtp12214/
Benchmarking Framework
• Japex.– measures class loading, compilation time, garbage
collection time.– Easy to implement driver classes
• public void prepare(TestCase testCase);• public void warmup(TestCase testCase);• public void run(TestCase testCase);
– Define test cases in xml
35http://www.ibm.com/developerworks/library/j-jtp12214/
Benchmarking Framework
• Designing Benchmark Problems using a library of available behaviors by manipulating them.
36
Benchmarks
Number of Behaviors
Target Complexity
Environment Complexity
ND Amplification Chains
Benchmarking Framework
• No of behaviors– If a solution exists, increasing the number of
behaviors exponentially complicates the problem without affecting the ‘reliazability’.
– Example
• armA:1 1 1 1• armB:1 2 3 4• armC:1 1 1 1
37
Test case 1 Test case 2 Test case 3 Test case 4
Benchmarking Framework
• Target Complexity– Make the target do multiple loops without affecting
the solution.– Example
38
t1 t2
t3
t4
t5
prepare
clean
paint
paint
dispose
recharge
t1 t2
t3
t4
t5
prepare
clean
paint
paint
dispose
recharge
t1 t2
t3
t4
t5
prepare
clean
paint
paint
dispose
recharge
Benchmarking Framework
• Target Complexity with solution break– The last loop of the target requests an unknown
action.
– Multiple break points
39
Target 1 Target 1 Target 1 Target 1recharge recharge recharge
breakAction
Target 1 Target 1 Target 1 Target 1breakAction recharge recharge
breakAction
Benchmarking Framework
• Environment Complexity– Similar to target complexity except for
• Removal of guards from the target/behaviors
• Chains– Single behavior and target with ‘n’ states. – Behavior cannot do the last action of the target
40
t1
e1
t2
e1
a t3
e1
a a t4
e1
a t5
e1
s1
e1
s2
e1
a s3
e1
a a s4
e1
b s5
e1
Enacted Target
Enacted System
Benchmarking Framework
• ND Amplification– Add more non-deterministic states to an already
existing non-deterministic behavior
41
b1 b2prepare b3 b4
preparepaint
Paint, clean
recharge
clean
b1 b2prepare b3 b4
preparepaint
Paint, clean
recharge
clean
b2`Paint, clean
paint
prepare
ND Amplification = 1
Experiments
• No of Behaviors
42
Experiments
• No of Behaviors
43
Experiments
• Target Complexity
44
Experiments
• Target Complexity
45
Experiments
• Complex web services example
46From honor’s thesis of Paolo Felli, Service Composition through Synthesis Techniques based on Alternating-time Temporal Logic, University of Rome
Experiments
47
Tool Time
Symfony ~50 mins
Opus 18 hrs
Regression Original 23 seconds
Regression Optimized 15 seconds
TLV 100 ms
Conclusion
• First serious implementation of regression approach
• The first optimization should always be done• The second optimization is beneficial of patterns
similar to chain• Benchmark problems creation by modifying
existing problems.
48
Questions ?
Comparing the speed of the techniques