Simulation Adaptation, Composition and Reuse

Paul F. Reynolds, Jr.Paul F. Reynolds, Jr.reynolds@virginia.edureynolds@virginia.edu 434 924-1039 434 924-1039

David Brogan,David Brogan, Rob Bartholet, Joe Carnahan, Xinyu Liu, Ross Gore, Lingjia Tang, Rob Bartholet, Joe Carnahan, Xinyu Liu, Ross Gore, Lingjia Tang, Yannick LoitiYannick Loitièère, Michael Spiegel, Chris Whitere, Michael Spiegel, Chris White

Modeling and Simulation Technology Research Initiative

Computer Science DepartmentComputer Science Department

University of Virginia, USAUniversity of Virginia, USA

http://www.cs.virginia.edu/~MaSTRI/

Designing for Change:

What Are Key M&S Challenges?

Multi-resolution modeling – Multi-resolution modeling – Simulating over-Simulating over-lapping phenomena at two or more levels of lapping phenomena at two or more levels of (spatial/temporal) resolution concurrently.(spatial/temporal) resolution concurrently.

Composition – Composition – Constructing a (larger) simulation from two or more existing simulations.

Interoperability – Interoperability – When two or more simulations can execute together meaningfully.

Reuse – Reuse – Using a simulation for a purpose other than that for which it was originally intended.

Multi-resolution modeling – Multi-resolution modeling – Simulating over-Simulating over-lapping phenomena at two or more levels of lapping phenomena at two or more levels of (spatial/temporal) resolution concurrently.(spatial/temporal) resolution concurrently.

Composition – Composition – Constructing a (larger) simulation from two or more existing simulations.

Interoperability – Interoperability – When two or more simulations can execute together meaningfully.

Reuse – Reuse – Using a simulation for a purpose other than that for which it was originally intended.Adaptin

g for r

euse

Adapting fo

r reu

se

Modeling and Simulation Technology Research Initiative

Simulation design and Simulation design and semi-automatedsemi-automated adaptation for reuseadaptation for reuse

• COERCECOERCE

–CoercibilityCoercibility: the practices and methods for capturing : the practices and methods for capturing designer knowledge in softwaredesigner knowledge in software

–CoercionCoercion:: a user-guided, semi-automated software a user-guided, semi-automated software adaptation processadaptation process

• ComposabilityComposability

–Reusing components, possibly with acceptable amounts of Reusing components, possibly with acceptable amounts of revision, to meet new requirementsrevision, to meet new requirements

Simulation design and Simulation design and semi-automatedsemi-automated adaptation for reuseadaptation for reuse

• COERCECOERCE

–CoercibilityCoercibility: the practices and methods for capturing : the practices and methods for capturing designer knowledge in softwaredesigner knowledge in software

–CoercionCoercion:: a user-guided, semi-automated software a user-guided, semi-automated software adaptation processadaptation process

• ComposabilityComposability

–Reusing components, possibly with acceptable amounts of Reusing components, possibly with acceptable amounts of revision, to meet new requirementsrevision, to meet new requirements

Simulation

Flexible Points

Sensitivity AnalysesDesign-timeprovidedinsights

Expansion Opportunities

Metadataforcoercing

Coercibility

Flexible PointsSensitivity AnalysesExpansion Opportunities

Simulation

Modify? (solid insight)

Newrequirements

visualiz

e

Coercion metadata

Optimize? (need to explore)

Coercion

coerce(model) what-I-want

Full physics bicyclistFull physics bicyclist

2D point mass2D point mass

Collaborators at UVa

• Aerospace engineeringAerospace engineering (thermo-acoustic coupling)(thermo-acoustic coupling)

• High-energy physicsHigh-energy physics (quark-gluon plasma)(quark-gluon plasma)

• NeuroscienceNeuroscience (multi-scale model of hippocampus)(multi-scale model of hippocampus)

• Earth ScienceEarth Science (forest respiration) (forest respiration)

• Biomedical ScienceBiomedical Science (proposal: cell/tissue MRM) (proposal: cell/tissue MRM)

• Aerospace engineeringAerospace engineering (thermo-acoustic coupling)(thermo-acoustic coupling)

• High-energy physicsHigh-energy physics (quark-gluon plasma)(quark-gluon plasma)

• NeuroscienceNeuroscience (multi-scale model of hippocampus)(multi-scale model of hippocampus)

• Earth ScienceEarth Science (forest respiration) (forest respiration)

• Biomedical ScienceBiomedical Science (proposal: cell/tissue MRM) (proposal: cell/tissue MRM)

Outline of talk

CoercibilityCoercibility

ComposabilityComposability

CoercionCoercion

CoercibilityCoercibility

ComposabilityComposability

CoercionCoercion

Capturing and encoding the nature of Capturing and encoding the nature of alternative conditions (not specifics)alternative conditions (not specifics)

• Stipulate when simulation can/cannot be usedStipulate when simulation can/cannot be used

• State when/how it can be changedState when/how it can be changed

• Identify critical dependenciesIdentify critical dependencies

Capturing and encoding the nature of Capturing and encoding the nature of alternative conditions (not specifics)alternative conditions (not specifics)

• Stipulate when simulation can/cannot be usedStipulate when simulation can/cannot be used

• State when/how it can be changedState when/how it can be changed

• Identify critical dependenciesIdentify critical dependencies

Coercibility

Designing for change

Build upon simplifying assumptionsBuild upon simplifying assumptions• Frequently hiddenFrequently hidden

• Small changes can invalidate simulationSmall changes can invalidate simulation

• Examples:Examples:

– Bounding the space and time of the simulationBounding the space and time of the simulation

– Selecting equations to represent phenomenaSelecting equations to represent phenomena

– Knowing 4Knowing 4thth order Runge Kutta is good enough order Runge Kutta is good enough

Build upon simplifying assumptionsBuild upon simplifying assumptions• Frequently hiddenFrequently hidden

• Small changes can invalidate simulationSmall changes can invalidate simulation

• Examples:Examples:

– Bounding the space and time of the simulationBounding the space and time of the simulation

– Selecting equations to represent phenomenaSelecting equations to represent phenomena

– Knowing 4Knowing 4thth order Runge Kutta is good enough order Runge Kutta is good enough

Case study

Defining a simulation’s contextDefining a simulation’s context

• Articulate the assumptions in Articulate the assumptions in Falling Body ModelFalling Body Model

– A sphere falls to earth…A sphere falls to earth…

– 10 people listed assumptions10 people listed assumptions

– 29 assumptions were ultimately identified29 assumptions were ultimately identified

The top three people only found 21, 19, and 16 The top three people only found 21, 19, and 16

Defining a simulation’s contextDefining a simulation’s context

• Articulate the assumptions in Articulate the assumptions in Falling Body ModelFalling Body Model

– A sphere falls to earth…A sphere falls to earth…

– 10 people listed assumptions10 people listed assumptions

– 29 assumptions were ultimately identified29 assumptions were ultimately identified

The top three people only found 21, 19, and 16 The top three people only found 21, 19, and 16

Spiegel et al., WSC 2005

Capturing Insight

Flexible Points Flexible Points

• Any element of the model or simulation that can be Any element of the model or simulation that can be manipulated in meaningful and effective ways to direct manipulated in meaningful and effective ways to direct a simulation’s behaviora simulation’s behavior

• Examples:Examples:

– Value substitution for constants/parametersValue substitution for constants/parameters

– Replacing abstraction assumptionsReplacing abstraction assumptions

– Modifying stochastic elementsModifying stochastic elements

– Tuning logical time componentsTuning logical time components

Flexible Points Flexible Points

• Any element of the model or simulation that can be Any element of the model or simulation that can be manipulated in meaningful and effective ways to direct manipulated in meaningful and effective ways to direct a simulation’s behaviora simulation’s behavior

• Examples:Examples:

– Value substitution for constants/parametersValue substitution for constants/parameters

– Replacing abstraction assumptionsReplacing abstraction assumptions

– Modifying stochastic elementsModifying stochastic elements

– Tuning logical time componentsTuning logical time components

Clarifying Flexible Points

Pragmatic definition, emphasis on semi-Pragmatic definition, emphasis on semi-automated transformationautomated transformation

Contrast withContrast with• Simulation parametersSimulation parameters

• Design decisionsDesign decisions

Pragmatic definition, emphasis on semi-Pragmatic definition, emphasis on semi-automated transformationautomated transformation

Contrast withContrast with• Simulation parametersSimulation parameters

• Design decisionsDesign decisions

Parameters

Design decisions

Flexible points

Carnahan et al. Fall SIW 2004Carnahan et al., WSC 2005

“Assembly of parts into a whole without modifying the parts.” (Szyperski 2002)

“The capability to select and assemble simu-lation components in various combinations into valid simulation systems to satisfy specific user requirements.” (Petty and Weisel 2003)

“Assembly of parts into a whole without modifying the parts.” (Szyperski 2002)

“The capability to select and assemble simu-lation components in various combinations into valid simulation systems to satisfy specific user requirements.” (Petty and Weisel 2003)

Composability

We advocate semi-automated adaptation of components tosupport composability. --practical and realistic

Composability

It’s hard to build component repositoriesIt’s hard to build component repositories

• Size of componentsSize of components

• Component interfacesComponent interfaces

• Classifying semanticsClassifying semantics

It’s hard to build compositionsIt’s hard to build compositions

• Some theoretical results, few practicalSome theoretical results, few practical

It’s hard to build component repositoriesIt’s hard to build component repositories

• Size of componentsSize of components

• Component interfacesComponent interfaces

• Classifying semanticsClassifying semantics

It’s hard to build compositionsIt’s hard to build compositions

• Some theoretical results, few practicalSome theoretical results, few practical

r1

r3

r4

r2

r5r6

Rx1

x3

x4

x8

x2

x6x7

x5

r8r7

X

CS: Is there a subset of X of cardinality k or less that covers R?

Example instance when k = 3

REQUIREMENTSCOMPONENTS

Component Selection (CS)

Component Selection

CS is NP-completeCS is NP-complete

• Reduction from SAT (Page and Opper 1999)Reduction from SAT (Page and Opper 1999)

• Reduction from MSC (Petty et al. 2003)Reduction from MSC (Petty et al. 2003)

Approximate solutions to CS are possibleApproximate solutions to CS are possible

• Limit the amount of emergenceLimit the amount of emergence

• Use Greedy algorithmUse Greedy algorithm

CS is NP-completeCS is NP-complete

• Reduction from SAT (Page and Opper 1999)Reduction from SAT (Page and Opper 1999)

• Reduction from MSC (Petty et al. 2003)Reduction from MSC (Petty et al. 2003)

Approximate solutions to CS are possibleApproximate solutions to CS are possible

• Limit the amount of emergenceLimit the amount of emergence

• Use Greedy algorithmUse Greedy algorithm

Fox et al. WSC 2004

Applied Simulation Component Reuse (ASCR)

Critical New AssumptionCritical New Assumption: Any component : Any component can be adapted to satisfy any requirementcan be adapted to satisfy any requirement

Critical New AssumptionCritical New Assumption: Any component : Any component can be adapted to satisfy any requirementcan be adapted to satisfy any requirement

What does this buy us?What does this buy us?

• We no longer have to assume the existence of a We no longer have to assume the existence of a master set of components.master set of components.

• We can more flexibly react to changing We can more flexibly react to changing requirements.requirements.

But…But…We now have to account for the cost or utility of adapting a component!

ASCR

A formal model and analysis of component A formal model and analysis of component selection with selection with adaptableadaptable components components

• What is utility and cost of adapting component to What is utility and cost of adapting component to satisfy additional requirements?satisfy additional requirements?

• What is value of incomplete satisfaction?What is value of incomplete satisfaction?

A formal model and analysis of component A formal model and analysis of component selection with selection with adaptableadaptable components components

• What is utility and cost of adapting component to What is utility and cost of adapting component to satisfy additional requirements?satisfy additional requirements?

• What is value of incomplete satisfaction?What is value of incomplete satisfaction?

Results

Proven: Proven: Exact cover by three sets (X3C) Exact cover by three sets (X3C) reduces toreduces to ASCRASCR

• brings flexibility to component selection, but the brings flexibility to component selection, but the problem remains intractable (NP-Hard)problem remains intractable (NP-Hard)

Ongoing work:Ongoing work:

• Discovering scalable methods, algorithms, and Discovering scalable methods, algorithms, and heuristics for component selectionheuristics for component selection

• Encoding adaptability into the componentEncoding adaptability into the component

Proven: Proven: Exact cover by three sets (X3C) Exact cover by three sets (X3C) reduces toreduces to ASCRASCR

• brings flexibility to component selection, but the brings flexibility to component selection, but the problem remains intractable (NP-Hard)problem remains intractable (NP-Hard)

Ongoing work:Ongoing work:

• Discovering scalable methods, algorithms, and Discovering scalable methods, algorithms, and heuristics for component selectionheuristics for component selection

• Encoding adaptability into the componentEncoding adaptability into the component

Bartholet et al., WSC 2005

Efficient Adaptation of a simulation for Efficient Adaptation of a simulation for reuse and component selectionreuse and component selection

• Flexible pointsFlexible points

• Language toolsLanguage tools

• Automatic visualizationAutomatic visualization

• Sensitivity AnalysisSensitivity Analysis

• OptimizationOptimization

Efficient Adaptation of a simulation for Efficient Adaptation of a simulation for reuse and component selectionreuse and component selection

• Flexible pointsFlexible points

• Language toolsLanguage tools

• Automatic visualizationAutomatic visualization

• Sensitivity AnalysisSensitivity Analysis

• OptimizationOptimization

Support userSupport user

seeking insightseeking insight

Coercion

Optimization

Traditionally used to find best parameter Traditionally used to find best parameter valuesvalues

Generates additional insightGenerates additional insight• Identify sensitive/brittle systemsIdentify sensitive/brittle systems

• Explore novel circumstancesExplore novel circumstances

• Detect correlationsDetect correlations

• Discover constraintsDiscover constraints

• Bound search spaceBound search space

Traditionally used to find best parameter Traditionally used to find best parameter valuesvalues

Generates additional insightGenerates additional insight• Identify sensitive/brittle systemsIdentify sensitive/brittle systems

• Explore novel circumstancesExplore novel circumstances

• Detect correlationsDetect correlations

• Discover constraintsDiscover constraints

• Bound search spaceBound search space

S0 S?

P: The Coercion Process

Btarget?

S?

S?

.

.

P: The Coercion Process

S0 P SnSatisfiesBtarget?

Modify

Run optimizeror modify?

Optimize

Yes

No

Btarget

Paths to Coercion

S0 P S?SnS0

Dangerous Divergence

SnS0

Distance Function

S0

Si

Distance Function

Btarget Ii

D(SD(Sii, I, Iii)) Insight is

vital!Coercion converges on solution when D(Sn, In)==0

The Convergence of Coercion

How can we guarantee coercion will How can we guarantee coercion will terminate?terminate?

• We benefit from our successesWe benefit from our successes

–Better satisfy BBetter satisfy Btargettarget with good transformations with good transformations

• We learn from our mistakesWe learn from our mistakes

–Gain insight from bad transformationsGain insight from bad transformations

How can we guarantee coercion will How can we guarantee coercion will terminate?terminate?

• We benefit from our successesWe benefit from our successes

–Better satisfy BBetter satisfy Btargettarget with good transformations with good transformations

• We learn from our mistakesWe learn from our mistakes

–Gain insight from bad transformationsGain insight from bad transformations

Focus on User Interaction with Optimization Tools

Explore what you don’t know and exploit what you do Explore what you don’t know and exploit what you do knowknow

Explore what you don’t know and exploit what you do Explore what you don’t know and exploit what you do knowknow

ExploreUnknown

Exploit Insight

Code Modification

Random Code Generation

Simulated Annealing

Genetic AlgorithmsGradient-Based Search

Response Surface Methodology

Running Example

GlobalMinimum

Simulated Annealing

GlobalMinimum

Simulated Annealing

ExploreUnknown

Exploit Insight

Simulated Annealing

Insight:

• Perturbation func.

• Cooling schedule

Genetic Algorithms

Genetic Algorithms

ExploreUnknown

Exploit Insight

Genetic Algorithms

Insight:

• Mutation func.

• Crossover func.

Gradient-Based Search

Gradient-Based Search

ExploreUnknown

Exploit Insight

Gradient-Based Search

Insight:

• Initial guess

Response Surface Methodology

Response Surface Methodology

ExploreUnknown

Exploit Insight

Response Surface Methodology

Insight:

• What to inspect

• Where to inspect

• What to infer

Optimization Techniques

Explore

Unknown

Exploit Insight

Simulated Annealing

Genetic Algorithms

Gradient-Based Search

Response Surface Methodology

Waziruddin et al. Fall SIW 2004

Identify/classify best practices for optimization in coercion

Set-up timeSet-up time

Computation timeComputation time

Technique preemptionTechnique preemption

Set-up timeSet-up time

Computation timeComputation time

Technique preemptionTechnique preemption

Set-up Time

Explore

Unknown

Exploit Insight

Simulated Annealing

Genetic Algorithms

Gradient-Based Search

Response Surface Methodology

More Set-up Time

Computation Time

Explore

Unknown

Exploit Insight

Simulated Annealing

Genetic Algorithms

Gradient-Based Search

Response Surface Methodology

More Computation Time

Explore

Unknown

Exploit Insight

Simulated Annealing

Genetic Algorithms

Gradient-Based Search

Response Surface Methodology

More Preemption

Technique Preemption

Summary

• Efficient simulation adaptation appears viable for simulation reuse

• Currently funded under NSF (ITR) DDDAS program: dynamic adaptation

• Quite interested in collaborations with application experts.

reynolds@cs.virginia.edu dbrogan@cs.virginia.edu