clymer_1

Simulation-Based Engineering of Complex Systems

Module 1: System of Systems (SOS), Simulation-Based

Systems Engineering, Simulation Methodology

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Dr. John R. Clymer, INCOSE Fellow

University of WaterlooOctober 6-7, 2010

Module1 TOPICS• Context-Sensitive Systems• System Emulation Improves System Design• System of Systems (SOS)• Definition of system, system engineering, and system

engineering process• Definition of CAS, CSS interactions, and example

complex systems• Requirements for Simulation-Based Systems

Engineering (SBSE)• Conclusions

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Context-Sensitive Systems• Context-Sensitive Systems (CSS) are systems

where the input-output system responses depend on the spatial and temporal context of the system.– Same input does not necessarily result in the same

output due to:• Evolving environmental interactions• System learning

– CSS are best described as collections of interacting concurrent processes that require structural components to execute the processes.

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System Emulation Improves Design

• Operational Evaluation Modeling (OpEM) for Context Sensitive Systems (CSS) or OpEMCSS is a simulation library that works with ExtendSim a product of Imagine That Inc.

• Emulation is a simulation of the actual system functions, architecture, and interfaces in its dynamic environment:– OpEMCSS is designed to do system

emulation and optimization.

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System Emulation Improves Design (Continued)

• A “Co-evolutionary” System of Systems (SOS) has behavioral patterns that are constantly changing. There is no steady-state:– Static use cases and I/O timelines are not sufficient;– Simulation of the system in its dynamic, operational co-

evolutionary environment is required for system design.• Instrumentation of an OpEMCSS simulation of a SOS to collect

data and compute MOEs/MOPs for systems analysis naturally leads to the qualification system requirements.

• Capabilities of existing SE tools are enhanced with OpEMCSS:– Evolve system design using a design optimizer intelligent

agent. This is discussed further later in this module.

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OpEMCSS Blocks: Add On ExtendSim Library

• Blocks required to simulate interacting concurrent processes.

• Blocks required to do evolutionary optimization of the system design– Evolutionary algorithm block can optimize architecture

component algorithms and methods used– Classifier System block optimizes system control decisions

• Blocks required to simulate motion and spatial interactions among a set of physical entities in the system and its environment.

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System of Systems (SOS)• Systems of Systems (SOS): Collection of systems where

each system independently provides specific services and can operate independently of the rest of the SOS– Additional services provided through collaboration of a

subset of the individual systems, creating synergies.– Co-Evolutionary Systems: The behavior of the SOS is complex

and often in constant flux as the environment evolves and interacts with the SOS:

• There is no steady state, fractal patterns are observed.– Examples of SOS having complex behavior:

• UAV network can provide different mission services to different users such as communication across the network.

• Intelligent traffic control network that reduces the total waiting time for vehicles traversing the network.

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Intelligent System Concept for Complex SOS (1)• Each system in the SOS transforms its raw perceptions and

internal state into decision facts that are conditions of the decision rules. Rules decide choice of actions.

• The decision evaluation system rewards or punishes the decision rules per the appropriateness of its choices. The system strengthens or lessens its confidence in the decision rules that led to each choice.

• What must be learned is what knowledge to share and what actions to take in order to collaborate with other systems in the SOS to achieve the additional system services and synergies.

• Patterns of system perceptions are constantly changing and require constant reinterpretation. There is no steady-state

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Intelligent System Concept for Complex SOS (2)

• Sensors perceive raw data.• Signal processing measures “what is out there” and

produces a set of features that define a feature space.

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• Feature Extraction first maps the feature space to a smaller set of decision features: the feature space is mapped into a decision space, optimizing Hamming distances.

• Classifier System next discovers the exact decision facts in the decision space that are most relevant to the agent’s decisions.

Create manageable decision set

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• Create reward or punishment criteria by evaluating the environment, based on the degree of goal satisfaction or from actual environmental payoff.

• Pass these criteria on to the Feature Extraction block (CASSimTM only) to compute fitness values to guide evolutionary search.

• Pass these criteria to the Classifier System to guide rule learning.

Select Reward Criteria

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SOS Systems Must be Intelligent and Adaptive to Create Complex SOS

• Transactions (material, knowledge, energy) flow through the system

• Intelligent Systems (Agents) share knowledge and adapt their behavior to process these transactions

• Patterns of agent behavior emerge as the agents achieve collaboration that provides additional services of the SOS while interacting with the system environment

• Patterns change (adapt) as the features of transaction flow change: SOS behavior is in constant flux– No steady state operation in co-evolutionary SOS– New sensory perceptions emerge that require decisions

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Module1 TOPICS• Context-Sensitive Systems• System Emulation Improves System Design• System of Systems (SOS)• Definition of system, system engineering, and

system engineering process• Definition of CAS, CSS interactions, and example



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Definition of System• Aggregation of end products and enabling products that

achieves a given purpose. -- EIA 632 (CMM v1.0)• Composite of people, products, and processes that

provide a capability to satisfy stated needs… includes the facilities, equipment (hardware and software), material, services, data, skilled personnel, and techniques required to achieve, provide, and sustain system effectiveness. -- DOD 5000.2R

• Set of components acting together to achieve a set of common objectives via the accomplishment of a set of tasks or functions. -- Buede

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Definition of Engineering of a System• Discipline that develops, matches, and trades

off concepts, requirements, functions, and alternate resources to achieve a cost-effective, life-cycle-balanced product based upon the needs of the stakeholders (Buede 2000).

• Intellectual, academic, and professional discipline the primary concern of which is to ensure that all requirements for a bioware/hardware/software system are satisfied throughout the life cycle of the system (Wymore - 1993)

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SYSTEMS ENGINEERING LIFECYCLE

Stakeholder Needs Lead to Research and Idea Invention

Grey Area is Systems

Engineering Process

Discipline Engineers

(Aerospace, Electrical, etc)

Buede: “The Engineering Design of Systems: Models and Methods”, Wiley, 2000

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Expertise Required on a Engineering Design Team

Buede: “The Engineering Design of Systems: Models and Methods”, Wiley, 2000

Buede Systems Engineering Process• Define System-Level Design Problem: Simulation begins with the

operational scenarios in order to understand the problem space and visualize alternative system solutions.

• Develop System Functional Architecture: Simulate system in dynamic operational environment to derive time-related requirements and system control rules.

• Design System Physical Architecture: Simulate system to optimize selection of component instantiations (algorithms and methods).

• Develop System Operational Architecture: Simulate system to evaluate alternative functional allocations and component architectures: verify all stakeholder requirements have been met.

• Develop Interface Architecture: Simulate system to identify data and control flow bottlenecks in the physical interfaces.

• Develop Qualification System: Instrument simulation of system to collect data and compute MOEs and MOPs– Final result is basis for qualification system requirements

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OpEMCSS Model of an Intelligent Systems Engineering Organization

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IDEF0 Diagram for “Define System level Problem” SE function by Dennis Buede

USED AT: CONTEXT:

NODE: TITLE: NUMBER:

AUTHOR:PROJECT:

NOTES: 1 2 3 4 5 6 7 8 9 10

DATE:REV:

WORKINGDRAFTRECOMMENDEDPUBLICATION

READER DATE

P.

Originating & System Requirements,

Objectives Hierarchy, Boundary & Qualification

System Requirements

System-level Operational

Concept Design Changes

LowerLayerChanges toRequire-ments

Develop Operational

ConceptA1111

Define System

Boundary with an External Systems

A1112

Develop, Analyze and

Refine Requirements

A1114

Obtain Approval of

Requirements Documentation

A1117

RequirementsIssues

Originating & SystemRequirements

SystemBoundary

Define Qualification

System Requirements

A1116

QualificationSystemRequirements

Develop System

Objectives Hierarchy

A1113

ObjectivesHierarchy

System Boundary & ObjectivesHierarchy

Stakeholders'Constraints

Stakeholders'Objectives

Ensure Requirements

FeasibilityA1115

Proven RequirementsFeasibility

Proven RequirementsInfeasibility

Qualification System Issues

Inputs of Stakeholders

Originating & System

Requirements

Approval orDisapprovalQualification

ConstraintsStakeholders'Uses

Stakeholders'Jurisdiction

Operational ArchitectureChanges to Requirements

Engineers'RequirementsIssues

Stakeholders'RequirementsIssues

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x

Engineering Design of a SystemDennis Buede

GMU Systems Engineering

Program

05/24/99

Define System-Level Design ProblemA111

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“Define System level Problem” SE Function, OpEMCSS Version

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System Concept Simulation • Mission Analysis occurs prior to requirements

analysis in order to define the stakeholders’ problem.

• Alternative system concept simulations are evaluated and traded off given stakeholder inputs.

• A concept is selected. Subsequently, the selected system concept simulation is used to insure that originating requirements defined are actually required for the selected concept.

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Definition of Complex Adaptive Systems• A system that has emergent behavior due to component

interactions….. OR• A system that has automated decision making where

decisions change with external environment or system context– Intelligent systems adapt by generating rules and testing them

by making decisions for the system – Rules are selected based on success or failure feedback from

the environment

Intelligent adaptive systems add the ability to create new rules for control of system behavior.

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Decision Making Dominated Systems

• Many modern systems are dominated by decision making• Intelligent Agent technology is often employed to

automate or assist decision making tasks• Intelligent systems are non-linear (same input does not

necessarily produce the same output) because decisions and functions change depending on environmental and system context making output unpredictable.

• Therefore, system network behavior must be modeled and understood at each level of network design decomposition

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Module 2 28

Coping With Extreme Complexity• Network consists of nodes and links• Nodes in complex networks include

intelligent and adaptive agents• Agents share node information and adapt

for collaboration• Adaptation allows very complex network

behavior to emerge from simple agent behavior

Engineering Intelligent Adaptive Systems requires a new way of thinking about Systems Engineering. It also requires specific methods to understand the Intelligent Adaptive System “space.”

CONTEXT-SENSITIVE INTERACTION

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Examples of Intelligent Adaptive Systems

• Traffic Grid Optimization– Optimize flow of vehicles through a metro traffic

grid• Sonar System

– Detect, classify, and track ship and submarine targets using intelligent adaptive agents based on strategic threat and proximity

• World Model – Example of a CAS where agents communicate and

adapt resulting in emergent behavior.

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Vehicle Network Traffic Control• Each intersection traffic control system is an

intelligent agent and part of an SOS• Agents apply fuzzy sets and fuzzy rules to

decide intersection offset time• Manual development of feature map• Manual rule development during design

process

Agents communicate and collaborate to minimize vehicle waiting time at intersections.

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Each Intersection Is An Intelligent Traffic Control Agent

SINGLE AGENT

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Features that Describe Traffic Flow

• STREAM measures how many vehicles are waiting• FLOW measures how many vehicles do not wait• OFFSET changes can reduce STREAM

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Traffic Control Model

Gain allows easy modification of the classifier offset

decisions which are in the range of (0-15 sec)

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When Traffic Lights Collaborate, Waiting Time in Network is Minimized

Red: PayoffBlue: Avg WaitWhite: Avg PayoffGreen: Offset

Average payoff (white) +8.5 indicates we have greatly reduced the overall average waiting time in the network.

EmergentBehavior

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Sonar Network Scenario• Surface Ships and Submarines enter the scenario randomly from anywhere on the circle•Surface Ships and Submarines are given a vector that moves through the sonar array• Each sonar sensor is an intelligent agent that learns rules (module 7) and collaborates with other agents to classify surface ships and submarines

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Animation Grid for World Model

World Model comprised of many agents with random initial placement and vectors

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World Model – Agent Interaction Results in Emergent Behavior

• When an agent comes to within “IntRng” of another agent, they decide on a common vector.• All other agents within “IntRng” adopt same vector.

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Module1 TOPICS• Context-Sensitive Systems• System Emulation Improves System Design• System of Systems (SOS)• Definition of system, system engineering, and

system engineering process• Definition of CAS, CSS interactions, and example



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Requirements for Simulation-Based Systems Engineering (SBSE)

• A successful SBSE modeling language must:– Visually show important concepts such as process threads

(sequences of tasks) and all process interactions. – Allow variable instantiation of concurrent process threads

that are described by the same modeling diagram. – Provide data structures to represent process instance state

variables and shared global memory and be able to communicate and share these variables.

– Capable of developing a model using a hierarchical top-down structure.

– Model the system interacting with its dynamic operational environment including entity motion and spatial interactions.

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Requirements for Simulation-Based Systems Engineering (SBSE) - Continued

• A successful SBSE modeling language must:– Describe the component and interface architecture in

order to evaluate the structural performance and efficiency, resource utilization, and system Reliability, Maintainability, and Availability (RMA).

– Describe the decision-oriented part of a system architecture and be able to automatically develop decision making rules to manage the system and collaborate.

– Describe specialty engineering issues. This list of issues is long; further, SEs refer to this class of requirements as quality features. A system engineer must relate these disciplines to product entities and their affect on system effectiveness and performance, cost, and schedule.

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OpEMCSS Systems Engineering Methodology

Emulation of Actual System Functions and Architecture

in the DynamicOperational

Environment

(Systems Analysis)

Design CaptureData-Base:

Requirements,Functions,

Architecture, Interfaces

(System Definition)

Agent-Based System Design Optimizer:Control Rules and

Component Architecture and Interfaces

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2 This interface provides values, margin, variables, KPP’s to dynamic model.

This interface provides MOE and MOP statistics to System Definition (results).

OpEMCSSDesign

Emulation

OpEMCSSDesignAgent

CurrentSE

Tools

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Conclusions• System of Systems (SOS): OpEMCSS library blocks are

intended for system emulation and optimal design.• Simulation-Based Systems Engineering (SBSE) of SOS Problem:

Co-evolutionary operational environment complicates system design: No steady state operation!

• Systems Engineering Process: Integrated and timely application of system simulation is absolutely required for SOS.

• Requirements for SBSE: Systems analysis and evaluation of a system operating in its complex, dynamic environment: evolutionary optimization of system behavior and structure

• Required Systems Engineering Methodology: Combination of the capabilities of OpEMCSS and existing SE tools is a desired research goal.

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clymer_1

Documents

system control decisionsblocks

actual system functions

inputoutput system responses

systems analysis

collection of systems

context sensitive systems

opemcss simulation

css interactions