Clymer_1 by Waterloo Institute for Complexity and Innovation

Simulation-Based Engineering of Complex Systems Dr. John R. Clymer, INCOSE Fellow

Module 1: System of Systems (SOS), Simulation-Based Systems Engineering, Simulation Methodology University of Waterloo October 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 2

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. 3

Module1 TOPICS • • • •

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. 5

System Emulation Improves Design (Continued) • A “Co-evolutionary” System of Systems (SOS) has behavioral patterns that are constantly changing. There is no steadystate: – Static use cases and I/O timelines are not sufficient; – Simulation of the system in its dynamic, operational coevolutionary 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. 6

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. 7

Module1 TOPICS • • • •

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. 9

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 10

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. 11

Intelligent System Concept for Complex SOS (3)

Create manageable decision set

• 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. 12

Intelligent System Concept for Complex SOS (4) Select Reward Criteria

• 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. 13

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

Module1 TOPICS • • • •

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

Definition of Engineering of a System â&#x20AC;˘ Discipline that develops, matches, and trades off concepts, requirements, functions, and alternate resources to achieve a costeffective, life-cycle-balanced product based upon the needs of the stakeholders (Buede 2000). â&#x20AC;˘ 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) 17

SYSTEMS ENGINEERING LIFECYCLE Grey Area is Systems Engineering Process

Stakeholder Needs Lead to Research and Idea Invention

Discipline Engineers (Aerospace, Electrical, etc)

Buede: “The Engineering Design of Systems: Models and Methods”, Wiley, 2000 18

Expertise Required on a Engineering Design Team

Buede: â&#x20AC;&#x153;The Engineering Design of Systems: Models and Methodsâ&#x20AC;?, Wiley, 2000 19

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

OpEMCSS Model of an Intelligent Systems Engineering Organization

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

“Define System level Problem” SE Function, OpEMCSS Version

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.

Module1 TOPICS • • • •

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. 26

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 27

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.” Module 2

CONTEXT-SENSITIVE INTERACTION

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. 30

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. 31

Each Intersection Is An Intelligent Traffic Control Agent

SINGLE AGENT 32

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 33

Traffic Control Model

Gain allows easy modification of the classifier offset decisions which are in the range of (0-15 sec) 34

When Traffic Lights Collaborate, Waiting Time in Network is Minimized

Emergent Behavior

Red: Payoff Blue: Avg Wait White: Avg Payoff Green: Offset

Average payoff (white) +8.5 indicates we have greatly reduced the overall average waiting time in the network. 35

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

Animation Grid for World Model

World Model comprised of many agents with random initial placement and vectors 37

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. 38

Module1 TOPICS • • • •

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 topdown structure. – Model the system interacting with its dynamic operational environment including entity motion and spatial interactions. 40

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. 41

OpEMCSS Systems Engineering Methodology OpEMCSS Design Agent

Agent-Based System Design Optimizer: Control Rules and Component Architecture and Interfaces

OpEMCSS Design Emulation

Emulation of Actual System Functions and Architecture in the Dynamic Operational Environment (Systems Analysis)

1 2

Design Capture Data-Base: Requirements, Functions, Architecture, Interfaces

Current SE Tools

(System Definition)

This interface provides MOE and MOP statistics to System Definition (results). This interface provides values, margin, variables, KPPâ&#x20AC;&#x2122;s to dynamic model.

Module1 TOPICS • • • •

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. 44