Mesas14 tolk open

A. Tolk, SimIS © 2014

This presentation was given in the plenary session of the first “Modeling and

Simulation for Autonomous Systems (MESAS)” workshop that was conducted in

Rome, Italy, May 5-6, 2014, organized by the M&S Center of Excellence

(MSCOE) of NATO.

The presentation was prepared and given by Dr. Andreas Tolk, Chief Scientist

at SimIS Inc., USA.

We like to thank our sponsor, Qbit Technologies, for their support to participate

in the inaugural MESAS event.

Andreas Tolk, Ph.D.Chief Scientist SimIS Inc.

Adjunct Professor Old Dominion University

Portsmouth, Virginia, USA

Systems Engineering Processes

for M&S-based Development and Testing

of Autonomous Capabilities


Structure of the Presentation

Systems Engineering Processes

– Systems Engineering

– System of Systems Engineering

– System Architectures

Autonomous Capabilities

– Taxonomy of an Autonomous System

– Levels of Autonomy

Modeling and Simulation

– Taxonomy of an Intelligent Software Agent

– Modeling as Theory Building

M&S-based Development and Testing

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SYSTEMS ENGINEERING PROCESSES

Topic One


Systems Engineering

Systems Engineering Processes ensure that

– capability gaps are identified,

– requirements are captured,

– system functionality is derived and located to components,

and the resulting system is defined regarding operations,

performance, test, manufacturing, cost and schedule, training and

support, and disposal.

The systems engineering processes bring team members from various

system phases and stake holders for the whole system life cycle

together to ensure that the resulting system meets the specifications.

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Life Cycle Phases

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Operation

Test Performance

Manu-

facturingCost &

Schedule

DisposalTraining &

Support


System of Systems

Operational Independence of the Individual Systems

– independent and useful in their own right

– component are capable of independently performing useful operations

Managerial Independence of the Systems

– operate independently

– maintain a continuing operational existence

Geographic Distribution

– Often large geographic dispersion

Emergent Behavior

– behaviors that is not the behavior of any component system

Evolutionary Development

– never fully formed or complete

– systems evolve over time

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Summary: SE/SoSE Processes

Processes needed for all

– Phases of the life cycle

– Team members

– Stake holders

Common System Architecture captures

– All facets and views needed

– Functionality required for the system

– Common model of components and interfaces

Context/Environment

– Other systems and objects of interest

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System Architecture should be the common Knowledge Repository

for all team members and stake holders of all phases.


AUTONOMOUS CAPABILITIES

Topic Two


Autonomy

Autonomy

– Origin: Autos : Self Nomos : Law

Having a self-government, independent from others

– The capability of a system to make decisions about its actions

without the involvement of another system or an operator. This also

entails entrusting the system to make these decisions.

– The ability of integrated sensing, perceiving, analyzing, communicating,

planning, decision-making, and acting/executing, to achieve its goals as

assigned.(Autonomy Level for Unmanned Systems (ALFUS, NIST)

Automation

– Using control systems and information technology to reduce the

need for human intervention within well defined constraints

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Taxonomy of an Autonomous System

Locomotion components

– Moving the system in its environment

(different degrees of freedom)

Actuator components

– Moving parts of the autonomous

system (robot arms, sensors,

antennas, etc.)

Manipulation components

– Interacting with environment (grab,

push, turn, etc.)

Sensor components

– Observing the environment (all kind of

sensors)

Signal processing components

– Converts sensor signals into

computable information

– Converts computed information into

actuator signals

Control components

– “Brain” of the autonomous systems,

makes the decisions

Communication components

– Exchange information with others

(robots, as well as humans)

Power supply components

– Energy source (usually battery or solar

panel)

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Levels of Autonomy

Teleoperation

– Remote control systems

Supervisory

– Control with the human, certain

specific movements left to the

system

Task-level Autonomy

– Operator specifies the task,

system executes it

Full Autonomy

– No human interaction, system

creates and completes tasks

Autonomy Levels for Unmanned

Systems (ALFUS); NIST

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Environmental

Complexity


MODELING & SIMULATION

Topic Three


Intelligent SW Agent Taxonomy

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Intelligent SW Agent Characteristics

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How do Intelligent SW Agents “understand?”

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Observed

System

Perception Meta-ModelsMapping

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Observing

System

Properties

Concepts

Processes

Constraints

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Sensor


M&S-BASED DEVELOPMENT

AND TESTING

Topic Four


Autonomous Systems vs. Intelligent Agents

Intelligent Agents

Sensor-based perception

Propertied concept based object

recognition

Model-based situation

recognition using adaptable

memory

Socially competent

Utility-driven decision making

Action layer to execute decision

Autonomous Systems

Real sensors used for perception

Many actions are reactions on

sensor input

– Bumper switches

– Optical sensor

Complex sensors require

complex mapping

– Camera

Communication possible

Locomotion, actuators, and

manipulators used to execute

action

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Recommendation

Intelligent SW Agents have much in common with Autonomous Capabilities

– Mapping of Taxonomies possible

– Same form of machine understanding, learning, planning, etc.

Properties of M&S-based Solutions

– Full control of the environment (environmental complexity)

– Full control of the tasks (mission complexity)

– Intelligent SW agents represent all algorithms needed

Sense making

Communication

Planning

Decision making

Learning

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Agent-based Solutions optimally represent Autonomous Capabilities in

Virtual Environments configured by Systems Architecture Artifacts


Example – US Navy Riverscout

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Questions / Point of Contact

Andreas Tolk, PhD

Chief Scientist SimIS

200 High Street #305

Portsmouth, VA 23704

United States

[email protected]

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