Fundamentals and Principles of Information...
Transcript of Fundamentals and Principles of Information...
Importance of Semantic
Ontologies in Information Fusion
Erik Blasch
AFRL
Glue for High Level
Information Fusion
Erik Blasch – STIDS16 2
Abstract
The use of semantic technologies has essential implications for information fusion systems solutions.
An emerging development in high-level information fusion (HLIF) is the importance of the user for mission management, command and control, as well as process refinement. The ability of the user to be part of the systems solution supports low-level information fusion (LLIF) functions of object, situation, and impact assessment. Future technology designs will require coordinating the LLIF physics-based big data measurements with the HLIF human-derived information content.
A semantic ontology is necessary for physics-based and human-derived information fusion (PHIF). The fusion of measurements and content should augment contextual understanding, refine uncertainty estimates, and provide robust decision support. This talk will provide trends in high-level information fusion, address developments in an uncertainty ontology, and provide examples of PHIF.
Examples include unmanned aerial vehicle (UAV), multi-intelligence, and space situation awareness…. IoT
Erik Blasch – STIDS16 3
Outline
High-Level Information Fusion
1) Cyber-Physical Systems (CPS) and Internet of Things (IoT)
Methods to utilize data from IoT sensors (physics-based)
Mission needs (human-derived) requirements
2) Challenges in High Level Information Fusion
Ontology to link LLIF-HLIF problem definitions
Ontology to support uncertainty analysis
3) Uncertainty Analysis
Developments from the URREF
4) Examples
UAV traffic management
Machine Analytics
5) Summary
Erik Blasch – STIDS16
Level 0 SAW
SENSATION
Level 1 SAW
PERCEPTION
Level 2 SAW
COMPREHENSION
Level 3 SAW
PROJECTION Human
Situation
Awareness (SAW)
Machine Level 1 MIF
OBJECT
ASSESSMENT
Level 0 MIF
SUB-OBJECT
ASSESSMENT
Machine Information Fusion (MIF)
Interface
Observables
(dots on maps)
Situations
(storytelling)
Objects
(lines on maps)
Scenarios
(forecasting)
Low-level Information Fusion High-level Information Fusion
Visualization
(Evaluation)
Level 2 MIF
SITUATION
ASSESSMENT
Level 3 MIF
IMPACT
ASSESSMENT
Low-Level & High-Level Information Fusion
Decompose problem into elements of LLIF and HLIF
Determine the user (situation awareness) and machine (computation)
Discussion on evaluation/visualization and projection
Erik Blasch – STIDS16
Previous Panels What is HLIF,
• How to measure HLIF, and • Coordination machine/user.
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Summary – Top Ten Trends
• Area A. Data/Knowledge Representation • Reference Model • Taxonomy of notations, symbols, and meanings
•Area B. SA/TA/IA Assessment • Semantics/ontologies • Social/Behavioral/Cultural Models
•Area C. Systems Design • User/agent coordination • Display (interactive)
•Area D. Evaluation • Common scenario, Perf. comparison • Metrics / Uncertainty analysis
•Area E. Information Management • Resource planning and information analysis • Joint theory of methods integration
Determined from 2000-2010 Panel Discussions at the International Conference on Information Fusion
Blasch, et. al.“High Level Information Fusion (HLIF) Survey of Models, Issues, and Grand Challenges,”
IEEE AES Mag., Aug 2012.
Evaluation of Techniques for Uncertainty Representation (ETUR) Working Group,
• uncertainty analysis • ontologies
Erik Blasch – STIDS16
Situation Uncertainty
Time
Space
Accuracy
Timeliness
Confidence
Meaning (Knowledge)
Representation SA/IA/TA
Assessment
User Coordination
System Design
Metrics (Evaluation)
Information Management
HLIF Uncertainty Issues (Systems Approach)
Blasch, et. al.“High Level Information Fusion (HLIF) Survey of Models, Issues, and Grand Challenges,”
IEEE AES Mag., Aug 2012.
Erik Blasch – STIDS16 7
Summary
Panel 2000 2001 2004 2005 2006 2007 2008 2009 2009 2010 Topic Vision L2-4 HLF KR-RM RM Agent HLIF Coalition TA/IA HLIF-GC
Reference Model
Data/Knowledge Representation O O O O O O
Semantics/Ontologies O O O O O O
SA/TA/IA Assessment
Social/Behavioral Model
User/Agent Coordination
Display (Interactive) X X X X
Common Scenario X X X X
Performance Eval/Metrics
Uncertainty Analysis █ █ █ █ █ █ █ █
Resource Planning
Joint Theory of Methods X X X X X
Current and Consistent Theme
O Key Importance
X General Importance
E. P. Blasch, D. A. Lambert, P. Valin, M. M. Kokar, J. Llinas, S. Das, C-Y. Chong, and E. Shahbazian, “High Level
Information Fusion (HLIF) Survey of Models, Issues, and Grand Challenges,” IEEE AES Mag., Aug 2012.
Erik Blasch – STIDS16 8
Outline
High-Level Information Fusion
1) Cyber-Physical Systems (CPS) and Internet of Things (IoT)
Methods to utilize data from IoT sensors (physics-based)
Mission needs (human-derived) requirements
2) Challenges in High Level Information Fusion
Ontology to link LLIF-HLIF problem definitions
Ontology to support uncertainty analysis
3) Uncertainty Analysis
Developments from the URREF
4) Examples
UAV traffic management
Machine Analytics
5) Summary
Erik Blasch – STIDS16 9
Cloud, CPS, IoT
NIST: CPS
NIST Cyber-Physical Systems Public Working Group; Draft Framework to Help ‘Cyber Physical Systems’ Developers
https://pages.nist.gov/cpspwg/
https://pages.nist.gov/cpspwg/
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Cloud, CPS, IoT
Development of Human-Internet-Machine
http://www.sose2016.org/internetofthings.html
CyPhERS: “Cyber-Physical European Roadmap & Strategy” deliverable D5.2 CPS: Significance,
Challenges and Opportunities, from
Ref:
CyPhERS: “Cyber-Physical European Roadmap & Strategy” deliverable D5.2 CPS: Significance, Challenges and Opportunities, from
Erik Blasch – STIDS16
User-Machine Learning
Multiple Users – OODA Loops, Functions
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Observe
Orient
Decide
Act
SENSING
Exploitation
LEARNING
Data Mining
Analyze
Prepare
Prob
Discover
Real-time
OPERATOR
Forensic
ANALYST
Information Exploitation – both sensing and mining
- Enabled by cloud computing, analytics, and systems
E. Blasch, A. Steinberg, S. Das, J. Llinas, C.-Y. Chong, O. Kessler, E. Waltz, and F. White, "Revisiting the JDL model for information Exploitation," Int’l Conf. on Info Fusion, 2013.
Physical
Cyber
Erik Blasch – STIDS16
Why Cross-Layer Design in IoT
• Ubiquitous communications, autonomous and self-aware device operation and handling of multiple sensed data of varying characteristics:
Physical
Data Link
Network
Transport
Application
Communication Protocol Stack
Info
rmati
on
Information
Physical Component
Info
rmati
on
Connectivity
Processing/Computing
• IoT devices need to access information from different layers of the cyber physical system and
• Can process the information in an integrated manner.
• Information needs to be integrated at a processing element for the IoT device to feature self-aware characteristics and be able to operate autonomously.
Andres Kwasinski, “Cross-Layer Framework in the Internet of Things for Cyber-Physical Systems,” SPIE 2016
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Architecture for Cross-layer IoT
• All-layer cognitive agent module:
• A software module that gathers information from the different layered components of an IoT device.
• Able to develop the functions of self-awareness and autonomous operation while also bridging the separation between layers.
Based on Observe-Decide-Act cognitive cycle
Andres Kwasinski, “Cross-Layer Framework in the Internet of Things for Cyber-Physical Systems,” SPIE 2016
Erik Blasch – STIDS16
What is the IoT?
• Lack of uniform agreement.
• Adopted view:
three distinct features for an instance of IoT application:
1) awareness - as a result of a sensing/data collection operation,
2) autonomy - complete operation without human intervention,
3) actionable - using the results from the data processing for decision making and operation.
• Focused IoT application: integration with infrastructure to enable a cyber-physical system called a “smart infrastructure”;
• Example: smart grid.
Andres Kwasinski, “Cross-Layer Framework in the Internet of Things for Cyber-Physical Systems,” SPIE 2016
• Dramatic growth in Internet-connected devices- Most of this grow will come from sensing and actuation devices that act as nodes in the Internet of Things (IoT).
Source: "IMT Traffic Estimates for the Years 2020 to 2030," International Telecommunication Union (ITU)
technical report M.2370-0, July 2015.
Erik Blasch – STIDS16
Application Case: Powering Cellular Base Stations From the Smart Grid
• IoT has had a key role in modernizing the electric grid – the “smart grid”.
• One development from the smart grid: microgrids.
• Microgrid: electric power grids that are confined to a local area and which can operate connected to or isolated from a main grid because loads and local energy sources (generators or energy storage devices) are integrated through a controller that operates independently of the grid.
• ``Sustainable Wireless Area'' (SWA): an architecture that integrates a group of cellular base stations in a microgrid with the goal of maximizing the use of renewable energy to power the cellular infrastructure
New management dimension –
Shape video and data traffic.
Traffic shaping is reflected on cellular service quality experienced by end users.
Andres Kwasinski, “Cross-Layer Framework in the Internet of Things for Cyber-Physical Systems,” SPIE 2016
Erik Blasch – STIDS16
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Fig. 9: Big Data Management for Smart Grid
Big Data in Smart Grid Wei Yu, “On Secure and Resilient Energy-Based Critical Infrastructure ,” SPIE 2016
Smart grid must be dependable, cost-effective, secure, and operate in real-time
High volume data streams associated with smart grid operations need to be quickly
processed and analyzed (power grid to energy management system (EMS))
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Outline
High-Level Information Fusion
1) Cyber-Physical Systems (CPS) and Internet of Things (IoT)
Methods to utilize data from IoT sensors (physics-based)
Mission needs (human-derived) requirements
2) Challenges in High Level Information Fusion
Ontology to link LLIF-HLIF problem definitions
Ontology to support uncertainty analysis
3) Uncertainty Analysis
Developments from the URREF
4) Examples
UAV traffic management
Machine Analytics
5) Summary
Erik Blasch – STIDS16
BOOK: High-Level Information Fusion
Erik Blasch – STIDS16
High-Level Information Fusion Management and Systems Design
Hu
man
- M
ac
hin
e In
terfa
ce
Systems Design Machine Human
High-Level
Information Fusion
Info
rmati
on
/Reso
urc
e
M
an
ag
em
en
t
Situ
atio
n A
naly
sis
E
valu
atio
n
Low-Level Information Fusion
Perception
Object
Observable
Situation
Scenario
Sensation
Comprehension
Projection
Assessment Awareness
Erik Blasch – STIDS16
HLIF Book Outline
Human System Interaction
Information Fusion Concepts and Representations
Information Fusion Evaluation
4. Interpreted Systems
2. The IFSA Model
5. Role of Information Management
7. INFORM Testbed
8. Legal Agreement Protocol 10. C-OODA
11. SBD Applications
12. Coalition Approach
13. Operation Condition Model
15. Measures of Worthiness
16. Measures of Effectiveness
9. UDOP
14. Int. Sys. Evaluation Toolbox
3. The STDF Model
6. Coalition Testbed
Scenario-Based Design
Distributed Information Fusion and Management
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High-Level Information Fusion Management and Systems Design
1. Overview the HLIF problem (~ 1 hour)
Architecture, domain, algorithms, purpose (SA Approaches)
2. Methods for Situation Awareness (~ 1 hour)
Set up analysis of SAW/SA (functional)
Describe three types of approaches
Process, Interpreted, and State Transition
Develop notions of SA Prediction/Projection
3. Develop a IF Management and System Level Design (~ 1 hour)
Present System Management and Testbeds
Human Factors issues (C-OODA, UDOP)
4. Demonstrate HLIF Evaluation and Scenario Design (~ 1 hour)
Determine the design, testing, scenarios, and operability
Evaluation Methods
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Motivation
1. Three years of discussion
Focused on the main issues in HLIF
See companion paper in Fusion Panel Studies
See other tutorial on Evaluation
2. Collaboration (SUM)
Sensor Management - HLIF is about different INTs
User – HLIF is about a collection of users
Mission – HLIF is about focusing on the goal (Top-Down)
3. Each Coordination brought together ideas
Technical panels – C3I, Info Mgt, User, and Testbeds
Countries and perspectives – each had end-to-end solution
4. Developments fostered from the Grand Challenges
Issues to explore in the next decade
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Lesson 01: HLIF Overview
1. Overview the HLIF problem (~ 1 hour)
HLIF Architectures: JDL to Data Fusion Information Group (DFIG)
Grand Challenges
Paradigm , Semantic , Epistemic : HLIF Purpose
Interface, System: HLIF Management
Design, Evaluation : HLIF Design
Set up analysis of SAW/SA (functional)
SA Approaches
Process (DFIG) – US [Blasch, Salerno, Tangney]
Interpreted Systems (IS) /ODDA – Canada [Bosse, Jousselme/Maupin, Valin]
State Transition Data Fusion (STDF) – AUS [Lambert]
Common Issues: Metrics, Design, Future Concentrations
2. Methods for Situation Awareness (~ 1 hour)
3. Develop a IF Management and System Level Design (~ 1 hour)
4. Demonstrate HLIF Evaluation and Scenario Design (~ 1 hour)
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High Level Fusion Adapted from E. Waltz and J. Llinas, Multisensor Data Fusion, Artech House, Norwood, MA [1990])
Low-Level Processing
Sensor 1
Detection
Sensor 2
Detection
Sensor 3
Detection
Data
Association
State Estimation
Attribute Classification
Predicted States of
Targets in Track
Estimated Tracks
State Estimates
Target Identities
Low-Level Assessment
High-Level Processing
Assessment Detection of Pattern of Behavior
Association of Entities and Events
Prediction of Future Behavior
Classification of Situation
High-Level Assessment
On Situation
Behavior
Future Activities
Intent
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User Fusion Model From E. Blasch and S. Plano, “DFIG Level 5 (User Refinement) issues supporting Situational Assessment
Reasoning,” Int. Conf. on Info Fusion - Fusion 05, July 2005.
Distributed
Local
Intel SIG
IM
EL
EW
Sonar
Radar HRR
SAR
MTI
Data Sources
Distributed
Human
Computer
Interface
Information
Sources
Level 4
Process
Refinement
DataBase Management System
Support
DataBase Fusion
DataBase Sensor Management
Level 2 – Situation Assessment
Level 3 – Impact Assessment
Level 1 – Object Assessment
Level 0 – Pre-processing Level 5
USER
Refinement
Interface
Design
Team
interaction
Proactive SF
Effectiveness Risk Throughput Utility
Performance Assessment/ Metrics
Context
Intent
Value
Priority
SOFT
HARD
Erik Blasch – STIDS16
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L 4
DFIG - Fusion Model (Data Fusion Information Group), Fusion 2006 (from 2004)
Real
World
Platform
Ground
Station
Sensors
And
Sources
Human
Decision
Making
Resource Management
Mission Management
Explicit
Fusion
Machine
Tacit
Fusion
Human
Info Fusion
L 0
L 1 L2/3 L 5
Reasoning
Knowledge Representation
E. Blasch, I. Kadar, J. Salerno, M. M. Kokar, S. Das, G. M. Powell, D. D. Corkill, and E. H. Ruspini, “Issues and challenges of knowledge representation and reasoning methods in situation
assessment (Level 2 Fusion)”, J. of Advances in Information Fusion, Dec. 2006.
L 6
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DFIG - Fusion Model (Data Fusion Information Group), Fusion 2006 (from 2004)
Low Level Information Fusion (LLIF)
Level 0 Data Assessment: estimation and prediction of signal/object observable states on the basis of pixel/signal level data association (e.g. information systems collections);
Level 1 Object Assessment: estimation and prediction of entity states on the basis of data association, continuous state estimation and discrete state estimation (e.g. data processing);
High Level Information Fusion (HLIF)
Level 2 Situation Assessment: estimation and prediction of relations among entities, to include force structure and force relations, communications, etc. (e.g. information processing);
Level 3 Impact Assessment: estimation and prediction of effects on situations of planned or estimated actions by the participants; to include interactions between action plans of multiple players (e.g. assessing threat actions to planned actions and mission requirements, performance evaluation);
Level 4 Process Refinement (an element of Resource Management): adaptive data acquisition and processing to support sensing objectives (e.g. sensor management and information systems dissemination, command/control).
Level 5 User Refinement (an element of Knowledge Management): adaptive determination of who queries information and who has access to information (e.g. information operations) and adaptive data retrieved and displayed to support cognitive decision making and actions (e.g. human computer interface).
Level 6 Mission Management (an element of Platform Management): adaptive determination of spatial-temporal control of assets (e.g. airspace operations) and route planning and goal determination to support team decision making and actions (e.g. theater operations) over social, economic, and political constraints.
E. Blasch, I. Kadar, J. Salerno, M. M. Kokar, S. Das, G. M. Powell, D. D. Corkill, and E. H. Ruspini, “Issues and challenges of knowledge representation and reasoning methods in situation
assessment (Level 2 Fusion)”, J. of Advances in Information Fusion, Dec. 2006.
Erik Blasch – STIDS16
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Fusion Model Comparisons
Activity DFIG SAW Model OODA C-OODA
Command
Execution
Level 6 Resource
Tasking
Act Action
Implementation
Decision
Making
Level 5 User Control
User Refinement
Decide Recall
Evaluate
Sensor
Management
Level 4 Decision Making
Impact
Assessment
Level 3 Projection Orient Projection
Situation
Assessment
Level 2 Comprehension Comprehension
Object
Assessment
Level 1 Object
Assessment
Feature Matching
Signal/Info
Processing
Level 0 Signal/Feature
Processing
Observe Perception
Data
Acquisition
Sensing
Registration
Data Gathering
* DFIG (Data Fusion Information Group), SA(Situation Assessment) - J. of Adv. in Info. Fusion, Dec. 2006.
* C-OODA (Cognitive Observe, Orient, Decide, Act) – Fusion11, 2011
E. Blasch, R. Breton, P. Valin, and E. Bosse, “User Information Fusion Decision Making
Analysis with the C-OODA Model,” Int. Conf. on Info Fusion - Fusion11, 2011.
Erik Blasch – STIDS16 29
Lesson 01: HLIF Overview
1. Overview the HLIF problem (~ 1 hour)
HLIF Architectures: JDL to Data Fusion Information Group (DFIG)
Grand Challenges
Paradigm , Semantic , Epistemic : HLIF Purpose
Interface, System: HLIF Management
Design, Evaluation : HLIF Design
Set up analysis of SAW/SA (functional)
SA Approaches
Process (DFIG) – US [Blasch, Salerno, Tangney]
Interpreted Systems (IS) /ODDA – Canada [Bosse, Jousselme/Maupin, Valin]
State Transition Data Fusion (STDF) – AUS [Lambert]
Common Issues: Metrics, Design, Future Concentrations
2. Methods for Situation Awareness (~ 1 hour)
3. Develop a IF Management and System Level Design (~ 1 hour)
4. Demonstrate HLIF Evaluation and Scenario Design (~ 1 hour)
Erik Blasch – STIDS16 30
High Level Information Fusion Challenges
Focus of the text:
Paradigm Challenge: How should the interdependency between the sensor
fusion and information fusion paradigms be managed?
Semantic Challenge: What symbols should be used and how do those
symbols acquire meaning?
Epistemic Challenge: What information should we represent and how
should it be represented and processed within the machine?
Interface Challenge: How do we interface people to complex symbolic
information stored within machines to provide decision support?
System Challenge: How should we manage information fusion systems
formed from combinations of people and machines?
Design Challenge: How should we design information fusion systems
formed from combinations of people and machines?
Evaluation Challenge: How should we evaluate the effectiveness of
information fusion systems?
E. P. Blasch, D. A. Lambert, P. Valin, M. M. Kokar, J. Llinas, S. Das, C-Y. Chong, and E. Shahbazian, “High Level Information Fusion (HLIF) Survey
of Models, Issues, and Grand Challenges,” IEEE Aerospace and Electronic Systems Mag., Vol. 27, No. 9, Sept. 2012.
Erik Blasch – STIDS16 31
Joint HLIF Contributions HLIF Perspectives for Paradigm Challenges
AUS CAN US Paradigm
Challenge
Unifying human and machine
functional models across level 0 to 3
(STDF Ch3)
Formal models across level 0 to
level 3 fusion
(IS/ODDA – Ch 04)
Operational process models across
level 0 to level 5 fusion
(DIFG/IFSA Ch02)
Semantic
Challenge
Axiomatic semantics in First Order
Logics (FOLs) and Description Logics
(DLs) covering various metaphysical,
environmental, functional, cognitive and
social concepts
Axiomatic semantics in Modal
Logics covering various
metaphysical, environmental, and
functional concepts (Ch 4)
Operational semantics of computational
models to infer meaning over
environmental, functional, cognitive and
social concepts (Ch 2, Ch13, Ch 15)
Epistemic
Challenge
Cognitive agents with semantic,
epistemic (declarative facts and rules)
and episodic (procedural cognitive
routines) long-term memories
User (agent) with semantic,
epistemic (facts and rules), and
episodic (procedural) interactive
goals. Belief Theory (Ch 4, 7, Ch14)
Agents for workflow and service-based
semantic, epistemic (facts and rules) and
episodic (procedures) information
processing (Ch5)
Interface
Challenge
Interactive virtual news engaging
virtual advisers, battlespace, interactive
planning rooms, video, & newspapers
(web pages), Lexpresso controlled
natural language
Semantic and symbology
presentation, visualization, and
interactive sensor and mission
management (Ch 6, Ch 7, Ch 9)
Visualizations for a Common Operational
Picture (COP) with symbologies, info.
management, and collaboration tools (Ch
9). User refinement support to fusion
methods with cognitive theory (Ch10)
System
Challenge
Legal agreements between
combinations of CDIFT connected
human and machine cognitive
agents based on formal semantic
theories
OODA-based agent (Ch 7), state-
space approach, belief networks (Ch
4, Ch 7,Ch 14)
Use of ontologies and
workflow/service/human agents for the
CDIFT. Coordination of user/machine
fusion methods based on information
needs and tools (Ch10, Ch13, Ch16)
Design
Challenge
Use of synthetic development
environments containing track data,
intelligence reports, and various
domain knowledge
Track data, intelligence reports,
various domain knowledge,
simulations (Ch 6, Ch12)
Track data, intelligence reports, various
domain knowledge (Ch 6, Ch12)
Evaluation
Challenge
Probabilistic propositional set
disparity measures based on random
inference networks
OODA agents operate in a
distributed feedback loop (Ch 7)
Model checking techniques (Ch 14)
Bayes networks to measure probabilistic
variations from Operational Conditions (Ch
13) and derivation of MOEs from MOPs
(Ch 10)
Erik Blasch – STIDS16 32
HLIF Compare and Contrast (1)
Paradigm Challenge: How should the interdependency between the sensor
fusion and information fusion paradigms be managed?
Models: US IFSA framework (Ch 2); the AUS STDF framework (Ch 3); and
the Canadian IS framework (Ch 4).
COMMON:
• Promote situations as a fundamental construct of the world.
• Utilize the machine interpretation of situations and the machine
prediction of situations in the world.
• Represent situations in machines through states and time stepped
transitions between states.
CONTRAST:
• Situations : represented very formally under the IS and STDF frameworks
less formally under the IFSA framework.
• Machine processing of situations is characterized by formal logics under the IS
and by functional architecture process models under the STDF and IFSA
Erik Blasch – STIDS16 33
HLIF Compare and Contrast (2)
Semantic Challenge: What symbols should be used and how do those
symbols acquire meaning?
Meaning: US IFSA framework (Ch 2); the AUS STDF framework (Ch 3); and
the Canadian IS framework (Ch 4).
COMMON:
• States are implemented as knowledge representations within the machine.
• Knowledge representations can express sophisticated concepts well beyond
sensed characteristics.
• Transitions between states are understood as graphs.
CONTRAST:
• Semantics: IS and IFSA implement state vectors with operational semantics,
STDF: Mephisto engages propositional formulae with axiomatic semantics.
• State Transitions: IS and IFSA models use directed graphs.
STDF: graphs, expressed as regular expression cognitive routines with
procedural semantics (see Ch 12 for example), but actual state transitions
are simply expressed through knowledge base content.
Erik Blasch – STIDS16 34
HLIF Compare and Contrast (3)
Epistemic Challenge: What information should we represent and how
should it be represented and processed within the machine?
Complexity: Social Relationships
COMMON:
• Processing emphasis shifts from the world to the machine .
• LLIF processing is machine extracting content from information sensed .
• HLIF processing is machine imposing content on the sensed information.
• HLIF machines are termed agents.
• HLIF agent can only infer that a sensed airborne object poses a threat if it
imposes background knowledge about alliances, possible targets, et cetera.
CONTRAST:
• Cognition:
• STDF : ATTITUDE TOO Cognitive Model
• IS/C-OODA: Cognitive-OODA model (Ch 10)
• IFSA: User refinement composes cognitive refinement (UDOP)
Erik Blasch – STIDS16 35
HLIF Compare and Contrast (4)
Interface Challenge: How do we interface people to complex symbolic
information stored within machines to provide decision support?
Linking: Human Situation Awareness with Machines
COMMON:
• Pairing involves interfaces across the different levels of fusion
• Interface technology moves beyond the traditional “dots on maps” and “lines on
maps” technology of LLIF (UDOP in Ch 9, command and control graphical user
interface in Ch 7 and HiCOP in [4, 12, 13]).
CONTRAST:
• Modeling:
• IS/C-OODA and STDF same modal logic framework to both people and
machines.
• IFSA introduces additional fusion levels
• Role of Human : • IFAS : obtaining and utilizing human SAW;
• IS/C-OODA: directed toward decision support
• STDF: agnostic toward what is performed by humans and machines.
Erik Blasch – STIDS16 36
HLIF Compare and Contrast (5)
System Challenge: How should we manage information fusion systems
formed from combinations of people and machines?
Distributed : Collections of humans and clusters of machines: CoABS (Ch
06), IS (Ch 14), and LAP (Ch08)
COMMON:
• Information management is deemed fundamental (Ch 5, TTCP C3I TP3).
• Distributed infrastructure is used to facilitate interaction between clusters of
fusion machines (CDIFT Ch 6 and INFORM Ch 7).
• CDIFT as common HLIF testbed (TP1) - support interoperable fusion products.
CONTRAST:
• Coordination : to manage multi-agent engagements
• IS/C-OODA and IFAS use a game theoretic model for agent interaction
• STDF : employs an agreement protocol for agent interaction
• Ch 6 (TP3) Agent-based systems (CoABS) framework (Ch 6) employs the
knowledge acquisition in automated specification (KAoS) system to resource
constrain distributed agents.
Erik Blasch – STIDS16 37
HLIF Compare and Contrast (6)
Design Challenge: How should we design information fusion systems
formed from combinations of people and machines?
Content: Role of Agent
COMMON:
• Agent imposing content on the sensed information
• promotion of a scenario-based approach to the development of HLIF
• HLIF design system cannot occur without a rich context of the world in mind.
• Multi-national collaboration.
CONTRAST:
• Fidelity : to manage various levels of design
• IS/C-OODA and IFAS use a hierarchical model
• IFSA uses operational conditions of sensor, target, and environment
• STDF : employs a similar design across levels for design
Erik Blasch – STIDS16 38
HLIF Compare and Contrast (7)
Evaluation Challenge: How should we evaluate the effectiveness of
information fusion systems?
Metrics: IFSA (Ch15), IS/C-OODA (Ch 7, Ch14), STDF [14]
COMMON:
• Use of goals and missions
• Measures of content similarity or disparity assessments.
CONTRAST:
• IFSA and IS/C-OODA includes a number of SA measures
• MOPs: based on activities,
• Evidential reasoning to measure probabilistic relations,
• Game theory to measure action tradeoffs, and
• MOEs: Information theory for situation analysis
• The Australian offering [14] promotes probabilistic measures of the disparity
between sets of propositions.
Erik Blasch – STIDS16
HLIF Book Outline
Human System Interaction
Information Fusion Concepts and Representations
Information Fusion Evaluation
4. Interpreted Systems
2. The IFSA Model
5. Role of Information Management
7. INFORM Testbed
8. Legal Agreement Protocol 10. C-OODA
11. SBD Applications
12. Coalition Approach
13. Operation Condition Model
15. Measures of Worthiness
16. Measures of Effectiveness
9. UDOP
14. Int. Sys. Evaluation Toolbox
3. The STDF Model
6. Coalition Testbed
Scenario-Based Design
Distributed Information Fusion and Management
Erik Blasch – STIDS16 40
Information Fusion Situation Awareness Data Fusion Information Group and SA Reference Model
Platform
Ground
Station
Human
Decision
Making
Resource Management
Mission Management
Explicit Fusion
Tacit Fusion
Information Fusion
Reasoning
Knowledge Representation/ Discovery
S
O
U
R
C
E
S
D
A
T
A
Sensor mgt
Data mgt
Real World
Level 0
Object
Recognition
And
Tracking
Level 1 Level 2
Level 3 New Revised Models and Collection Requirements
Level 6
Level 4
Level 5
Situation Assessment
Knowledge of ‘Us”
Impact
(Changes)
Knowledge of ‘Them”
Knowledge
of ‘Us”
Possible Features
Plausible Features
Impact
Threat
X
issue for book, not intended to be a new model
HLIF Figure 2.8
Erik Blasch – STIDS16 41
Lesson 01: HLIF Overview
1. Overview the HLIF problem (~ 1 hour)
HLIF Architectures: JDL to Data Fusion Information Group (DFIG)
Grand Challenges
Paradigm , Semantic , Epistemic : HLIF Purpose
Interface, System: HLIF Management
Design, Evaluation : HLIF Design
Set up analysis of SAW/SA (functional)
SA Approaches
Process (DFIG) – US [Blasch, Salerno, Tangney]
Interpreted Systems (IS) /ODDA – Canada [Bosse, Jousselme/Maupin, Valin]
State Transition Data Fusion (STDF) – AUS [Lambert]
Common Issues: Metrics, Design, Future Concentrations
2. Methods for Situation Awareness (~ 1 hour)
3. Develop a IF Management and System Level Design (~ 1 hour)
4. Demonstrate HLIF Evaluation and Scenario Design (~ 1 hour)
Erik Blasch – STIDS16 42
IF Quality of Service Performance Measures
COMM Human
Factors
Info Fusion ATR TRACK
Delay Reaction Time Timeliness Acquisition /Run
Time
Update Rate
Probability of
Error
Confidence Confidence Prob. (Hit)
Prob. (FA)
Probability of
Detection
Delay Variation Attention Accuracy Positional
Accuracy
Covariance
Throughput Workload Throughput # Images No. Targets
Cost Cost Cost Collection
platforms
No. Assets
Stallings 2002 Wickens, 1992 Blasch, 2003 COMPASE
Morrison
Blasch, (DDB)
Hoffman 2000
E. Blasch, M. Pribilski, B. Daughtery, B. Roscoe, and J. Gunsett, “Fusion Metrics for
Dynamic Situation Analysis”, Proc. of SPIE, Vol. 5429, April 2004.
E. Blasch, I. Kadar, J. Salerno, M. M. Kokar, S. Das, G. M. Powell, D. D. Corkill, and E. H. Ruspini, “Issues and challenges of knowledge representation and reasoning methods in situation
assessment (Level 2 Fusion)”, J. of Advances in Information Fusion, Dec. 2006.
SUMMARY 3
Erik Blasch – STIDS16
Multi-sensor Data
Situation Assessment
Increasing Operational Relevance and Semantic Specificity
SAR/EO ILCD
SAR OLCD
EO OLCD
HSI / IR
SAR
•
•
•
User Coordination
Information Management
Verifying and Validating Assessment of Information Association SA/SAW
TESTBEDS
EO
STDF Model
OODA Model
DFIG/ IFSA Model
Scenario-Based Design
Evaluation
R
A
B
V
DA DP
DE
SK
EF SBD
Sensor,
User
Cueing
Data,
Info,
Management
Hu
ma
n
-
Information Management
Simulated World
Information Fusion
Hu
ma
n-M
ac
hin
e
Re
so
urc
e M
gt
Information Management
Simulated World
Information Fusion
Coalition - DIFT Model
proposes
accepts
Situation
Scenario
Analysis
IS Model
Awareness A,
Implicit
Knowledge
K,
Explicit Knowledge
X,
Erik Blasch – STIDS16
Correlation:
Very Strong Relationship
Strong Relationship
Weak Relationship
Research Selection
User
Requirements
Importance
to User
Accuracy 5
Timeliness 5
Confidence 5
Throughput 4
Cost 3
Relevance 4
Completeness 3
Kn
ow
led
ge
Rep
rese
nta
tio
n
Sit
ua
tio
n A
wa
ren
ess
Info
rma
tio
n Q
ua
lity
Co
ali
tio
n S
tan
da
rds
Inte
rop
era
ble
Sem
an
tics
Info
mra
tio
n
Ma
na
gem
ent
Th
eory
Sce
na
rio
Tes
tin
g
Hu
ma
n-M
ach
ine
Inte
rfa
ce
3 44 4 5 5Importance Weighting 3
Engineering Characteristics
Deployment
+
House of Quality
Erik Blasch – STIDS16 45
Outline
High-Level Information Fusion
1) Cyber-Physical Systems (CPS) and Internet of Things (IoT)
Methods to utilize data from IoT sensors (physics-based)
Mission needs (human-derived) requirements
2) Challenges in High Level Information Fusion
Ontology to link LLIF-HLIF problem definitions
Ontology to support uncertainty analysis
3) Uncertainty Analysis
Developments from the URREF
4) Examples
UAV traffic management
Machine Analytics
5) Summary
Erik Blasch – STIDS16 46
Example 1: UAV C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
NextGen
• A key example for NextGen system includes developments in the weather ontology, implemented in three operational capability phases [1]:
• Initial (2013): Significantly enhanced weather infrastructure providing modestly improved meteorological data to all users of the Nation's Air Transportation System.
• Midterm (2016): NextGen begins to implement automated decision assistance tools and algorithms for managing the air space, requiring high resolution weather forecasts and observations with a greater degree of accuracy and precision.
• Farterm (2022): NextGen weather must meet all meteorological and engineering performance requirements to support the NextGen traffic management systems.
SESAR
• One example is the European ATM Reference Model (AIRM). Specifically, they looked at Notices to Airman (NOTAM).
• NOTAMs provide weather and emergency updates to aviators in the form of text messages.
Erik Blasch – STIDS16 47
Example 1: UAV C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
• From: Semantic Driven Assurance for System Engineering in SESAR/NextGen,
Rainer Koelle, EUROCONTROL
Erik Blasch – STIDS16
Avionics Ontology
On-Demand Information Retrieval
Data
Acce
ss
Structured Authoring
Analytic Tools
Information
Loading
Information
Template Analyst Directory
Aviation Systems
Collaboration
• Decision Making
• Collection Management
• ATM
Data
Weather
NOTAMS
Flight Into
Airspace
No
rmalizati
on
Standardized Data
Taxonomy
Data Template
Ontology
Visualization Information
Catalog
• Mandates
• Policies
Automated Information
Retrieval
E. Blasch, “Ontologies for NextGen Avionics Systems,” IEEE/AIAA Digital Avionics Systems Conference, 2015.
Erik Blasch – STIDS16
Avionics Ontology
Ontology
Computer
Science
Information
Science
Knowledge Domain
Computation
Reasoning
Set of
Concepts
Concepts
Relationships
Subject of
Contains
Contains
Contains
Contains
Subject of Representation of
Description of
Makes
use of
Supports
Supports
Application of
Describes
Application of
Sructures
Uses
Affords
Enables
E. Blasch, “Ontologies for NextGen Avionics Systems,” IEEE/AIAA Digital Avionics Systems Conference, 2015.
Erik Blasch – STIDS16 50
Ex. 1: UAV – Ontology Representation C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Ontology Semantics
• The Ontology Web Language (OWL) and the Protégé tool [10] are selected to realize the ontology for the approach proposed.
• The main OWL components to be created are the concepts (classes), properties for individuals, and instances of classes (individuals). They are set for Avionics Analytics Ontology (AAO) as follows:
• Classes (concepts) can be atomic classes (stand-alone ones) or associate classes (subclasses) along with “is-a” links, e.g., airspace, weather, vehicle (aircraft), person, location, and building.
• Properties (roles); are basically relationships between classes (or eventually individuals), e.g., hasAirpace, hasLanding, hasRoute, hastatus, hasTakeoff, etc.
ThingAirport
Airspace
Route
Vehicle
Aircraft
Weather
is-a
is-a
is-a
is-a
is-a
is-a
• Individuals; they are instances of classes (objects), e.g. a Boeing 747-800 is an individual (instance of the class “aircraft”).
Erik Blasch – STIDS16 51
Ex 1: UAV – Ontology Representation C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Ontology Semantics
• Property restrictions along with classes and individuals are the building block to define axioms.
• Tbox: A terminological axioms (in particular, based on operators such as inclusion, equivalence, etc.)
• Abox: A set of assertional axioms (facts or assertions)
• The ABox and the TBox form the AAO knowledge base.
Set of axioms for the class “Aircraft”Set of axioms for the class “Route”Set of axioms for the class “Airport”Set of axioms for the class “Airspace”Set of axioms for the class “Weather”
Set of facts for the class “Aircraft”Set of facts for the class “Route”Set of facts for the class “Airport”Set of facts for the class “Airspace”Set of facts for the class “Weather”
TBox
ABox
Examples of terminological axioms
• Aircraft_A subclass of AircraftcannotLand and AircraftcanTakeoff
• Route_B subclass of NoLanding and Takeoff
• Airport_I subclass of LandingAirport and TakeoffAirport
• Airspace_IV subclass of FlyingAirspace
• ClearSky subclass of GoodWeather and VeryGoodWeather
Examples of assertional axioms
• AircraftcannotLand equivalent to Aircraft and (hasRoute only NoLanding)
• Takeoff equivalent to Route and (hasTakeoff only TakeoffAirport)
• NonFlyingAirspace equivalent to Weather and (hasWeather only VeryBadWeather)
• BadWeather equivalent to Weather and (Storm or ThuderStorm)
Erik Blasch – STIDS16 52
Example 1: UAV – Ontology Reasoning C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
ThingAirport
Airspace
Route
Vehicle
Aircraft
Weather
is-a
is-a
is-a
is-a
is-a
is-a
hasRoute
hasWeather
hasAirspace
hasTakeoff/Landing
Ontology Reasoning
• Reasoners are the engine for the knowledge-based reasoning queries.
• Aircraft have routes which in turn have a start point (departure or take-off from an airport) and an end point (landing in an airport).
• Airports have their own airspace that is part of a larger airspace. Airspaces have weather conditions as well as air traffic.
Erik Blasch – STIDS16 53
Example 1: UAV – Ontology Example 1 C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Case Study I: A Weather Condition on and nearby an Airport
Erik Blasch – STIDS16 54
Example 1: UAV – Ontology Example 1 C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Application Examples
Case Study I: A Weather Condition on and nearby an Airport
• The query inference results suggest that (from left to right)
• Aircraft A and B (Flight A and B) can take off but they will not be able to land.
• Aircraft C (Flight C) can take off and it will be able to land.
Erik Blasch – STIDS16 55
Example 1: UAV – Ontology Example 2 C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Application Examples
Case Study II: Weather Condition on and nearby an Airport, and Runway Availability
Erik Blasch – STIDS16 56
Example 1: UAV – Ontology Example 2 C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Case Study II: Weather Condition on and nearby an Airport, and Runway Availability
• The query inference results suggest that (from left to right)
• Aircraft C & D (Flight C & D) will not be able to land as planned in route C and D
• Aircraft B, C, & D (Flight B, C, & D) should be advised to change routes as planned (Route B, C, & D)
• Aircraft A & B (Flight A & B) will be able to land as planned in route A and B.
Erik Blasch – STIDS16 57
Example 1: UAV – Ontology Example 3 C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Case Study III: Aircraft Proximity in Navigation based on UAV Size
Erik Blasch – STIDS16 58
Example 1: UAV – Ontology Example 3 C. C. Insaurralde, E. Blasch, “Ontological Knowledge Representation for Avionics Decision-Making
Support,” IEEE/AIAA Digital Avionics Systems Conference, 2016.
Case Study III: Aircraft Proximity in Navigation based on UAV Size • The query inference results suggest that (from left to right)
• The Boeing 747 (due to its size and on-board pilots) requires 10 km.
• The UAV 1 and 2 can allow a distance of 3 km.
• The UAV 2 should keep a distance of 7 km (because its size) but it could be approached up to 3 km since it has a remote pilot who can be reached by ATM controllers.
• The Cessna 400 and UAV 3 require 7 km of minimum distance, even though the UAV 3 is small; it is autonomous (no pilot whatsoever).
• The UAV 4 is larger than the UAV 3 (no pilot) but it has a contactable remote pilot to deal with its waypoints.
Erik Blasch – STIDS16 59
World being reported World being sensed
Uncertainty Reasoning
- Computational cost - Performance - Consistency - Correctness - Scalability
Uncertainty Reasoning
- Computational cost - Performance - Consistency - Correctness - Scalability
? ? ?
? ? ?
?
InputInput
-- Relevance toRelevance to problemproblem -- Weight ofWeight of evidenceevidence -- CredibilityCredibility
OutputOutput
-- InterpretationInterpretation -- QualityQuality -- TraceabilityTraceability
Evaluation Framework Boundary
Uncertainty Representation
- Evidence handling - Knowledge handling
System Processes
Ideally, evaluating how the management of uncertainty affects the overall performance of a fusion
system would be just a matter of isolating the directly related aspects. However,
Real-life information fusion systems are usually too complex to allow for such a clear-cut separation,
Most of the aspects considered in system-wide performance are entangled with or influenced by uncertainty
representation considerations to some degree.
Isolating the impact of uncertainty handling in an IF system is thus a matter of understanding how
intertwined the choice of an uncertainty representation and reasoning approach is to the major
performance metrics used to evaluate the IF system itself.
Example 2: Machine Analytics E. Blasch, P. C. G. Costa, K. B. Laskey, H. Ling, and G. Chen, “The URREF Ontology for Semantic
Wide Area Motion Imagery Exploitation,” Proc. of the Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS), Fairfax, VA, October, 2012, pp. 88-95
Erik Blasch – STIDS16 60
Machine and Visual Analytics
Analytics and Information Fusion.
Fusion Machine Concept
Level 0 Scientific Access to data and pedigree of information and
issues of structured/unstructured data
Level 1 Information
(Images, text)
Development of graphical methods for data
analysis
Level 2 Descriptive Uses data mining to estimate the current state
(i.e. Machine learning) over different reasoning
of trends for modeling
Level 3 Predictive Future options from current estimates
Level 4 Prescriptive Sequencing of selected actions
Level 5 Visual Sensing Making and Reasoning
Level 6 Activity-Based
Analytics
Policy instantiation of desired outcomes as to a
focused mission
E. Blasch, A. Steinberg, S. Das, J. Llinas, C.-Y. Chong, O. Kessler, E. Waltz, and F. White, "Revisiting the JDL model for information Exploitation," Int’l Conf. on Info Fusion, 2013.
Erik Blasch – STIDS16
Machine Analytics: User Involvement
Multiple Users – OODA Loops, Functions
61
Observe
Orient
Decide
Act
SENSING
Exploitation
LEARNING
Data Mining
Analyze
Prepare
Prob
Discover
Real-time
OPERATOR
Forensic
ANALYST
Information Exploitation – both sensing and mining
- Enabled by cloud computing, analytics, and systems
E. Blasch, A. Steinberg, S. Das, J. Llinas, C.-Y. Chong, O. Kessler, E. Waltz, and F. White, "Revisiting the JDL model for information Exploitation," Int’l Conf. on Info Fusion, 2013.
Erik Blasch – STIDS16
Ex: Automating Data Analysis with Semantics
EO UAV
MWIR UAV
Wide-Area Motion Imagery
Building-mounted EO
Timeliness (duration fulfilled)
WAMI Data Analysis
Veracity (truthfulness)
Known terrain
Given an image, predict the story
quality
Completeness (Area requested)
(Day-Night)
Accuracy (spot requested - CEP)
Precision (point requested)
Specificity (reduce false alarms)
E. Blasch, P. C. G. Costa, K. B. Laskey, H. Ling, and G. Chen, “The URREF Ontology for Semantic Wide Area Motion Imagery Exploitation,” Proc. of the Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS), Fairfax, VA, October, 2012, pp. 88-95
Erik Blasch – STIDS16 63
Example 2: Machine Analytics: URREF E. Blasch, P. C. G. Costa, K. B. Laskey, H. Ling, and G. Chen, “The URREF Ontology for Semantic
Wide Area Motion Imagery Exploitation,” Proc. of the Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS), Fairfax, VA, October, 2012, pp. 88-95
Erik Blasch – STIDS16
Example 2: URREF
Erik Blasch – STIDS16
Example 2: Machine Analytics: CoT E. Blasch, P. C. G. Costa, K. B. Laskey, H. Ling, and G. Chen, “The URREF Ontology for Semantic
Wide Area Motion Imagery Exploitation,” Proc. of the Seventh International Conference on Semantic Technologies for Intelligence, Defense, and Security (STIDS), Fairfax, VA, October, 2012, pp. 88-95
Erik Blasch – STIDS16 66
Example 3: Space Situational Awareness
RSO (Resident Space Object)
X. Tian, G. Chen, T. Martin, K. C. Chang, T. Nguyen, K. Pham, E. Blasch, “Multi-entity Bayesian network for the handling of uncertainties at SATCOM Links,” Proc. SPIE, Vol. 9469, 2015.
Erik Blasch – STIDS16 67
Example 3: Space Situational Awareness
RSO (Resident Space Object)
Alexander P. Cox, et al.” The Space Object Ontology,” Int’t. Conf. On Information Fusion, 2016
Erik Blasch – STIDS16 68
Example 3: Space Situational Awareness
RSO (Resident Space Object)
Alexander P. Cox, et al.” The Space Object Ontology,” Int’t. Conf. On Information Fusion, 2016
Erik Blasch – STIDS16 69
Summary
Low-level Information Fusion (Context)
Cyber-Physical Systems (CPS) and Internet of Things (IoT)
Methods to utilize data from IoT sensors (physics-based)
High-Level Information Fusion (Content)
Ontologies and user involvement (visualization)
Mission needs (human-derived) requirements
Challenges in Information Fusion
Ontology to link LLIF-HLIF problem definitions
Ontology to support uncertainty analysis
Examples
UAV traffic management
Machine Analytics
Space tracking
Erik Blasch – STIDS16 70
Outline
High-Level Information Fusion
1) Cyber-Physical Systems (CPS) and Internet of Things (IoT)
Methods to utilize data from IoT sensors (physics-based)
Mission needs (human-derived) requirements
2) Challenges in High Level Information Fusion
Ontology to link LLIF-HLIF problem definitions
Ontology to support uncertainty analysis
3) Uncertainty Analysis
Developments from the URREF
4) Examples
UAV traffic management
Machine Analytics
5) Summary
Erik Blasch – STIDS16 71
Previous Texts
Focus of the text [1] Bosse, E., Roy, J., and Wark, S., Concepts, Models, and Tools for Information Fusion,
Artech House, Inc., Norwood, MA, 2007.
[2] Lambert, D. A., “Grand Challenges of Information Fusion,” Intl. Conf on Info. Fusion,
2003.
[3] Blasch, E. P., Llinas, J., Lambert, D. A., Valin, P., Das, S., Chong, C-Y., Kokar, M. M,
and Shahbazian, E., “High Level Information Fusion Developments, Issues, and
Grand Challenges – Fusion10 Panel Discussion,” Intl. Conf on Info. Fusion, 2010.
[4] Lambert, D. A., “A Blueprint for Higher-Level Fusion Systems,” Journal of Information
Fusion, Vol. 10, No. 1, pp. 6 – 24, 2009.
[5] Waltz, E., and Llinas, J., Multisensor and Data Fusion, Artech House, Norwood, MA,
1990.
[6] Blackman, S., and Popoli, R., Design and Analysis of Modern Tracking Systems,
Artech House, Norwood, MA, 1999.
[7] Das, S., High-Level Data Fusion, Artech House, Norwood, MA, 2008.
[8] Waltz, E., Knowledge Management in the Intelligence Enterprise, Artech House,
Norwood, MA, 2003.
[9] Hall, D. L., and Jordan, J. M., Human-Centered Information Fusion, Artech House,
Norwood, MA, 2010.
[10] Lambert, D. A., “Unification of Sensor and Higher-Level Fusion,” presentation at Intl.
Conf on Info. Fusion, 2006.