Transcript Document 7458951

Does PNACO make
airborne toxins?
What’s the best way
to find out?
Unclassified/For Official Use Only
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PAINT
RAPID:
Representation and Analysis of
Probabilistic Intelligence Data
Carnegie Mellon University
PI : Prof. Jaime G. Carbonell / [email protected] / (412) 268-7279
Dr. Eugene Fink / [email protected] / (412) 268-6593
Dr. Anatole Gershman / [email protected] / (412) 268-8259
DYNAMiX Technologies
POC: Dr. Ganesh Mani / [email protected] / (412) 401-0121
Mr. Dwight Dietrich / [email protected] / (724) 940-4304
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Motivation
To create innovative
management methods and
computational tools for
representation and analysis of
uncertainty in large-scale data
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PAINT Architecture
Targeted Data
Extraction
Strategy
Development
2
Data
1
Leadership
Interactions
3
4
Probes
Pathways
REAL WORLD
RAPID: Uncertainty Calculus and Proactive Probe Planning
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RAPID Solution Features
• Automatic impact evaluation of uncertainty on
analyst hypotheses and conclusions
• Recommends optimized plans for proactive
intelligence gathering
• Targeted reduction of system uncertainty by
directed collection of critical data
• Based on industry-standard platforms Excel and
Oracle
• Specialized enhancements for managing
uncertainty calculus
• Easy-to-use; no programming required
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RAPID Value Proposition
Provide analysts with an uncertainty decision
framework answering the following questions:
•
What conclusions can be drawn from the available uncertain
data, and at what level of certainty?
•
What missing data is critical for verifying specific
hypotheses, increasing the certainty of current conclusions,
refuting tentative conclusions, or drawing new conclusions?
•
What are the tradeoffs between the value of specific missing
data and the difficulty of obtaining it?
•
What is the most efficient way of verifying a given hypothesis
by collecting additional data?
•
What factors can change in the future, what are possible
implications of these changes, and which contingencies
should be analyzed?
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Intelligence
Analysis Workflow
New
intelligence
Filtering and
processing of
new intelligence
Propagation
of inferences
Analysis
of key
indicators
Development of
intelligencecollection plans
Intelligence
collection
Analysts
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Uncertainty
Management Architecture
Approved plans for
proactive data collection
Hypotheses,
conclusions, and
data-collection plans
Analyst
interface
Value-added
reasoning tools
Uncertain situation assessment
and data-collection planning
Uncertainty calculus and
proactive probe planning
Proactive
intelligence
collection
New
intelligence
Excel extension for the analysis
of uncertainty, probes, and
proactive data collection
Processing of
data streams
Matching of queries and
inferences against new data
General
intelligence
collection
Scalable assessment of
uncertain intelligence
Relational database of uncertain
data and inference rules
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Functional Components
•
Uncertainty calculus and probe planning: Analysis of
incomplete data, identification of critical uncertainties, and
selection of probes; these tools are integrated into
Microsoft Excel.
•
Scalable assessment of uncertain intelligence: A database
of incomplete and uncertain facts, uncertain inference
rules, and hypotheses, which allows scalable planning of
proactive data collection; these tools are integrated into
Oracle.
•
Analyst interface: Integrated access to all system
components, which extends the standard Excel interface.
•
Value-added reasoning tools: Optional tools for the
automation of related reasoning tasks, such as entity coreference, contingency analysis, and adversarial search.
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Integration Features
• Novel uncertainty analysis tools built on industrystandard Excel and Oracle platforms
• Leverages all existing integration features of
Excel and Oracle
• Familiar user interface allows users of Excel to
quickly and easily build task-specific uncertainty
analysis spreadsheets
• General-purpose Application Programming
Interface (API) enables integrations with 3rd party
software packages including other PAINT
subsystems and components
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Uncertainty Calculus
and Proactive Probe Planning
Analyst GUI
Uncertainty analysis
• Representation of probability
distributions and qualitative
uncertainty
• Uncertainty arithmetic
Proactive data collection
Microsoft Excel
• Representation of probes
• Evaluation of probe utility
• Automated selection and
launching of critical probes
Situation assessment
• Representation of data utility
• Tracking utility changes
during data collection
• Identification of critical
uncertainties
Processing of
data streams
Contingency planning
What-if analysis of alternative
future developments and datacollection plans based on an
extension of Excel “scenarios”
Uncertainty database
Optional
RAPID tools
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Generating Probes
to Minimize Uncertainty
1 2 3 4 5 6 7 8 9 10
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Novel Research (1 of 3)
Management of uncertain data:
• Semi-automated collection and analysis of uncertain
data
• Enables analysts to create a database of uncertain
knowledge
• Track changes in the available data and implications of
these changes
• Underlying mechanisms include:
• reasoning with uncertain data
• indexing structures for fast retrieval of exact and
approximate matches
• inference rules for describing relations among data.
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Novel Research (2 of 3)
Hypothesis formulation and validation:
• Semi-automated
formulation
of
new
hypotheses, their verification, and tracking
changes in their estimated validity.
• Underlying algorithms include:
• fast inference propagation
• game-theoretic analysis of goals
• possible actions of adversarial groups
• automated explanation of inferences to
human analysts.
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Novel Research (3 of 3)
Data-collection planning:
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•
•
•
Identification of critical missing data
Analysis of its utility and costs
Planning of proactive data-collection strategies
We are developing:
• active-learning techniques for the identification of
key indicators
• game-theoretic techniques for the planning of
proactive intelligence collection
• Analyst assistance for developing plans for resolving
critical uncertainties at the lowest possible cost.
• mechanisms for the evaluation of trade-offs between
the utility and costs of missing data, and
contingency analysis of future developments.
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Database Architecture
Analyst GUI
Manual entry, selection,
and editing of knowledge
Uncertain
facts
Uncertain
inference
rules
Semantic
network
Prioritized plans
for proactive
data collection
Goals,
queries, and
hypotheses
Learned
inference
rules
Inferred
facts
Critical
uncertainties
Conflict
detection
Query
matches
Evaluation of
hypotheses
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Does PNACO make
airborne toxins?
Answer: Probably not,
P  7%
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