fusion 2010 - prognos: predictive situational awareness with probabilistic ontologies
DESCRIPTION
Presentation given by Rommel Carvalho at the 13th International Conference on Information Fusion in 27 July 2010.TRANSCRIPT
PROGNOS: Predictive Situational Awareness with
Probabilistic OntologiesRommel Carvalho, Paulo Costa, Kathryn Laskey, and KC Chang
George Mason University
Paper - 13th International Conference on Information FusionFusion 2010
Thursday, July 15, 2010
Agenda
2
Thursday, July 15, 2010
AgendaObjective
2
Thursday, July 15, 2010
AgendaObjective
Methodology
2
Thursday, July 15, 2010
AgendaObjective
Methodology
Modeling the PO for MDA
Requirements
Analysis & Design
Implementation
2
Thursday, July 15, 2010
AgendaObjective
Methodology
Modeling the PO for MDA
Requirements
Analysis & Design
Implementation
Reasoning
2
Thursday, July 15, 2010
AgendaObjective
Methodology
Modeling the PO for MDA
Requirements
Analysis & Design
Implementation
Reasoning
Testing the PO for MDA
2
Thursday, July 15, 2010
AgendaObjective
Methodology
Modeling the PO for MDA
Requirements
Analysis & Design
Implementation
Reasoning
Testing the PO for MDA
Conclusion
2
Thursday, July 15, 2010
Objective
3Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Objective
4Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Objective
4
Develop a probabilistic ontology capable of reasoning with masses of evidence from different domains in order to provide situation awareness on maritime domain.
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Objective
4
Develop a probabilistic ontology capable of reasoning with masses of evidence from different domains in order to provide situation awareness on maritime domain.
Part of PROGNOS
PRobabilistic OntoloGies for Net-centric Operation Systems
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Objective
4
Develop a probabilistic ontology capable of reasoning with masses of evidence from different domains in order to provide situation awareness on maritime domain.
Part of PROGNOS
PRobabilistic OntoloGies for Net-centric Operation Systems
Use
PR-OWL
MEBN
High-Level Fusion
UnBBayes
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Methodology
5Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
UMP-SW
6Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
POMC
7
Requirements
Analysis & Design
Implementation
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Modeling
8Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Is the ship using an unusual route?
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Is the ship using an unusual route?
Verify if there is a direct report that the ship is using an unusual route;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Is the ship using an unusual route?
Verify if there is a direct report that the ship is using an unusual route;
Verify if there is a report that the ship is meeting some other ship for no apparent reason.
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Is the ship using an unusual route?
Verify if there is a direct report that the ship is using an unusual route;
Verify if there is a report that the ship is meeting some other ship for no apparent reason.
Does the ship seem to exhibit evasive behavior?
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Is the ship using an unusual route?
Verify if there is a direct report that the ship is using an unusual route;
Verify if there is a report that the ship is meeting some other ship for no apparent reason.
Does the ship seem to exhibit evasive behavior?
Verify if an electronic countermeasure (ECM) was identified by a navy ship;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Requirements
9
In our domain we have the following set of goal/queries/evidence:
Does the ship have a terrorist crewmember?
Verify if a crewmember is related to any terrorist;
Verify if a crewmember is associated with any terrorist organization.
Is the ship using an unusual route?
Verify if there is a direct report that the ship is using an unusual route;
Verify if there is a report that the ship is meeting some other ship for no apparent reason.
Does the ship seem to exhibit evasive behavior?
Verify if an electronic countermeasure (ECM) was identified by a navy ship;
Verify if the ship has a responsive radar and automatic identification system (AIS).
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design I
10Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
A ship is of interest if and only if it has a terrorist crewmember;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
A ship is of interest if and only if it has a terrorist crewmember;
If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
A ship is of interest if and only if it has a terrorist crewmember;
If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist;
If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
A ship is of interest if and only if it has a terrorist crewmember;
If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist;
If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist;
If an organization has a terrorist member, it is more likely that it is a terrorist organization;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
A ship is of interest if and only if it has a terrorist crewmember;
If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist;
If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist;
If an organization has a terrorist member, it is more likely that it is a terrorist organization;
A ship of interest is more likely to have an unusual route;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design II
11
The probabilistic rules for our model include:
A ship is of interest if and only if it has a terrorist crewmember;
If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist;
If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist;
If an organization has a terrorist member, it is more likely that it is a terrorist organization;
A ship of interest is more likely to have an unusual route;
A ship of interest is more likely to meet other ships for trading illicit cargo;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;
A ship of interest is more likely to have an evasive behavior;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;
A ship of interest is more likely to have an evasive behavior;
A ship with evasive behavior is more likely to have non responsive electronic equipment;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;
A ship of interest is more likely to have an evasive behavior;
A ship with evasive behavior is more likely to have non responsive electronic equipment;
A ship with evasive behavior is more likely to deploy an ECM;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;
A ship of interest is more likely to have an evasive behavior;
A ship with evasive behavior is more likely to have non responsive electronic equipment;
A ship with evasive behavior is more likely to deploy an ECM;
A ship might have non responsive electronic equipment due to working problems;
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Analysis & Design III
12
The probabilistic rules for our model include:
A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;
A ship of interest is more likely to have an evasive behavior;
A ship with evasive behavior is more likely to have non responsive electronic equipment;
A ship with evasive behavior is more likely to deploy an ECM;
A ship might have non responsive electronic equipment due to working problems;
A ship that is within radar range of a ship that deployed an ECM might be able to detect the ECM, but not who deployed it.
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Implementation I
13Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Implementation II
14Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Implementation III
15Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Implementation IV
16Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Reasoning
17Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
SSBN Construction
18Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Scalability I
19
*Linear time compared to number of nodes, but...
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
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Thursday, July 15, 2010
Scalability II
20
*...exponential number of nodes compared to KB size
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Scalability III
21
*SSMSBN to explore local computation
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Scalability IV
22
*Approximation algorithms to improve computation speed
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Testing
23Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Simulate Ground Truth
24Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Create Agents
25Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Sample Reports
26
Ground Truth
*Some GT information will never be sampled - e.g. ship of interest**Different KBs might have different types of information - e.g. social network vs ship location
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Sample Reports
26
Ground Truth
CIA
*Some GT information will never be sampled - e.g. ship of interest**Different KBs might have different types of information - e.g. social network vs ship location
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Sample Reports
26
Ground Truth
CIA
FBI
*Some GT information will never be sampled - e.g. ship of interest**Different KBs might have different types of information - e.g. social network vs ship location
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Sample Reports
26
Ground Truth
CIA
FBI Navy
*Some GT information will never be sampled - e.g. ship of interest**Different KBs might have different types of information - e.g. social network vs ship location
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Sample Reports
26
Ground Truth
CIA
FBI Navy
...
*Some GT information will never be sampled - e.g. ship of interest**Different KBs might have different types of information - e.g. social network vs ship location
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Connect the Dots
27Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Compare to Ground Truth
28Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Compare to Ground Truth
28Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Compare to Ground Truth
28
Ground Truth
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Compare to Ground Truth
28
Ground Truth
=?
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
PCC Evaluation
29
!
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Conclusion
30Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Conclusion
31Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Conclusion
31
Showed how use the UMP-SW to create a PO for MDA
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Conclusion
31
Showed how use the UMP-SW to create a PO for MDA
Implemented different solutions to scalability problems
SSMSBN
Approximation algorithms
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Conclusion
31
Showed how use the UMP-SW to create a PO for MDA
Implemented different solutions to scalability problems
SSMSBN
Approximation algorithms
Implemented a solid framework for testing the models
Simulation
Comparing results to ground truth
PCC Evaluation
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Future Work
32Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Future Work
32
Improve the PO for MDA
Include new rationales based on statistical data
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Future Work
32
Improve the PO for MDA
Include new rationales based on statistical data
Improve scalability
Implement hypothesis management
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Future Work
32
Improve the PO for MDA
Include new rationales based on statistical data
Improve scalability
Implement hypothesis management
Improve communication
Gather information from different sources using OWL-S
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Future Work
32
Improve the PO for MDA
Include new rationales based on statistical data
Improve scalability
Implement hypothesis management
Improve communication
Gather information from different sources using OWL-S
Generate statistical results from different simulations
Compare the results to the ground truth
Compute PCC
Objective - Methodology - Modeling - Reasoning - Testing - Conclusion
Thursday, July 15, 2010
Obrigado!
33
Thursday, July 15, 2010