fusion 2010 - prognos: predictive situational awareness with probabilistic ontologies

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


AgendaObjective

2


AgendaObjective

Methodology

2


AgendaObjective

Methodology

Modeling the PO for MDA

Requirements

Analysis & Design

Implementation

2


AgendaObjective

Methodology


Requirements

Analysis & Design

Implementation

Reasoning

2


AgendaObjective

Methodology


Requirements

Analysis & Design

Implementation

Reasoning

Testing the PO for MDA

2


AgendaObjective

Methodology


Requirements

Analysis & Design

Implementation

Reasoning

Testing the PO for MDA

Conclusion

2


Objective

3Objective - Methodology - Modeling - Reasoning - Testing - Conclusion


Objective



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


Objective

4


Part of PROGNOS

PRobabilistic OntoloGies for Net-centric Operation Systems



Objective

4


Part of PROGNOS

PRobabilistic OntoloGies for Net-centric Operation Systems

Use

PR-OWL

MEBN

High-Level Fusion

UnBBayes



Methodology



UMP-SW



POMC

7

Requirements

Analysis & Design

Implementation



Modeling



Requirements



Requirements

9

In our domain we have the following set of goal/queries/evidence:



Requirements

9


Does the ship have a terrorist crewmember?



Requirements

9



Verify if a crewmember is related to any terrorist;



Requirements

9




Verify if a crewmember is associated with any terrorist organization.



Requirements

9





Is the ship using an unusual route?



Requirements

9






Verify if there is a direct report that the ship is using an unusual route;



Requirements

9







Verify if there is a report that the ship is meeting some other ship for no apparent reason.



Requirements

9








Does the ship seem to exhibit evasive behavior?



Requirements

9









Verify if an electronic countermeasure (ECM) was identified by a navy ship;



Requirements

9









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).



Analysis & Design I



Analysis & Design II




11

The probabilistic rules for our model include:




11


A ship is of interest if and only if it has a terrorist crewmember;




11



If a crewmember is related to a terrorist, then it is more likely that he is also a terrorist;




11




If a crewmember is a member of a terrorist organization, then it is more likely that he is a terrorist;




11





If an organization has a terrorist member, it is more likely that it is a terrorist organization;




11






A ship of interest is more likely to have an unusual route;




11






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;



Analysis & Design III




12





12


A ship that meets other ships to trade illicit cargo is more likely to have an unusual route;




12



A ship of interest is more likely to have an evasive behavior;




12




A ship with evasive behavior is more likely to have non responsive electronic equipment;




12





A ship with evasive behavior is more likely to deploy an ECM;




12






A ship might have non responsive electronic equipment due to working problems;




12






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.



Implementation I



Implementation II



Implementation III



Implementation IV



Reasoning



SSBN Construction



Scalability I

19

*Linear time compared to number of nodes, but...


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Scalability II

20

*...exponential number of nodes compared to KB size



Scalability III

21

*SSMSBN to explore local computation



Scalability IV

22

*Approximation algorithms to improve computation speed



Testing



Simulate Ground Truth



Create Agents



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



Sample Reports

26

Ground Truth

CIA




Sample Reports

26

Ground Truth

CIA

FBI




Sample Reports

26

Ground Truth

CIA

FBI Navy




Sample Reports

26

Ground Truth

CIA

FBI Navy

...




Connect the Dots



Compare to Ground Truth




28

Ground Truth




28

Ground Truth

=?



PCC Evaluation

29

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Conclusion



Conclusion

31

Showed how use the UMP-SW to create a PO for MDA



Conclusion

31


Implemented different solutions to scalability problems

SSMSBN

Approximation algorithms



Conclusion

31


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



Future Work



Future Work

32

Improve the PO for MDA

Include new rationales based on statistical data



Future Work

32



Improve scalability

Implement hypothesis management



Future Work

32



Improve scalability


Improve communication

Gather information from different sources using OWL-S



Future Work

32



Improve scalability


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



Obrigado!

33


fusion 2010 - prognos: predictive situational awareness with probabilistic ontologies

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