Modeling a Probabilistic Ontology forMaritime Domain AwarenessRommel N. Carvalho, Richard Haberlin, Paulo C. G. Costa, Kat...
Agenda         2
AgendaObjective                     2
AgendaObjectiveIntroduction  Ontology  Probabilistic Ontology                             2
AgendaObjectiveIntroduction  Ontology  Probabilistic OntologyMethodology                             2
AgendaObjectiveIntroduction  Ontology  Probabilistic OntologyMethodologyModeling the PO for MDA                           ...
AgendaObjectiveIntroduction  Ontology  Probabilistic OntologyMethodologyModeling the PO for MDATesting                    ...
AgendaObjectiveIntroduction  Ontology  Probabilistic OntologyMethodologyModeling the PO for MDATestingConclusion          ...
ObjectiveObjective - Introduction - Methodology - Modeling - Testing - Conclusion   3
ObjectiveObjective - Introduction - Methodology - Modeling - Testing - Conclusion   4
ObjectiveDevelop a probabilistic ontology capable of reasoningwith masses of evidence from different domains in orderto pr...
ObjectiveDevelop a probabilistic ontology capable of reasoningwith masses of evidence from different domains in orderto pr...
ObjectiveDevelop a probabilistic ontology capable of reasoningwith masses of evidence from different domains in orderto pr...
IntroductionObjective - Introduction - Methodology - Modeling - Testing - Conclusion   5
OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application....
OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application....
OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application....
OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application....
OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application....
Ontology in OWLObjective - Introduction - Methodology - Modeling - Testing - Conclusion   7
Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge abo...
Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge abo...
Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge abo...
Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge abo...
Probabilistic Ontology in PR-OWL    Objective - Methodology - Modeling - Reasoning - Testing - Conclusion   9
What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion   10
What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion   10
What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion   10
What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion   10
What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion   10
What’s the Goal?                           ?Objective - Introduction - Methodology - Modeling - Testing - Conclusion   10
Uncertainty Modeling Process for Semantic Technologies         (UMP-ST)** In the paper I use UMP-SW                     Ob...
UMP-STObjective - Introduction - Methodology - Modeling - Testing - Conclusion   12
Modeling Cycle - MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion   13
Modeling Cycle - MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion   13
Modeling Cycle - MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion   13
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Modeling Cycle - MDA                                           Goal: Identify whether a ship is a ship of interest        ...
Incremental andIterative ModelingObjective - Introduction - Methodology - Modeling - Testing - Conclusion   14
RequirementsObjective - Introduction - Methodology - Modeling - Testing - Conclusion   15
RequirementsAssumptions change          Objective - Introduction - Methodology - Modeling - Testing - Conclusion   15
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member            Ob...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
RequirementsAssumptions change  Iteration 1: A ship is a ship of interest iff it has a terrorist crew member  Iteration 2:...
Analysis & Design - Iteration I    Objective - Methodology - Modeling - Reasoning - Testing - Conclusion   16
Analysis & Design - Iteration 2    Objective - Methodology - Modeling - Reasoning - Testing - Conclusion   17
Changes in Iteration 3 Objective - Introduction - Methodology - Modeling - Testing - Conclusion   18
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist            Objective - Introduction -...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query           ...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query    Is the ...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query    Is the ...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query    Is the ...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query    Is the ...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query    Is the ...
Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query    Is the ...
Implementation - Impact of Change   Objective - Introduction - Methodology - Modeling - Testing - Conclusion   19
Implementation - Impact of ChangeOnly one change in the previous model        Objective - Introduction - Methodology - Mod...
Implementation - Impact of ChangeOnly one change in the previous model        Objective - Introduction - Methodology - Mod...
Implementation - Impact of ChangeOnly one change in the previous model               X        Objective - Introduction - M...
Implementation - Impact of ChangeOnly one change in the previous model        Objective - Introduction - Methodology - Mod...
Testingfrom Evaluating Uncertainty Representation and Reasoning  in HLF Systems paper - also presented in Fusion 2011     ...
Manual EvaluationManual case-based evaluation 4 major categories were defined:   A possible bomb plan using fishing ship;   ...
Automatic EvaluationAutomatic case-based evaluation Used simulation tool to generate ground truth Generated reports based ...
ConclusionObjective - Introduction - Methodology - Modeling - Testing - Conclusion   23
ConclusionObjective - Introduction - Methodology - Modeling - Testing - Conclusion   24
ConclusionShowed how to use the UMP-ST to create/extend a PO for MDA        Objective - Introduction - Methodology - Model...
ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements ...
ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements ...
ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements ...
ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements ...
Obrigado!            25
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  • Modeling a Probabilistic Ontology for Maritime Domain Awareness

    1. 1. Modeling a Probabilistic Ontology forMaritime Domain AwarenessRommel N. Carvalho, Richard Haberlin, Paulo C. G. Costa, Kathryn B. Laskey, and KC Chang Center of Excellence in C4I / The Sensor Fusion Lab George Mason University Paper - 14th International Conference on Information Fusion Fusion 2011 - 07/07/2011
    2. 2. Agenda 2
    3. 3. AgendaObjective 2
    4. 4. AgendaObjectiveIntroduction Ontology Probabilistic Ontology 2
    5. 5. AgendaObjectiveIntroduction Ontology Probabilistic OntologyMethodology 2
    6. 6. AgendaObjectiveIntroduction Ontology Probabilistic OntologyMethodologyModeling the PO for MDA 2
    7. 7. AgendaObjectiveIntroduction Ontology Probabilistic OntologyMethodologyModeling the PO for MDATesting 2
    8. 8. AgendaObjectiveIntroduction Ontology Probabilistic OntologyMethodologyModeling the PO for MDATestingConclusion 2
    9. 9. ObjectiveObjective - Introduction - Methodology - Modeling - Testing - Conclusion 3
    10. 10. ObjectiveObjective - Introduction - Methodology - Modeling - Testing - Conclusion 4
    11. 11. ObjectiveDevelop a probabilistic ontology capable of reasoningwith masses of evidence from different domains in orderto provide situation awareness on maritime domain. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 4
    12. 12. ObjectiveDevelop a probabilistic ontology capable of reasoningwith masses of evidence from different domains in orderto provide situation awareness on maritime domain.Part of PROGNOS PRobabilistic OntoloGies for Net-centric Operation Systems Objective - Introduction - Methodology - Modeling - Testing - Conclusion 4
    13. 13. ObjectiveDevelop a probabilistic ontology capable of reasoningwith masses of evidence from different domains in orderto provide situation awareness on maritime domain.Part of PROGNOS PRobabilistic OntoloGies for Net-centric Operation SystemsUse Probabilistic Web Ontology Language (PR-OWL) Multi-Entity Bayesian Network (MEBN) High-Level Fusion UnBBayes Objective - Introduction - Methodology - Modeling - Testing - Conclusion 4
    14. 14. IntroductionObjective - Introduction - Methodology - Modeling - Testing - Conclusion 5
    15. 15. OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Properties of those entities; Relationships among entities; Processes and events that happen with those entities; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2].The Web Ontology Language (OWL) Developed by the World Wide Web Consortium (W3C) As a language to represent ontologies for the Semantic Web (SW) Accepted as a W3C recommendation in 2004 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 6
    16. 16. OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; Relationships among entities; Processes and events that happen with those entities; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2].The Web Ontology Language (OWL) Developed by the World Wide Web Consortium (W3C) As a language to represent ontologies for the Semantic Web (SW) Accepted as a W3C recommendation in 2004 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 6
    17. 17. OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; Processes and events that happen with those entities; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2].The Web Ontology Language (OWL) Developed by the World Wide Web Consortium (W3C) As a language to represent ontologies for the Semantic Web (SW) Accepted as a W3C recommendation in 2004 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 6
    18. 18. OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; motherOf, ownerOf, memberOf ... Processes and events that happen with those entities; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2].The Web Ontology Language (OWL) Developed by the World Wide Web Consortium (W3C) As a language to represent ontologies for the Semantic Web (SW) Accepted as a W3C recommendation in 2004 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 6
    19. 19. OntologyAn ontology is an explicit, formal knowledge representation thatexpresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; motherOf, ownerOf, memberOf ... transporting cargo, Processes and events that happen with those entities; fishing, ... where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2].The Web Ontology Language (OWL) Developed by the World Wide Web Consortium (W3C) As a language to represent ontologies for the Semantic Web (SW) Accepted as a W3C recommendation in 2004 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 6
    20. 20. Ontology in OWLObjective - Introduction - Methodology - Modeling - Testing - Conclusion 7
    21. 21. Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; motherOf, ownerOf, memberOf ... transporting cargo, Processes and events that happen with those entities; fishing, ... Statistical regularities that characterize the domain; Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 8
    22. 22. Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; motherOf, ownerOf, memberOf ... transporting cargo, Processes and events that happen with those entities; fishing, ... Statistical regularities that characterize the domain; Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 8
    23. 23. Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; motherOf, ownerOf, memberOf ... transporting cargo, Processes and events that happen with those entities; fishing, ... Statistical regularities that characterize the domain; Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; Uncertainty about all the above forms of knowledge; where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 8
    24. 24. Probabilistic OntologyA probabilistic ontology is an explicit, formal knowledge representationthat expresses knowledge about a domain of application. This includes: Types of entities that exist in the domain; Person, Organization, Ship, ... Properties of those entities; firstName, lastName, shipID, ... Relationships among entities; motherOf, ownerOf, memberOf ... transporting cargo, Processes and events that happen with those entities; fishing, ... Statistical regularities that characterize the domain; Inconclusive, ambiguous, incomplete, unreliable, and dissonant knowledge related to entities of the domain; P(hasEvasiveBehavior| hasResponsiveAIS = false, Uncertainty about all the above forms of knowledge; hasResponsiveRadio = false) = 50% where the term entity refers to any concept (real or fictitious, concrete or abstract) that can be described and reasoned about within the domain of application [2]. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 8
    25. 25. Probabilistic Ontology in PR-OWL Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 9
    26. 26. What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion 10
    27. 27. What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion 10
    28. 28. What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion 10
    29. 29. What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion 10
    30. 30. What’s the Goal?Objective - Introduction - Methodology - Modeling - Testing - Conclusion 10
    31. 31. What’s the Goal? ?Objective - Introduction - Methodology - Modeling - Testing - Conclusion 10
    32. 32. Uncertainty Modeling Process for Semantic Technologies (UMP-ST)** In the paper I use UMP-SW Objective - Introduction - Methodology - Modeling - Testing - Conclusion 11
    33. 33. UMP-STObjective - Introduction - Methodology - Modeling - Testing - Conclusion 12
    34. 34. Modeling Cycle - MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    35. 35. Modeling Cycle - MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    36. 36. Modeling Cycle - MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    37. 37. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    38. 38. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson hasCrewMember isRelatedTo Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    39. 39. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson hasCrewMember isRelatedTo If a crew member is related to a terrorist, then it is more likely that he is also a terrorist Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    40. 40. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson hasCrewMember isRelatedTo If a crew member is related to a terrorist, then it is more likely that he is also a terrorist Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    41. 41. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson hasCrewMember isRelatedTo If a crew member is related to a terrorist, then it is more likely that he is also a terrorist Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    42. 42. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson hasCrewMember isRelatedTo If a crew member is related to a terrorist, then it is more likely that he is also a terrorist Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    43. 43. Modeling Cycle - MDA Goal: Identify whether a ship is a ship of interest Query: Does the ship have a terrorist crew member? Evidence: Crew member related to any terrorist; Crew member associated with terrorist organization Ship Person Organization isTerroristPerson hasCrewMember isRelatedTo If a crew member is related to a terrorist, then it is more likely that he is also a terrorist Objective - Introduction - Methodology - Modeling - Testing - Conclusion 13
    44. 44. Incremental andIterative ModelingObjective - Introduction - Methodology - Modeling - Testing - Conclusion 14
    45. 45. RequirementsObjective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    46. 46. RequirementsAssumptions change Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    47. 47. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    48. 48. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member plan Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    49. 49. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member planRequirements change Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    50. 50. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member planRequirements change Iteration 1: Verify if an electronic countermeasure (ECM) was identified by a navy ship. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    51. 51. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member planRequirements change Iteration 1: Verify if an electronic countermeasure (ECM) was identified by a navy ship. Iteration 2: No need to include ECM scenario, since the ships analyzed (fishing and merchant) do not have such capability. Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    52. 52. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member planRequirements change Iteration 1: Verify if an electronic countermeasure (ECM) was identified by a navy ship. Iteration 2: No need to include ECM scenario, since the ships analyzed (fishing and merchant) do not have such capability.New requirements are included Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    53. 53. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member planRequirements change Iteration 1: Verify if an electronic countermeasure (ECM) was identified by a navy ship. Iteration 2: No need to include ECM scenario, since the ships analyzed (fishing and merchant) do not have such capability.New requirements are included Is the ship being used to exchange illicit cargo? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    54. 54. RequirementsAssumptions change Iteration 1: A ship is a ship of interest iff it has a terrorist crew member Iteration 2: A ship is a ship of interest iff it has is part of a terrorist crew member planRequirements change Iteration 1: Verify if an electronic countermeasure (ECM) was identified by a navy ship. Iteration 2: No need to include ECM scenario, since the ships analyzed (fishing and merchant) do not have such capability.New requirements are included Is the ship being used to exchange illicit cargo? Is the ship being used as a suicide ship to bomb a port? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 15
    55. 55. Analysis & Design - Iteration I Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 16
    56. 56. Analysis & Design - Iteration 2 Objective - Methodology - Modeling - Reasoning - Testing - Conclusion 17
    57. 57. Changes in Iteration 3 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    58. 58. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    59. 59. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    60. 60. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Is the crew member associated with any terrorist organization? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    61. 61. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Is the crew member associated with any terrorist organization? 4 new major requirements/queries Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    62. 62. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Is the crew member associated with any terrorist organization? 4 new major requirements/queries Has the crew member been negatively influenced in some way by his/her personal history? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    63. 63. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Is the crew member associated with any terrorist organization? 4 new major requirements/queries Has the crew member been negatively influenced in some way by his/her personal history? Has the crew member been using communications media frequently used by terrorists? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    64. 64. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Is the crew member associated with any terrorist organization? 4 new major requirements/queries Has the crew member been negatively influenced in some way by his/her personal history? Has the crew member been using communications media frequently used by terrorists? Is the crew member a potential terrorist recruit? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    65. 65. Changes in Iteration 3Goal: Identify whether a particular crew member is a terrorist Existing requirement/query Is the crew member associated with any terrorist organization? 4 new major requirements/queries Has the crew member been negatively influenced in some way by his/her personal history? Has the crew member been using communications media frequently used by terrorists? Is the crew member a potential terrorist recruit? Is the crew member associated with any of the four primary terrorist cliques introduced by Sageman who are operating in the Middle East, North Africa and Southeastern Asia? Objective - Introduction - Methodology - Modeling - Testing - Conclusion 18
    66. 66. Implementation - Impact of Change Objective - Introduction - Methodology - Modeling - Testing - Conclusion 19
    67. 67. Implementation - Impact of ChangeOnly one change in the previous model Objective - Introduction - Methodology - Modeling - Testing - Conclusion 19
    68. 68. Implementation - Impact of ChangeOnly one change in the previous model Objective - Introduction - Methodology - Modeling - Testing - Conclusion 19
    69. 69. Implementation - Impact of ChangeOnly one change in the previous model X Objective - Introduction - Methodology - Modeling - Testing - Conclusion 19
    70. 70. Implementation - Impact of ChangeOnly one change in the previous model Objective - Introduction - Methodology - Modeling - Testing - Conclusion 19
    71. 71. Testingfrom Evaluating Uncertainty Representation and Reasoning in HLF Systems paper - also presented in Fusion 2011 Objective - Introduction - Methodology - Modeling - Testing - Conclusion 20
    72. 72. Manual EvaluationManual case-based evaluation 4 major categories were defined: A possible bomb plan using fishing ship; A possible bomb plan using merchant ship; A possible exchange illicit cargo using fishing ship; A possible exchange illicit cargo using merchant ship. 5 variations for each scenario: “Sure” positive, “looks” positive, unsure, “looks” negative, and “sure” negative. All 20 different scenarios were analyzed by the SME and were evaluated as reasonable results (what was expected). Objective - Introduction - Methodology - Modeling - Testing - Conclusion 21
    73. 73. Automatic EvaluationAutomatic case-based evaluation Used simulation tool to generate ground truth Generated reports based on simulated data Inferred result and compared with ground truth Confusion matrix with threshold of 50%Inferred/ Inferred/ ≥ 50% < 50% ≥ 50% < 50% Real Real TRUE 24 3 TRUE 88.89% 11.11% FALSE 11 577 FALSE 1.87% 98.13% Objective - Introduction - Methodology - Modeling - Testing - Conclusion 22
    74. 74. ConclusionObjective - Introduction - Methodology - Modeling - Testing - Conclusion 23
    75. 75. ConclusionObjective - Introduction - Methodology - Modeling - Testing - Conclusion 24
    76. 76. ConclusionShowed how to use the UMP-ST to create/extend a PO for MDA Objective - Introduction - Methodology - Modeling - Testing - Conclusion 24
    77. 77. ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements arechanged and new ones are included Objective - Introduction - Methodology - Modeling - Testing - Conclusion 24
    78. 78. ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements arechanged and new ones are includedShowed the advantage of PR-OWL/MEBN modularity whenextending an existing model Objective - Introduction - Methodology - Modeling - Testing - Conclusion 24
    79. 79. ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements arechanged and new ones are includedShowed the advantage of PR-OWL/MEBN modularity whenextending an existing modelTested the model and showed that we get good results with bothmanual and automatic evaluation Objective - Introduction - Methodology - Modeling - Testing - Conclusion 24
    80. 80. ConclusionShowed how to use the UMP-ST to create/extend a PO for MDAShowed how the model evolves as existing requirements arechanged and new ones are includedShowed the advantage of PR-OWL/MEBN modularity whenextending an existing modelTested the model and showed that we get good results with bothmanual and automatic evaluationFuture work Scalability Learning Hypothesis Management Objective - Introduction - Methodology - Modeling - Testing - Conclusion 24
    81. 81. Obrigado! 25

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