UnBBayes is a probabilistic network framework written in Java. It has both a GUI and an API with inference, sampling, learning and evaluation. It supports BN, ID, MSBN, OOBN, HBN, MEBN/PR-OWL, structure, parameter and incremental learning. The overview is presented through a slides potpourri from different presentations the Artificial Intelligence Group (GIA) from University of Brasilia (UnB) has given since 1999. It covers BN, ID, MSBN, UnBBayes Server, and MEBN. This presentation was given by Rommel Carvalho when he started his PhD at George Mason University on the Friday seminar called Krypton (http://krypton.c4i.gmu.edu/).

1. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes Overview
Slides Potpourri
Rommel Novaes Carvalho
and GIA (Artificial Intelligence Group) from UnB
GMU - September 19th 2008

2. ©2008 Rommel Novaes Carvalho – University of Brasília
Team & Background
Advisor:
Dr. Marcelo Ladeira - UnB
Co-advisor:
Dr. Paulo C. G. Costa - GMU
Bachelor Degree in CS:
Laecio L. Santos - UnB
Shou Matsumoto - UnB
Consultant:
Dr. Kathryn B. Laskey - GMU
Papers
IADIS
FLAIRS
ISDA - IEEE
Selected to extend it as a book chapter
2

3. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
3

4. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
3

5. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
3

6. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
BN Learning
K2, B, CBL-A and CBL-B
Incremental
3

7. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
BN Learning
K2, B, CBL-A and CBL-B
Incremental
Metaphor
3

8. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
BN Learning
K2, B, CBL-A and CBL-B
Incremental
Metaphor
XMLBIF 0.4 (never published, though)
3

9. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
BN Learning
K2, B, CBL-A and CBL-B
Incremental
Metaphor
XMLBIF 0.4 (never published, though)
UnBBayes Server (J2EE)
3

10. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
BN Learning
K2, B, CBL-A and CBL-B
Incremental
Metaphor
XMLBIF 0.4 (never published, though)
UnBBayes Server (J2EE)
MEBN/PR-OWL
3

11. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes History
BN in Delphi ... Migrated to Java
BN, ID, and MSBN
BN Learning
K2, B, CBL-A and CBL-B
Incremental
Metaphor
XMLBIF 0.4 (never published, though)
UnBBayes Server (J2EE)
MEBN/PR-OWL
Other things
Gibbs for missing values
Monte Carlo
Etc (not even I know it all!)
UnBMiner (maybe some other day...)
People involved > 15
3

12. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes before MEBN
BN, ID, MSBN, and UnBBayes Server

13. ©2008 Rommel Novaes Carvalho – University of Brasília
Agenda I
BN
ID
MSBN
UnBBayes Server
5

14. ©2008 Rommel Novaes Carvalho – University of Brasília
BN
Asia
BN - ID - MSBN - UnBBayes Server 6

15. ©2008 Rommel Novaes Carvalho – University of Brasília
BN - Compile
Asia
BN - ID - MSBN - UnBBayes Server 7

16. ©2008 Rommel Novaes Carvalho – University of Brasília
BN - Update Beliefs
Asia
BN - ID - MSBN - UnBBayes Server 8

17. ©2008 Rommel Novaes Carvalho – University of Brasília
ID
Car Buyer
BN - ID - MSBN - UnBBayes Server 9

18. ©2008 Rommel Novaes Carvalho – University of Brasília
ID - Compile
Car Buyer
BN - ID - MSBN - UnBBayes Server 10

19. ©2008 Rommel Novaes Carvalho – University of Brasília
ID - Update Beliefs
Car Buyer
BN - ID - MSBN - UnBBayes Server 11

20. ©2008 Rommel Novaes Carvalho – University of Brasília
MSBN
Extended Asia
BN - ID - MSBN - UnBBayes Server 12

21. ©2008 Rommel Novaes Carvalho – University of Brasília
MSBN - Compile
Extended Asia
BN - ID - MSBN - UnBBayes Server 13

22. ©2008 Rommel Novaes Carvalho – University of Brasília
MSBN - Update Beliefs
Extended Asia
BN - ID - MSBN - UnBBayes Server 14

23. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes Server
CRUD for BN models and evidence history + online reasoner
BN - ID - MSBN - UnBBayes Server 15

24. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes after MEBN

25. ©2008 Rommel Novaes Carvalho – University of Brasília
Agenda II
Motivation
Methodology
Understanding MEBN
Building a MTheory - Star Trek
SSBN in UnBBayes
Conclusions
17

26. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Exploitation of data from disparate sources
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 18

27. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Exploitation of data from disparate sources
Making semantic information explicit and computationally accessible
[Laskey et al, 2007]
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 18

28. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Exploitation of data from disparate sources
Making semantic information explicit and computationally accessible
[Laskey et al, 2007]
Semantic Web - SW emerges as a motivation for defining:
common formats to integrate and combine data
formalisms for recording how data relates to real world objects
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 18

29. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Exploitation of data from disparate sources
Making semantic information explicit and computationally accessible
[Laskey et al, 2007]
Semantic Web - SW emerges as a motivation for defining:
common formats to integrate and combine data
formalisms for recording how data relates to real world objects
Ontologies have precisely defined concepts to represent a certain domain
Key for semantic interoperability [Costa et al, 2006]
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 18

30. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Exploitation of data from disparate sources
Making semantic information explicit and computationally accessible
[Laskey et al, 2007]
Semantic Web - SW emerges as a motivation for defining:
common formats to integrate and combine data
formalisms for recording how data relates to real world objects
Ontologies have precisely defined concepts to represent a certain domain
Key for semantic interoperability [Costa et al, 2006]
“Washington” - syntatic <> SW <> uncertainty in SW
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 18

31. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Exploitation of data from disparate sources
Making semantic information explicit and computationally accessible
[Laskey et al, 2007]
Semantic Web - SW emerges as a motivation for defining:
common formats to integrate and combine data
formalisms for recording how data relates to real world objects
Ontologies have precisely defined concepts to represent a certain domain
Key for semantic interoperability [Costa et al, 2006]
“Washington” - syntatic <> SW <> uncertainty in SW
URW3-XG
WWW uncertainty reasoning use cases and standard approach
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 18

32. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Uncertainty in the SW
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 19

33. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Uncertainty in the SW
BN [Pearl, 1988] is one of the most promising approaches for dealing
with uncertainty in the SW
Probability: well fundamented principles and known semantics
Limitations in the SW
The number of variables has to be known in advance
Lack of support to recursive definition
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 19

34. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Uncertainty in the SW
BN [Pearl, 1988] is one of the most promising approaches for dealing
with uncertainty in the SW
Probability: well fundamented principles and known semantics
Limitations in the SW
The number of variables has to be known in advance
Lack of support to recursive definition
Use of the FOL expressiveness to overcome these limitations
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 19

35. ©2008 Rommel Novaes Carvalho – University of Brasília
Motivation
Uncertainty in the SW
BN [Pearl, 1988] is one of the most promising approaches for dealing
with uncertainty in the SW
Probability: well fundamented principles and known semantics
Limitations in the SW
The number of variables has to be known in advance
Lack of support to recursive definition
Use of the FOL expressiveness to overcome these limitations
Costa (2005) proposed a First-Order Bayesian framework to probabilistic
ontologies based in PR-OWL and MEBN
There was no implementation of PR-OWL and MEBN
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 19

36. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

37. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Discuss formalisms and contributions
PR-OWL: Dr. Paulo Cesar G. Costa
MEBN: Dr. Kathryn B. Laskey
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

38. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Discuss formalisms and contributions
PR-OWL: Dr. Paulo Cesar G. Costa
MEBN: Dr. Kathryn B. Laskey
URW3-XG
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

39. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Discuss formalisms and contributions
PR-OWL: Dr. Paulo Cesar G. Costa
MEBN: Dr. Kathryn B. Laskey
URW3-XG
Extension for UnBBayes
Development in Java with GNU GPL v3 and internationalization
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

40. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Discuss formalisms and contributions
PR-OWL: Dr. Paulo Cesar G. Costa
MEBN: Dr. Kathryn B. Laskey
URW3-XG
Extension for UnBBayes
Development in Java with GNU GPL v3 and internationalization
Selection of free tools
FOL: PowerLoom
OWL: Protégé
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

41. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Discuss formalisms and contributions
PR-OWL: Dr. Paulo Cesar G. Costa
MEBN: Dr. Kathryn B. Laskey
URW3-XG
Extension for UnBBayes
Development in Java with GNU GPL v3 and internationalization
Selection of free tools
FOL: PowerLoom
OWL: Protégé
Friendly GUI
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

42. ©2008 Rommel Novaes Carvalho – University of Brasília
Methodology
Discuss formalisms and contributions
PR-OWL: Dr. Paulo Cesar G. Costa
MEBN: Dr. Kathryn B. Laskey
URW3-XG
Extension for UnBBayes
Development in Java with GNU GPL v3 and internationalization
Selection of free tools
FOL: PowerLoom
OWL: Protégé
Friendly GUI
Experimental evaluation through a toy use case
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20

43. ©2008 Rommel Novaes Carvalho – University of Brasília
Understanding MEBN
BN + FOL = MEBN
The knowledge is represented as a set of MEBN fragments (MFrags, for
short) organized as a MEBN theory (MTheories, for short)
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 21

44. ©2008 Rommel Novaes Carvalho – University of Brasília
Understanding MEBN
MTheory and MFrag
MEBN theories extend ordinary Bayesian networks to provide an inner structure
for RVs that take arguments that refer to entities in the domain of application. A
MEBN theory implicitly expresses a JPD over truth-values of sets of FOL
sentences.
Context
Nodes
Input
Nodes
Resident
Nodes
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 22

45. ©2008 Rommel Novaes Carvalho – University of Brasília
Understanding MEBN
MEBN + OWL = PR-OWL
PR-OWL was proposed by Costa (2005) as an extension to the OWL
language, based in MEBN, that enables a probabilistic distribution over
over models of any FOL axiomatized theory.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 23

46. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes Architecture
Version Layers
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 24

47. ©2008 Rommel Novaes Carvalho – University of Brasília
UnBBayes Architecture
Extension Points
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 25

48. ©2008 Rommel Novaes Carvalho – University of Brasília
MEBN before UnBBayes
Protégé GUI
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 26

49. ©2008 Rommel Novaes Carvalho – University of Brasília
MEBN after UnBBayes
UnBBayes GUI
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 27

50. ©2008 Rommel Novaes Carvalho – University of Brasília
Building a MTheory
Star Trek Model
Starship
Zone
TimeStep [ord]
SensorReport
Sensor
CloakMode
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 28

51. ©2008 Rommel Novaes Carvalho – University of Brasília
Building a MTheory
Resident node
L
OW
P R-
L
OW
PR-
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 29

52. ©2008 Rommel Novaes Carvalho – University of Brasília
Building a MTheory
Input node
E BN
M
L
OW
BN P R-
M E
E BN
M
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 30

53. ©2008 Rommel Novaes Carvalho – University of Brasília
Building a MTheory
Context node
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 31

54. ©2008 Rommel Novaes Carvalho – University of Brasília
Building a MTheory
Proposed grammar for dynamic CPT
E BN
M
ithf
w i
w ted
noes
n
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 32

55. ©2008 Rommel Novaes Carvalho – University of Brasília
Building a MTheory
Saving ubf and pr-owl files
UnBBayes
Save
UBF
PR-OWL
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 33

56. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Inserting entities in the KB
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 34

57. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Inserting evidences in the KB
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 35

58. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

59. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY-
LIST), search for evidence in the KB. If
found it, return it. If not, continue.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

60. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY-
LIST), search for evidence in the KB. If
found it, return it. If not, continue.
ii. Search for the resident node that has the
name NODE and get its MFrag. Once
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

61. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY-
LIST), search for evidence in the KB. If
found it, return it. If not, continue.
ii. Search for the resident node that has the
name NODE and get its MFrag. Once
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST.
iii. Verify in the KB which context node refers
to the OVs in OV-LIST, replacing its values
by ENTITY-LIST. If any of them is false,
mark the MFrag to use the default
distribution.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

62. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY- iv. If the context node in (iii) does not have a
LIST), search for evidence in the KB. If solution, create it as father of NODE.
found it, return it. If not, continue.
ii. Search for the resident node that has the
name NODE and get its MFrag. Once
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST.
iii. Verify in the KB which context node refers
to the OVs in OV-LIST, replacing its values
by ENTITY-LIST. If any of them is false,
mark the MFrag to use the default
distribution.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

63. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY- iv. If the context node in (iii) does not have a
LIST), search for evidence in the KB. If solution, create it as father of NODE.
found it, return it. If not, continue.
v. For each father of NODE, go to step (i),
ii. Search for the resident node that has the replacing the OVs by the known entities
name NODE and get its MFrag. Once (contained in the query or KB).
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST.
iii. Verify in the KB which context node refers
to the OVs in OV-LIST, replacing its values
by ENTITY-LIST. If any of them is false,
mark the MFrag to use the default
distribution.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

64. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY- iv. If the context node in (iii) does not have a
LIST), search for evidence in the KB. If solution, create it as father of NODE.
found it, return it. If not, continue.
v. For each father of NODE, go to step (i),
ii. Search for the resident node that has the replacing the OVs by the known entities
name NODE and get its MFrag. Once (contained in the query or KB).
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST. vi. Create the NODE’s CPT.
iii. Verify in the KB which context node refers
to the OVs in OV-LIST, replacing its values
by ENTITY-LIST. If any of them is false,
mark the MFrag to use the default
distribution.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

65. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY- iv. If the context node in (iii) does not have a
LIST), search for evidence in the KB. If solution, create it as father of NODE.
found it, return it. If not, continue.
v. For each father of NODE, go to step (i),
ii. Search for the resident node that has the replacing the OVs by the known entities
name NODE and get its MFrag. Once (contained in the query or KB).
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST. vi. Create the NODE’s CPT.
iii. Verify in the KB which context node refers vii. Finish.
to the OVs in OV-LIST, replacing its values
by ENTITY-LIST. If any of them is false,
mark the MFrag to use the default
distribution.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

66. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Proposed algorithm to create a SSBN
i. For a given entry in the form NODE(ENTITY- iv. If the context node in (iii) does not have a
LIST), search for evidence in the KB. If solution, create it as father of NODE.
found it, return it. If not, continue.
v. For each father of NODE, go to step (i),
ii. Search for the resident node that has the replacing the OVs by the known entities
name NODE and get its MFrag. Once (contained in the query or KB).
NODE(OV-LIST) is found, verify if the type
in ENTITY-LIST is the same as OV-LIST. vi. Create the NODE’s CPT.
iii. Verify in the KB which context node refers vii. Finish.
to the OVs in OV-LIST, replacing its values
by ENTITY-LIST. If any of them is false, BN
mark the MFrag to use the default ME
distribution.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 36

67. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Query
HarmPotential(!ST4, !T3) = ?
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 37

68. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK
OK OK
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 38

69. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 39

70. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 40

71. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
GE OK O
AN
2R OK
S ER
P HA ]
)= [F
,! T0
T4 PT
(!S a teC
FO er
D
g en
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 41

72. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
OK
C PT
ate
er
g en
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 42

73. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
OK
C PT
ate
er
g en
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 43

74. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 44

75. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 45

76. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
]
[F
Z2
z =!
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 46

77. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 47

78. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK
C PT
r ate
e ne
g
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 48

79. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK
C PT
r ate
e ne
g
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 49

80. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
]
[F
Z2
z =!
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 50

81. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 51

82. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK
C PT
ate
er
g en
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 52

83. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
C PT
r ate
e ne
g
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 53

84. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
C PT
ate
er
g en
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 54

85. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
PT OK
a teC
n er
ge
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 55

86. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Bottom-up algorithm (goal driven)‫‏‬
OK K
OK O
OK
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 56

87. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
BN inference
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 57

88. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
BN inference
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 58

89. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
BN inference
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 59

90. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Explosive* (goal driven with evidence below)‫‏‬
SSBN topology
1 3 Does NODE has
children RNs in the
same MFrag? If
1 Evidence for yes, call 1 for each
NODE in KB? If CHILD.
yes, finish.
4 Get related INs to
2 Get related NODE. If INs have
context nodes children, call 1 for
and evaluate each CHILD.
them.
OK
OK
OK
OK
5 If NODE is
permanent,
evaluate NODE’s
parents.
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 60

91. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Explosive* (goal driven with evidence below)‫‏‬
Permanent Nodes
1
1 The query node is 1 4
always permanent.
2 Parents from a 2 3 5
permanent node
are also permanent
(except for
evidence nodes).
3 All findings are
permanent nodes.
1 6 4
Parents are only evaluated
if node is permanent.
2 3 5
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 61

92. ©2008 Rommel Novaes Carvalho – University of Brasília
SSBN in UnBBayes
Explosive* (goal driven with evidence below)‫‏‬
General algorithm
1
Finding No parent
1 Generate SSBN topology.
2 Create CPT for permanent nodes.
3 Remove temporary nodes.
4 Compile the network.
5 Set evidences and update beliefs.
Finding below
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 62

93. ©2008 Rommel Novaes Carvalho – University of Brasília
General Metaphor
Simple use of BN for end users
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 63

94. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 64

95. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
1. Identify and define the scope of the problem to be solved;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 64

96. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
1. Identify and define the scope of the problem to be solved;
2. Identify the entities present in the domain. If you are having trouble
trying to come up with different entities, you probably do not need to use
MEBN. Try using just BN, instead;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 64

97. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
1. Identify and define the scope of the problem to be solved;
2. Identify the entities present in the domain. If you are having trouble
trying to come up with different entities, you probably do not need to use
MEBN. Try using just BN, instead;
3. Identify logical groups (there are groups of information that are or
could be logically put together) for identifying sets of entities;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 64

98. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
1. Identify and define the scope of the problem to be solved;
2. Identify the entities present in the domain. If you are having trouble
trying to come up with different entities, you probably do not need to use
MEBN. Try using just BN, instead;
3. Identify logical groups (there are groups of information that are or
could be logically put together) for identifying sets of entities;
4. Identify criterias that can classify in some way the identified entities.
This will help you choose which entities are relevant and which are not to
solve the problem (discard them from your model). This step can also help to
detect uncertain about the existence of information and to identify contexts
where certain informations can be considered valid;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 64

99. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
1. Identify and define the scope of the problem to be solved;
2. Identify the entities present in the domain. If you are having trouble
trying to come up with different entities, you probably do not need to use
MEBN. Try using just BN, instead;
3. Identify logical groups (there are groups of information that are or
could be logically put together) for identifying sets of entities;
4. Identify criterias that can classify in some way the identified entities.
This will help you choose which entities are relevant and which are not to
solve the problem (discard them from your model). This step can also help to
detect uncertain about the existence of information and to identify contexts
where certain informations can be considered valid;
5. Identify the attributes the entities can have;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 64

100. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

101. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
6. If any attribute identified in the previous step is continuous, decide how
the discretization must be done. In the case where the values are discrete, but
there are too many values, try to group them some how;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

102. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
6. If any attribute identified in the previous step is continuous, decide how
the discretization must be done. In the case where the values are discrete, but
there are too many values, try to group them some how;
7. Identify rules related to entities present in the same group and rules
related to entities in different groups;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

103. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
6. If any attribute identified in the previous step is continuous, decide how
the discretization must be done. In the case where the values are discrete, but
there are too many values, try to group them some how;
7. Identify rules related to entities present in the same group and rules
related to entities in different groups;
8. Evaluate if MEBN is necessary and/or sufficient for modeling the
problem;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

104. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
6. If any attribute identified in the previous step is continuous, decide how
the discretization must be done. In the case where the values are discrete, but
there are too many values, try to group them some how;
7. Identify rules related to entities present in the same group and rules
related to entities in different groups;
8. Evaluate if MEBN is necessary and/or sufficient for modeling the
problem;
9. Evaluate if the identified entities are enough for your MEBN model;
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

105. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
6. If any attribute identified in the previous step is continuous, decide how
the discretization must be done. In the case where the values are discrete, but
there are too many values, try to group them some how;
7. Identify rules related to entities present in the same group and rules
related to entities in different groups;
8. Evaluate if MEBN is necessary and/or sufficient for modeling the
problem;
9. Evaluate if the identified entities are enough for your MEBN model;
10. Map the entities, groups, rules, and relations identified to their
respective MEBN element (MFrag, Node, etc);
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

106. ©2008 Rommel Novaes Carvalho – University of Brasília
Building MEBN Models
A simple guideline
6. If any attribute identified in the previous step is continuous, decide how
the discretization must be done. In the case where the values are discrete, but
there are too many values, try to group them some how;
7. Identify rules related to entities present in the same group and rules
related to entities in different groups;
8. Evaluate if MEBN is necessary and/or sufficient for modeling the
problem;
9. Evaluate if the identified entities are enough for your MEBN model;
10. Map the entities, groups, rules, and relations identified to their
respective MEBN element (MFrag, Node, etc);
11. Design the model in UnBBayes. Here, you might need to change your
model a little bit, because UnBBayes has some singularities due to
implementation (the way it implements recursion, possible states for a
resident node, etc).
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 65

107. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 66

108. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Results
Contributions to PR-OWL/MEBN
Tool for representing and reasoning in probabilistic ontologies
Complete use case example
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 66

109. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Results
Contributions to PR-OWL/MEBN
Tool for representing and reasoning in probabilistic ontologies
Complete use case example
Scientific contributions
PR-OWL
Entity as a possible state for a node
Global exclusivity
Built-in recursion
MEBN
New algorithm for creating SSBN
Grammar for creating dynamic CPT
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 66

110. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 67

111. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Technological contributions
New algorithm for creating SSBN implemented
First implementation of PR-OWL/MEBN in the world
GNU GPL v3, open source and free
www.sourceforge.net/projects/unbbayes
Platform independent – Java
Internationalization
OWL compatible
Friendly GUI
Compiler for dynamic CPT
Format .ubf
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 67

112. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 68

113. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Limitations
Query with only one node
No recursion explicit stop condition
GUI
Node’s size is not proportional to its label
Edges do not get to the node’s boundary
No overall MTheory visualization
No MFrag reuse
Some elements present in Laskey 2007 were not implemented
Save finding in PR-OWL
Built-in recursion in PR-OWL
“Likely” and “FOL” findings
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 68

114. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 69

115. ©2008 Rommel Novaes Carvalho – University of Brasília
Conclusions
Future work
Real use case
Transparency and corruption prevention
CGU - Brazilian General Comptroller Office
ONR?
Implement limitations
Integrate OWL reasoner with MEBN reasoner => real PR-OWL reasoner
MCMC (Gibbs) for approximate inference
OOBN
...?
Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 69

116. ©2008 Rommel Novaes Carvalho – University of Brasília
Obrigado!
Slides Potpourri
Rommel Novaes Carvalho
and GIA (Artificial Intelligence Group) from UnB
GMU - September 19th 2008