UnBBayes Overview

3,489 views
3,434 views

Published on

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/).

Published in: Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
3,489
On SlideShare
0
From Embeds
0
Number of Embeds
1,269
Actions
Shares
0
Downloads
82
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide









































































































  • UnBBayes Overview

    1. 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. 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. 3. ©2008 Rommel Novaes Carvalho – University of Brasília UnBBayes History 3
    4. 4. ©2008 Rommel Novaes Carvalho – University of Brasília UnBBayes History BN in Delphi ... Migrated to Java 3
    5. 5. ©2008 Rommel Novaes Carvalho – University of Brasília UnBBayes History BN in Delphi ... Migrated to Java BN, ID, and MSBN 3
    6. 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. 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. 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. 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. 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. 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. 12. ©2008 Rommel Novaes Carvalho – University of Brasília UnBBayes before MEBN BN, ID, MSBN, and UnBBayes Server
    13. 13. ©2008 Rommel Novaes Carvalho – University of Brasília Agenda I BN ID MSBN UnBBayes Server 5
    14. 14. ©2008 Rommel Novaes Carvalho – University of Brasília BN Asia BN - ID - MSBN - UnBBayes Server 6
    15. 15. ©2008 Rommel Novaes Carvalho – University of Brasília BN - Compile Asia BN - ID - MSBN - UnBBayes Server 7
    16. 16. ©2008 Rommel Novaes Carvalho – University of Brasília BN - Update Beliefs Asia BN - ID - MSBN - UnBBayes Server 8
    17. 17. ©2008 Rommel Novaes Carvalho – University of Brasília ID Car Buyer BN - ID - MSBN - UnBBayes Server 9
    18. 18. ©2008 Rommel Novaes Carvalho – University of Brasília ID - Compile Car Buyer BN - ID - MSBN - UnBBayes Server 10
    19. 19. ©2008 Rommel Novaes Carvalho – University of Brasília ID - Update Beliefs Car Buyer BN - ID - MSBN - UnBBayes Server 11
    20. 20. ©2008 Rommel Novaes Carvalho – University of Brasília MSBN Extended Asia BN - ID - MSBN - UnBBayes Server 12
    21. 21. ©2008 Rommel Novaes Carvalho – University of Brasília MSBN - Compile Extended Asia BN - ID - MSBN - UnBBayes Server 13
    22. 22. ©2008 Rommel Novaes Carvalho – University of Brasília MSBN - Update Beliefs Extended Asia BN - ID - MSBN - UnBBayes Server 14
    23. 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. 24. ©2008 Rommel Novaes Carvalho – University of Brasília UnBBayes after MEBN
    25. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 36. ©2008 Rommel Novaes Carvalho – University of Brasília Methodology Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 20
    37. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 107. ©2008 Rommel Novaes Carvalho – University of Brasília Conclusions Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 66
    108. 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. 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. 110. ©2008 Rommel Novaes Carvalho – University of Brasília Conclusions Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 67
    111. 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. 112. ©2008 Rommel Novaes Carvalho – University of Brasília Conclusions Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 68
    113. 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. 114. ©2008 Rommel Novaes Carvalho – University of Brasília Conclusions Motivation - Methodology - Understanding MEBN - Building a MTheory - SSBN in UnBBayes - Conclusions 69
    115. 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. 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

    ×