1. Outline Soft computing in decision support LSP  The different components of LSP  Suitability vs. affordability What can we learn from decision support? Applications The Project is co-financed by the European Union from resources of the European Social Fund

2. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund J.J. Dujmović “Preference Logic for System Evaluation” IEEE Transactions on Fuzzy Systems 15 6 (2007) 1082-1099. Logic Scoring of Preferences

3. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences mulitiple criteria decision making cases ? user preferences (multiple criteria) select the best score

4. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences scoring: overall degree of suitability the overall degree of suitability E is a soft computing logical function of n attributes of which it is assumed that its range is normalized the value 0 denotes an unsuitable case and the value 1 (or 100%) denotes the maximum level of suitability

5. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences main steps of the LSP method 1.create the system attribute tree 2.define an elementary criterion for each attribute 3.for each competitive system, compute elementary degrees of suitability of elementary criteria 4.use logic aggregators developed to aggregate all elementary degrees of suitability and compute the overall suitability (of all user requirements) 5.if the overall degree of suitability E corresponds to the overall cost C, perform a cost/suitability analysis to find the best value

6. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 1: creation of the system attribute tree

7. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 2: definition of elementary criteria

8. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 2: definition of elementary criteria (cont’d) examples

9. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 3: evaluation of elementary criteria

10. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 3: evaluation of elementary criteria (cont’d) examples

11. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability construction of a hierarchic preference aggregation structure that reflects the semantics of the attribute tree

12. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) building blocks simple aggregators compound aggregators

13. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) simple aggregators Based on superposition of the fundamental Generalized Conjunction/Disjunction (GCD) function (basic LSP aggregator) Continuous transition from conjunction to disjunction Adjustable degrees of andness/orness (r) Adjustable relative importance of inputs (w i )

14. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) generalized conjunction/disjunction (GCD) GCD is implemented as a mean frequently used implementations –weighted power mean (WPM) –exponential mean –quasi-arithmetic mean –OWA WPM is used for practical purposes

15. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) weighted power mean (WPM)

16. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) discrete levels of andness/orness associate aggregators with linguistic interpretations the use of linguistic labels (weak, medium, strong, etc.) simplifies the process of selecting the most appropriate aggregator LSP basically uses a system with 17 discrete levels in practice: discrete levels of andness/orness

17. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences 17 discrete levels and their symbols GCD

18. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences GCD implemented as a weighted power mean mandatory (all inputs must be satisfied)

19. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) compound aggregators combining a mandatory with a desired input –conjunctive partial absorption combining a sufficient with a desired input –disjunctive partial absorption

20. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) conjunctive partial absorption

21. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) HPFdd C+D+ C+ A P(enalty); R(eward)

22. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d)

23. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) in practice

24. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) D+C+ D+ A P(enalty); R(eward)

25. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d)

26. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d) example

27. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 4: aggregation of elementary degrees of suitability (cont’d)

28. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 5: perform a cost/suitability analysis to find the best value suitability and affordability are orthogonal concepts both have a hierarchic structure but aggregation is different as values are different –suitability: logical aggregation (and, or, not, etc.) –cost: arithmetic aggregation (add, multiply, etc.) decision makers usually need a tradeoff between both

29. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 5: perform a cost/suitability analysis to find the best value

30. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 5: perform a cost/suitability analysis to find the best value overall suitability E vs. global cost C global quality Q best suitability-cost tradeoff

31. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 5: perform a cost/suitability analysis to find the best value

32. Soft computing in decision support LSP The Project is co-financed by the European Union from resources of the European Social Fund Logic Scoring of Preferences step 5: perform a cost/suitability analysis to find the best value

33. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund what can we learn from decision support?

34. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund Bipolarity in ‘fuzzy’ querying ‘and if possible’ is a special case of Conjunctive Partial Absorption J.J. Dujmović “Partial Absorption Function” Journal of the University of Belgrade 659 (1979) 156-163.

35. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund Bipolarity in ‘fuzzy’ querying ‘or else’ is a special case of Disjunctive Partial Absorption J.J. Dujmović “Partial Absorption Function” Journal of the University of Belgrade 659 (1979) 156-163.

36. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund there is a need for querying facilities to handle mandatory and optional criteria

37. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund current querying facilities do not efficiently support complex data searches

38. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity need for grouping and structuring preferences need for generalizing and specializing preferences criterion trees

39. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity tree structure leaf node: elementary criterion c i on a single attribute a i internal node: specification of an aggregation operator A edge: relative weight (importance) of the criterion A A c1c1 c1c1 c2c2 c2c2 ckck ckck … … w1w1 w1w1 w2w2 w2w2 wkwk wkwk

40. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity aggregators Generalised Conjunction Disjunction (GCD) Ordered Weighted Average (OWA) Yager Dujmović C D HPC HPD SPC SPD A

41. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity no more need for weight propagation! (because internal nodes have associated weights) weighted aggregators

42. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity evaluation leaf node: evaluation of elementary criterion c i internal node: aggregation of evaluation results of all leaf nodes criterion tree: evaluation of the root node

43. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity example …

44. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity advanced GCD aggregation for BSDs, if 0<|r|<+ , if r= - , if r= +  where r models the logical counterpart of the operator modelled by r (e.g., if r models HPC, then r models HPD)  

45. Soft computing in decision support What can we learn from decision support? The Project is co-financed by the European Union from resources of the European Social Fund query expressivity advanced OWA aggregation for BSDs

46. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund some applications

47. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund LSP suitability maps logically aggregated geographical suitability maps (S-maps) provide specialized maps of the suitability degree of a selected geographic region for a specific purpose – construction of industrial objects, airports, entertainment centers, shopping malls, sport facilities – land/sea exploitation – agriculture – etc. for the purpose of evaluating and comparing locations, areas or regions suitability degrees are computed using the LSP method

48. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund LSP suitability maps regular approach

49. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund LSP suitability maps bipolar approach

50. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund the TILES project Transnational and Integrated Long-term Marine Exploitation Strategies

51. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund the TILES project

52. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification Disaster Victim Identification identification of human bodies large scale disasters

53. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification collect as many data as possible about: – victims – missing persons data examples – biometrical (DNA, dental records, ear photographs...) – general data (gender, name...) – descriptive data (clothes, tattoo’s, piercings...) strategy

54. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification issues fast data collection intelligent combination of all information final decision by a committee of experts uncertainty in early stage charitable approach is preferred

55. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification data = ? victim: post mortem (PM) 3D ear picture missing person: ante mortem (AM) 2D pictures

56. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification challenge cope with poor picture quality of AM pictures

57. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach 1.Ear detection Positioning and extraction 2.Ear normalisation and enhancement Transform to a 3D ear model using geometrical and photometric corrections 3.Feature extraction 4.Ear recognition Compare feature sets and compute matching score 5.Decision

58. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach ……    AM ear normalized 3D ear ear detection ear normalisation and enhancement

59. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach ear normalisation and enhancement  PM ear normalized 3D ear 3D camera / 3D scanner

60. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach feature extraction selecting n representative points L A =[p 1 A,…,p n A ]

61. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach ear recognition  comparing feature sets om 3D AM and PM ear models L A =[p 1 A,…,p n A ] AM ear PM ear L P =[p 1 P,…,p n P ]

62. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach ear recognition  comparing feature sets om 3D AM and PM ear models match + coping with imperfect data

63. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification hesitation spheres

64. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification hesitation spheres handle unreliable parts manually assigned by forensic experts overall hesitation of point p covered by multiple hesitation spheres

65. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification traditional approach pApA 1. distance d(p A,p P ) 2. similarity f sim (p A,p P )

66. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification bipolar approach pApA local similarity f Bsim (p A,p P )

67. match Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification bipolar approach

68. Soft computing in decision support Applications The Project is co-financed by the European Union from resources of the European Social Fund ear identification approach interpretation of results  each comparison i : (s i,d i ) satisfaction about matching dissatisfaction about matching h i = 1-s i -d i : overall hesitation about matching ranking of the results: top-k matching results

69. Questions? Warsaw, June 22-26 2015 The Project is co-financed by the European Union from resources of the European Social Fund

70. Warsaw, June 22-26 2015 The Project is co-financed by the European Union from resources of the European Social Fund