Report on Multi-agent Data Fusion System: Design and implementation issues 1

1. Report on Multi-agent Data Fusion System: Design and implementation issues1 By Ganesh Godavari

2. Data Fusion • Data Fusion : task of data processing aiming at making decisions on the basis of distributed data sources specifying an object • Data sources • Different physical nature • Electromagnetic signals, sensor data… • Different accuracy • Reliability?

3. JDL views • Data and Information Fusion • Multi level process • Level0 • Fusion of sensor signals to produce semantically understandable data • Level1 • Make decisions with regard to classes of objects • Level2 • Asses a situation constituted by the set of aboce objects • Level3 • Impact assessment i.e. adversary intent assesment on the basis of situation development prediction • Level4 • Calculation a feedback like planning resource usage, sensor management etc • Level5 • Human activity and situation management

4. Applications of DF • Some applications of data fusion • Detection of intrusions into computer networks • Large data available through tools like tcpdump, IDS… • Analysis and prognosis of natural and man-made disaster development • Prediction and prevention of calamities like earthquakes, floods, weather conditions, nuclear explosions effect

5. Focus/strategy of the paper • Focus • Design and implementation of DF system at Level1 • Proposed strategy • Multilevel hierarchy of classifiers • Source based classifiers • Decision based on data of particular sources followed by meta-level decisions

6. Advantages of the strategy • Advantages • Decrease of the data sources information exchange • Simplicity of data source classifiers fusion even if they use different representation structures, certainty, accuracy etc.. • Use of mathematically sound mechanism for combining decisions of multiple classifiers

7. Problems inherent to DF applications • Cause of concern • Data sources are physically distributed • Spatially distributed, represented in different databases, located on different hosts • Hetrogeneous • Diversity of possible data structures, difference in data structures/data specification language

8. Problem list • Problem • development of the shared thesaurus providing for monosemantic understanding of the terminology • Entity identification problem • Data specifying an object is represented in different data sources • Non coherency of data measurement scales • data specifying in different sources the same entity attribute can be of different structures.

9. Technical terms • Decision • In DF tasks its classification of an entity (object, state of an object, situation etc) • Base-level/base classifiers • scheme of data fusion, each local data source is associated with a single or several classifiers

10. Classification of multi-level classifiers • Approaches for combining decisions of multilevel classifiers can be grouped into four groups: • Voting algorithms; • Probability-based or fuzzy algorithms; • Meta-learning algorithms based on stacked generalization idea; • Meta-learning algorithms based on classifiers' competence evaluation.

11. Meta classification scheme

12. Competence based approach to combine decisions of multiple classifiers

13. Meta model of training and testing data • Important peculiarities from learning viewpoint • Data are distributed in space and stored in different databases; • Each data source only partially specifies the same object to be classified in terms of attributes which can be different in different data sources; • Data can be incomplete; it can contain particular attribute values and also the total records in a source missed

14. Questions ?

15. References • Multi-agent Data Fusion Systems: Design and Implementation Issues by Vladimir Gorodetski, Oleg Karsayev and Vladimir .Samoilov