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