Using Social Networks for Learning New Concepts in Multi-Agent Systems

1. Using Social Networks for Learning New Concepts in Multi-Agent Systems By: Shimaa El-Sherif Behrouz Far Armin Eberlein

2. Agenda • Distributed Knowledge Management (DKM) • Multi-Agent System (MAS) • Social Networks • Measuring Tie Strengths • System Architecture • Concept Learning System • KnowlegeBases • Results • Conclusion

3. Distributed Knowledge Management (DKM) • DKM can solve real world complex problems that can’t be solved by centralized systems • The challenges that DKM faces are: • Representation of knowledge (Ontologies) • Distribution of knowledge (Multi-Agent System MAS) • Sharing of distributed knowledge (overcome semantic hetrogeniety) • We get use of MAS and Social Networks sharing capabilities.

4. Multi-Agent System MAS • A MAS is a collection of heterogeneous agents. • Each agent has its own problem solving strategy. • They are able to interact with each other • Each MAS controls a repository uses different ontologies. • They try to understand each other.

5. Social Networks • We do not mean Facebook, Twitter or other social web services. • It is represented as a set of nodes have one or more kinds of relationships • Agents can understand the meaning of the same concept even if its definition is different in each agent’s ontology. • It improves the quality of ontology based concept learning and search.

6. Measuring tie strengths • The strengths of ties are affected by: • Closeness factor • Similarity between two ontologies. • Time-related factors • duration of relationship. • Frequency of communications • Time since last communication • … • Mutual confidence factor • One-sided or mutual relationship • Neighbourhood overlap • The number of common friends

7. System Architecture R2 R1 Rn R1 R2 Rn ... ... Controller Ontology Ontology Ontology Ontology Ontology Ontology Documents Documents Documents Documents Documents Documents Query Handler Concept Learner Document Annotator Peer Finder Concept Manager Tie Manager Agn MASn Ag1 MAS1 Ag2 MAS2 ... ... PA

8. Concept Learning system Learner Agent Teacher Agent Send learning request Receive learning request Collect all example sets Search for best matching concept Send a request for conflicting examples Select +ve and -ve examples Resolve conflicts Send examples back Learn new concept Vote for conflicting examples Update local repository Return vote back Update tie strengths

9. University College of Art & Science College of Engineering Electrical & Computer Engineering Computer Science Mathemactics English Mechanical Engineering Knowledge Base(Cornell University)

10. University College of Art & Design College of Engineering English Mathemactics Electrical Engineering & Computer Science Mechanical Engineering Knowledge Base (University of Michigan)

11. University College of Arts & Sciences College of Engineering Mathemactics Electrical Engineering English Mechanical Engineering Computer Science & Engineering Knowledge Base (University of Washington)

12. Results (Learning new concept NO SN) • Using K-NN for learning • Using Naive Bayes for learning • Using SVM for learning

13. Results (Applying Social Networks) • The closeness values between the learner agent AgL and teacher agents AgC, AgM, AgW • Number of positive and negative examples selected from each teacher agent

14. Results (Learning new concept with SN) • Using K-NN for learning • Using Naive Bayes for learning • Using SVM for learning

15. Results (Update Tie Strengths) • The updated tie strength between the learner agent AgL and teacher agents AgC, AgM, AgW

16. Conclusion • We introduce a new mechanism for learning new concepts from MAS with different ontologies. • It depends on sending +ve and –ve examples to identify the new concept. • The number of +ve and –ve examples depending on the strength of ties between learner agent and each teacher agent. • Using social networks improves the learning accuracy.

17. Thank you