Using social networks for learning new concepts in multi agent systems
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Using Social Networks for Learning New Concepts in Multi-Agent Systems. By: Shimaa El- Sherif Behrouz Far Armin Eberlein. Agenda. Distributed Knowledge Management (DKM) Multi-Agent System (MAS) Social Networks Measuring Tie Strengths System Architecture Concept Learning System

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Using social networks for learning new concepts in multi agent systems

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

By:

Shimaa El-Sherif

Behrouz Far

Armin Eberlein


Agenda
Agenda Multi-Agent Systems

  • Distributed Knowledge Management (DKM)

    • Multi-Agent System (MAS)

  • Social Networks

    • Measuring Tie Strengths

  • System Architecture

  • Concept Learning System

  • KnowlegeBases

  • Results

  • Conclusion


Distributed knowledge management dkm
Distributed Knowledge Management (DKM) Multi-Agent Systems

  • 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.


Multi agent system mas
Multi-Agent System MAS Multi-Agent Systems

  • 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.


Social networks
Social Networks Multi-Agent Systems

  • 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.


Measuring tie strengths
Measuring tie strengths Multi-Agent Systems

  • 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


System architecture
System Architecture Multi-Agent Systems

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


Concept learning system
Concept Learning system Multi-Agent Systems

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


Knowledge base cornell university

University Multi-Agent Systems

College of Art & Science

College of Engineering

Electrical & Computer Engineering

Computer Science

Mathemactics

English

Mechanical Engineering

Knowledge Base(Cornell University)


Knowledge base university of michigan

University Multi-Agent Systems

College of Art & Design

College of Engineering

English

Mathemactics

Electrical Engineering &

Computer Science

Mechanical Engineering

Knowledge Base (University of Michigan)


Knowledge base university of washington

University Multi-Agent Systems

College of Arts & Sciences

College of Engineering

Mathemactics

Electrical Engineering

English

Mechanical Engineering

Computer Science & Engineering

Knowledge Base (University of Washington)


Results learning new concept no sn
Results (Learning new concept NO SN) Multi-Agent Systems

  • Using K-NN for learning

  • Using Naive Bayes for learning

  • Using SVM for learning


Results applying social networks
Results (Applying Social Networks) Multi-Agent Systems

  • 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


Results learning new concept with sn
Results (Learning new concept with SN) Multi-Agent Systems

  • Using K-NN for learning

  • Using Naive Bayes for learning

  • Using SVM for learning


Results update tie strengths
Results (Update Tie Strengths) Multi-Agent Systems

  • The updated tie strength between the learner agent AgL and teacher agents AgC, AgM, AgW


Conclusion
Conclusion Multi-Agent Systems

  • 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.


Thank you
Thank you Multi-Agent Systems


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