Dynamic Generation of Agent Communities from Distributed Production - - PowerPoint PPT Presentation

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Dynamic Generation of Agent Communities from Distributed Production and Content-Driven Delivery of Knowledge

AAAI Spring Symposium on Agent-Mediated Knowledge Management (AMKM-03)

Sinuhé Arroyo

Institut für Informatik IFI Next Generation Research Group University of Innsbruck, Austria

Juan Manuel Dodero

Computer Science Department Universidad Carlos III de Madrid

Richard Benjamins

Intelligent Software Components (iSOCO), S.A. Madrid, Spain

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• 1. Introduction
• 2. Multi-agent collaborative

production

• Features and structure
• Interaction within marts
• Consolidation protocol
• 3. Case study
• Course of the protocol
• Results
• 4. Dynamics of markets
• 5. Conclusions

Dynamic Generation of Agent Communities from Distributed Production and Content-Driven Delivery

• f Knowledge

Dodero, Arroyo. — AMKM 2003 –3–

Multi-agent system Case study Conclusions Intro Dynamic of markets

• 1. Introduction

Collaborative knowledge management

KM processes Distributed system Collaborative creation Task coordination needed

Creation or production

Different interaction policies:

compete, cooperate, negotiate

Structured interaction

Delivery

Content-driven Communities of interest

delivery production acquisition

Intro

Dodero, Arroyo. — AMKM 2003 –4–

Multi-agent system Case study Conclusions Intro Dynamic of markets

• 2. Multi-agent collaborative production

Producers’ collaboration (e.g. instructional designers)

Asynchrony

• Development, exchange and evaluation of proposals are

asynchronous.

• Different pace of creation

Different levels of knowledge (Domain-level knowledge) Decision privileges (e.g. lecturers vs. assistants) Conflicts

Multi-agent architecture motivation

Facilitates coordination when collaborating (e.g.,

compose a new educational resource)

Allows different interaction styles (e.g., compete,

cooperate, or negotiate)

Organizes interaction in distributed, but interconnected

domains of interaction

Multi-agent system

Dodero, Arroyo. — AMKM 2003 –5–

Multi-agent system Case study Conclusions Intro Dynamic of markets

System features

From a functional perspective…

Consolidation of knowledge that is

produced

From a structural perspective…

Multi-tiered structure Agents operate in tightly-coupled

hierarchical knowledge marts

Progressive consolidation of

knowledge

From a behavioural perspective…

Affiliation of agents into marts Evolution of marts

Multi-agent system

Interaction group S1 Interaction gr Interaction group M

Agent Proxy Agent Agent Agent Agent Agent Proxy Agent Agent Agent

Dodero, Arroyo. — AMKM 2003 –6–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Interaction within marts

Multi-agent system

Principles

Agent rationality modeled as preference relationships k1

> k2 or relevance functions u(k)

Relevant aspects modeled as RDF triples (object,

attribute, value):

• Submitter’s hierarchical level
• Fulfilment of goals
• Time-stamp

Message exchange

Message types

• proposal ( knowledge, interaction )
• consolidate ( knowledge, interaction )

Multicast, reliable transport facility

Dodero, Arroyo. — AMKM 2003 –7–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Consolidation protocol

Distribution Consolidation Idle Failure Success any message start (send proposal) receive any worse-evaluated receive consolidation better-evaluated receive consolidation better-evaluated t0 expires receive proposal better-evaluated receive any worse-evaluated t1 expires Distribution Consolidation

Multi-agent system

Dodero, Arroyo. — AMKM 2003 –8–

Multi-agent system Case study Conclusions Intro Dynamic of markets

• 3. Case study

Case study

Learning Object

Course titled “Introduction to XML”

Roles

3 instructional designers, represented by agents

A1..A3

A1 is a docent coordinator

Task

Development of the TOC A1 submits p, A2 submits q, A3 does nothing

Proposals

p = Proposed manifest file with 6 chapters q = Modified manifest file, divides up chapter 5 in two

Evaluation criteria

Fulfillment of objectives Actor’s rank

Dodero, Arroyo. — AMKM 2003 –9–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Course of the protocol

A1 A2 A3 Proposal p Proposal p A1 A2 A3 u(p) < u(q) Reply with Proposal q Proposal q u(p) < u(q) Start timeout t1 Start timeout t0 Start timeout t0 t0 expires OK A1 A2 A3 Consolidate q Consolidate q Start timeout t1 Termination: unsuccessful OK A1 A2 A3 Finish: successful t1 expires Initial exchange of proposals After receiving proposals Consolidation after t0 expiration After t1 expiration Proposal q

Case study

Dodero, Arroyo. — AMKM 2003 –10–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Results: quality (grade of fulfilment)

75 55 54 75 43 43 75 55 54 43 10 20 30 40 50 60 70 80 2 4 6 8 10 12 14 16 18 Proposals ordered by submission time Grade of fulfillment (%) Issued in two-mart scenario Issued in one-mart scenario

Case study

Dodero, Arroyo. — AMKM 2003 –11–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Results: consolidation lifetime

1622 2183 3976 7401 190288 13439 7291 183027 20000 40000 60000 80000 100000 120000 140000 160000 180000 200000 1 2 3 4 Consolidated proposals ordered by instant of consolidation Consolidation lifetime (time units)

Case study

Dodero, Arroyo. — AMKM 2003 –12–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Results: number of conflicts

0,00 10,00 20,00 30,00 40,00 50,00 60,00 70,00 I1 A1 A3 Agents

• No. of conflicts/time unit
• No. of conflicts (two-mart)
• No. of conflicts (one-mart)

Case study

Dodero, Arroyo. — AMKM 2003 –13–

Multi-agent system Case study Conclusions Intro Dynamic of markets

• 4. Dynamics of markets

Dynamics of collaborative groups

Agents affiliate to marts depending on the kind of

knowledge that they produce

Marts evolve (merge or divide) depending on the kind of

knowledge consolidated within them

Agents arrangement

Cognitive distance dk between agents and marts Defined from dissimilarity between issued proposals’

attributes

Agents operate in the nearest mart Agents relocate based on Knowledge production

Evolution of groups

Mart fusion/division MajorClust algorithm

Dynamic of markets

Dodero, Arroyo. — AMKM 2003 –14–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Dynamic of markets

Information brokering services

Content-driven delivery Filters to deliver contents of interest Publish/subscribe pattern

Communities of users

User agents subscribe to items of

interest

User agents produce (publish) items Brokers’ routing tables are built Routing tables contain (hide) users’

layout into communities of interest

Dynamic of markets

Dodero, Arroyo. — AMKM 2003 –15–

Multi-agent system Case study Conclusions Intro Dynamic of markets

Goal

Effective communications

Reduce amount of info shared by brokers Reduce distance among agents and their

interested marts

Evaluate

Mart’s optimal size Cost of agent’s relocation related to brokers

communication efforts

Impact of mart’s evolution in the service

Find best clustering algorithm

K-means, COBWEB, MajorClust,… etc

Dynamic of markets

Dodero, Arroyo. — AMKM 2003 –16–

Multi-agent system Case study Conclusions Intro Dynamic of markets

• 5. Conclusions

Features

Bottom-up, multi-agent approach to collaborative

knowledge production systems

Dynamic building of user communities Applicable to other collaborative KM production tasks

• e-Book & learning objects composition
• Calendar organization
• Software development (analysis & design)

Improvements

Further validation in multi-tiered scenarios Test of mixed interaction styles (retract, substitute,

reject)

Evaluation of dynamic evolution of marts

Conclusions