1
Dynamic Generation of Agent Communities from Distributed Production and Content-Driven
Delivery of KnowledgeAAAI Spring Symposium on Agent-Mediated
Knowledge Management (AMKM-03)
Sinuhé ArroyoInstitut für Informatik IFI
Next Generation Research Group University of Innsbruck, Austria
Juan Manuel DoderoComputer Science DepartmentUniversidad Carlos III de Madrid
Richard BenjaminsIntelligent Software Components
(iSOCO), S.A. Madrid, Spain
2
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 of Knowledge
Dodero, Arroyo. — AMKM 2003 –3–
Multi-agentsystem
Casestudy
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-agentsystem
Casestudy
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-agentsystem
Dodero, Arroyo. — AMKM 2003 –5–
Multi-agentsystem
Casestudy
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-agentsystem
Interaction group S1 Interaction group S2
Interaction group M
Agent
Proxy Agent
Agent Agent
Agent Agent
Proxy Agent
Agent Agent
Dodero, Arroyo. — AMKM 2003 –6–
Multi-agentsystem
Casestudy
Conclusions
Intro
Dynamic of markets
Interaction within marts
Multi-agentsystem
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-agentsystem
Casestudy
Conclusions
Intro
Dynamic of markets
Consolidation protocol
Distribution
Consolidation
Idle Failure
Success
any message
start(send proposal)
receive anyworse-evaluated
receive consolidation better-
evaluated
receive consolidation better-
evaluated
t0 expires receive proposal better-evaluated
receive anyworse-evaluated
t1 expires
Distribution
Consolidation
Multi-agentsystem
Dodero, Arroyo. — AMKM 2003 –8–
Multi-agentsystem
Casestudy
Conclusions
Intro
Dynamic of markets
3. Case study
Casestudy
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-agentsystem
Casestudy
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 q
Proposal q
Proposal qu(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
Casestudy
Dodero, Arroyo. — AMKM 2003 –10–
Multi-agentsystem
Casestudy
Conclusions
Intro
Dynamic of markets
Results: quality (grade of fulfilment)
75
5554
75
4343
75
5554
43
0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14 16 18 20
Proposals ordered by submission time
Gra
de
of
fulf
illm
en
t (%
)
Issued in two-mart scenario Issued in one-mart scenario
Casestudy
Dodero, Arroyo. — AMKM 2003 –11–
Multi-agentsystem
Casestudy
Conclusions
Intro
Dynamic of markets
Results: consolidation lifetime
1622 2183 3976 7401
190288
13439 7291
183027
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
200000
1 2 3 4
Consolidated proposals ordered by instant of consolidation
Co
ns
olid
ati
on
life
tim
e (
tim
e u
nit
s)
Casestudy
Dodero, Arroyo. — AMKM 2003 –12–
Multi-agentsystem
Casestudy
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
con
flic
ts/t
ime
un
it
No. of conflicts (two-mart) No. of conflicts (one-mart)
Casestudy
Dodero, Arroyo. — AMKM 2003 –13–
Multi-agentsystem
Casestudy
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-agentsystem
Casestudy
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 interestDynamic of
markets
Dodero, Arroyo. — AMKM 2003 –15–
Multi-agentsystem
Casestudy
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-agentsystem
Casestudy
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 martsConclusions