What can agents do for P2P
systems
Bin Yu
Department of Computer Science
North Carolina State University
What is P2P
Definition
• A distributed system in which all nodes have identical
responsibilities, and all communication is symmetric.
• An application-level Internet on top of the Internet
Resource sharing on a massive scale
• Files, cycles, equipment, people...
Conferences
• O’Reilly P2P conference 2001
(conferences.oreilly.com/p2p/)
• First International Workshop on Peer-to-Peer Systems
(IPTPS '02)
(http://www.cs.rice.edu/Conferences/IPTPS02/)
Historical Perspective
P2P is nothing new – ARPANET
Internet was fundamentally designed to be a P2P
system
• any 2 computers could send packets to each other
– no firewalls / no network address translation
– no asymmetric connections (V.90, ADSL, cable, etc.)
The popularity of www, telnet, ftp... changed the
paradigm to client/server
• Server
– Service Provider
– Powerful machine to server a large number of clients
• Client
– Service Consumer
– client, machine with limited capacity, is used to request
services
P2P: today
Many emerging applications:
• Napster, Gnutella, Freenet…
P2P Properties:
•
•
•
•
no central coordination
no peer has a global view of the system
global behavior emerges from local interactions
all existing data and services are accessible from any
peer
• peers are autonomous
• peers and connections are unreliable
P2P File-sharing
Napster
• Decentralized storage of actual content
– transfer content directly from one peer (client) to another
• Centralized index and search
Gnutella
• Not like Napster, with decentralized indexing
• Search via flooding
• Direct download
Napster
128.1.2.3
Central Napster server
Napster
128.1.2.3
Central Napster server
Napster
128.1.2.3
Central Napster server
Gnutella
Gnutella
xyz.mp3 ?
Gnutella
Gnutella
Challenge
“Location Resolution”
• Given an object (might be name, attribute, or even
content)
• Return a channel to a node (peer) that has that object
Approaches:
• Centralized Index (Napster)
• Broadcast information to be resolved (Gnutella)
• Distributed Hashing (Chord, CAN)
Distributed Hashing
Objects
Nodes
1. Map both objects and nodes into some topology (“id space”)
Distributed Hashing
Objects
Nodes
1. Map both objects and nodes into some topology (“id space”)
2. Each node “owns” some neighborhood in the topology, has
channel to some neighbors
Distributed Hashing
Objects
Nodes
1. Map both objects and nodes into some topology (“id space”)
2. Each node “owns” some neighborhood in the topology, has
channel to some neighbors
3. Topological structure lets query be routed to the “owner” of a
given point
Chord - Basic Idea
Topology is a ring of ordered, fixed-size IDs (say 32
bits)
• Node ID based on IP address, object ID based on name,
content, ...
0
Chord - Basic Idea
Nodes “own” the part of the ID space between their ID
and their predecessor’s ID.
0
Chord - Basic Idea
Each node has a channel to its successors at distances
1, 2, 4, 8, 16, ..., 2^(m-1)
• where m = log_2 of the ring size (32 in this case)
0
Chord: Resolution
Get ID of desired object
Find the last node whose ID is LESS than the
desired ID
• Look in finger table to find farthest-away neighbor
whose ID is LESS than the desired ID
• Ask it for somebody closer
That node’s successor is the “owner” of the object
CAN: Basic Idea
Topology is an N-dimensional torus
• N=2 for simple examples
Each node is responsible for a subrange in each
dimension
• Space is partitioned among all nodes
Route via neighbors -- move in direction of
destination
CAN: simple example
1
CAN: simple example
1
2
CAN: simple example
3
1
2
CAN: simple example
3
1
2
4
CAN: simple example
CAN: simple example
(K,V)
(a,b)
retrieve (K)
insert
(K,V)
hash(K) = (a,b)
CAN: routing table
CAN: routing
(a,b)
(x,y)
Quick Review
Two similar approaches to locating objects by
“computed routing”
• Similar to Manhattan Street Networks
• Mainly for distributed storage systems.
All these P2P networks ignore underlying topology!
• Each node has relatively simple function
• Network is not reconfigurable, and there is no learning
happened
• Brute-force searching, and broadcast the request to all the
peers
• Some networks, i.e., social networks, can not be partitioned
by IP.
Agent-based P2P networks
Software agents
• Computer programs which can perform a set of tasks
autonomously.
How to find an appropriate service or person
• Through referrals
• Approach: automate the process using software agents
through referrals.
Agent-based referral networks
• Software agents cooperate to direct requests toward
appropriate service or person.
Agent-based Referral Networks
Referral systems
• MINDS 1987
• ReferralWeb 1996,1997
A computational model of agent-based referral networks
• Each node is represented a software agent
• Learn models of each other in term of
– Expertise (ability to produce correct domain answers)
– Sociability (ability to produce accurate referrals)
– Cooperativeness (willingness to produce answers or referrals)
.
32
Global View of Referral Networks
Why is the idea feasible?
Relative short distance between any two nodes
• Small-world phenomenon – six (5.5) for human social
networks of USA (Stanley Milgram, 1960s)
The relative small value indicates
• Intelligent software agents can follow only the relevant
links and find the desired experts.
Paths to the Expert(s)
A
Mark
B
Jenny
C
User
modeling
Paths to the Expert(s)
A
Mark
B
Jenny
C
User
modeling
Paths to the Expert(s)
Mark
A
D
B
Jenny
C
E
Uncooperative agents
User
modeling
Paths to the Expert(s)
Mark
A
D
B
Jenny
C
E
Uncooperative agents
User
modeling
Paths to the Expert(s)
Mark
A
D
E
B
Jenny
C
User
modeling
Uncooperative agents
Note that: all of the queries were sent out from Jenny.
A referral graph encodes how the computation spreads from
Jenny and referrals or answers are sent back to Jenny.
Referral Graph
Ar
A1
A2
Referral Graph
Ar
A1
A3
A2
Referral Graph
Ar
A1
A3
A4
A2
Referral Graph
Ar
A1
A2
A3
A6
A4
A5
Redundant referral
Ar
Root of the graph
A1
Node has been visited
A5
Node has not been
visited
Referral Graph
Ar
A1
A3
A2
A6
A4
A5
Question: node A5 and A6, which should be visited first?
Weighted Referral Graph
1.0
Ar
0.6
A1
0.5
A2
0.6
0.5
1.0
0.5
A3
0.3
A6
1.0
1.0
A4
0.3
1.0
A5
0.3
Wnodek  Wnodei Wlinki
iN
0.8
Credits/Penalties Propagation
1.0
Ar
0.6
A1
0.6
0.5
A2
0.5
1.0
0.5
A3
0.3
A6
1.0
0.8
Answer
1.0
A4
0.3
1.0
A5
0.3
Suppose A6 returns an answer, then Ar will
update the expertise for A6 and sociability for
A1, A2, A3, A4.
Research Challenges
Improving the accuracy of “referrals”
• User modeling and multiagent learning
Avoiding interaction with undesirable participants
• How to judge the trustworthiness of one agents
Studying key properties of referral networks
• Evolution of referral networks
• Transition to small-world networks through interactions.
• Protocols that foster the small-world phenomenon.
47
Vector Space Model
Let D = {d1, d2, …, dn} denotes a collection of documents. {t1,
t2, … tp} be the dictionary (a set of all the words)
Each document d is represented as a p-dimensional vector d =
dt1, dt2, … dtp
d ti  (0.5  0.5
tfi
n
)(log
)
tf max
df i
where tfi is the number of times word ti appears in document d
(the term frequency),
dfi is the number of documents in the collection which contain
ti (the document frequency),
n is the number of documents in the collection,
Tfmax is the maximum term frequency over all words in D.
Ontology
Understand the information context
• Ontology is a set of definitions of formal vocabulary
Class hierarchy of AI domain.
• We manually construct an ontology for AI domain
• Totally 19 domains
–
–
–
–
–
AI architecture
Agents and multiagent systems
…
Planning and search
Vision and robotics
User Modeling
Expertise as a term vector { EPi, LC1, LC2, … LCp}
Agent
Contact Info
Expertise
Profile
NeighborModel
NeighborModels
Contact Info
Expertise
Sociability
Cooperativeness
Cache
Each agent learns models of others based on experience:
• hen a good service is obtained, the expertise of the provider is
W
revised upwards as is the sociability of those who gave referrals to it.
•When a poor service is obtained, the revisions are downward.
When to answer a query?
Definition 1:
Given a query vector Q = q1, …, qn and an expertise vector E =
e1, …, en, the similarity between Q and E is defined as

n
QE 
t 1
t 1 (q t )
n
q tet
2
2
(e
)
t 1 t
n
Rule 1:
Given a query vector Q with the expertise domain Ci and a
threshold canAnswer, where 0  canAnswer 1, it says there is a
good match between the user Pi and the query Q for a domain
Ci if
max{L ci , Q  E Pi }  ωcanAnswer
When to generate referrals?
Definition 2:
Given a query vector Q with the domain Ci, the relevance of a
query Q to any neighbor Pj is defined as
Q Ci Pj  max{L Ci , Q  E Pj }(1  η)  ηSPj
where EPj and SPj are the expertise and sociability of agent Pj,
respectively; and  and (1- ) are the weights given to
sociability and expertise.
Rule 2:
Given a query vector Q with the domain Ci, and a threshold
canReferral, a neighbor is relevant to Q if
Q  Ci Pj  ωcanReferra l
for a value of .
Architecture
Jenny
Mark
Profile
NeighborModels
Agent
Cache
Referral
Networks
MARS is composed of a registration server and a bunch of
software agents.
GUI
Feedback
Learner
Queries
Answers
Profile
Collection-P
NeighborModels
Collection-N
Matchmaker
Planner
Close Friend List
Wrapper
Queries
Answers
Referrals
Classifier
Heuristics
Priority queue
Queries/Answers/Referrals
Outgoing messages
Incoming messages
Control Flow
GUI
Communication
4. Update
New Message
Notifier
New Message
Viewer
5. Get new message (Queries/answers)
6. Send message
2. Create message queue
2. Create
message queue
Incoming
Message
Processor
(Update every 5
minutes)
Send Queries
Answer/Refer
Evaluation
.
.
.
.
Learner
Planner
3. Send
out message
7. User
feedback
NeighborModels
Profile
Referral Graph
Builder
Out-going
Message
Processor
1. Incoming
Messages
8. Outgoing
Messages
Main windows of MARS
Sign up window of MARS
Window for incoming queries/answers
Conclusion
A computation model of agent-based referral networks
• Improving the accuracy of referrals
• A natural way for people to seek information
• Applied in building multiagent systems in general
A probabilistic model of distributed reputation
management
• Leads to a decentralized society in which agents help each
other weed out undesirable players.
A prototype system MARS
• Limited in AI domain
• Learning the knowledge in general is nontrivial.
• Privacy when sharing with user’s email account.
Future Work
Evaluate the efficiency of referral networks
• Reconstruct the social networks using AAAI (1980-2000)
and IJCAI (1981-1999) proceedings
• Visualize the whole network using KrackPlot and/or
UNINET
Economic model of referral networks
• Incentive of help
• Payment systems
Trust model of referral networks
• Lying and rumors
Wants to know more?
Book
• Peer-to-peer: harnessing the power of disruptive
techniques, Andy Oram (ed.) O’Reilly & Associates, Inc.
Conference
• O’Reilly P2P conference 2001
(http://conferences.oreilly.com/p2p/)
• First International Workshop on Peer-to-Peer Systems
(IPTPS '02)
(http://www.cs.rice.edu/Conferences/IPTPS02/)
• AAMAS-02 Workshop on Regulated Agent-based Social
systems
http://www.informatik.uni-hamburg.de/TGI/events/rasta02/
Bibliography
Journal Papers
• Bin Yu and Munindar P. Singh, Distributed Reputation Management for
Electronic Commerce, Computational Intelligence, 2002, to appear
• Bin Yu, Mahadevan Venkatraman and Munindar P. Singh, An Adaptive
Social Network for Information Access: Theoretical and Experimental
Results, Journal of Applied Artificial Intelligence, 2002, to appear.
• Munindar P. Singh, Bin Yu and Mahadevan Venkatraman, Beyond
Communication: Linking People and their Communities, Communications
of the ACM, 2001,44(4):49-54
Conference Papers
• Bin Yu and Munindar P. Singh, An Evidential Model of Distributed
Reputation Management, In Proceedings of First Joint Conference on
Autonomous Agents and Multiagent Systems, 2002, to appear
• Bin Yu and Munindar P. Singh, Emergence of Agent-based Referral
Networks (poster), In Proceedings of First Joint Conference on Autonomous
Agents and Multiagent Systems, 2002, to appear
• Bin Yu and Munindar P. Singh, Towards a probabilistic model of distributed
reputation management, Proceedings of Fourth International Workshop on
Deception, Fraud and Trust in Agent Societies, pages 125-137, 2001
• Bin Yu and Munindar P. Singh, A Social Mechanism of Reputation
Management in Electronic Communities, Proceedings of Fourth
International Workshop on Cooperative Information Agents, pages 154-165,
2000.
• Mahadevan Venkatraman, Bin Yu and Munindar P. Singh, Trust and
Reputation Management in a Small-World Network, accepted by
ICMAS'2000 (Poster), Proceedings of Fourth International Conference on
MultiAgent Systems, pages 449-450.
• Bin Yu, Mahadevan Venkatraman and Munindar P. Singh, A Multiagent
Referral System for Expertise Location, Proceedings of AAAI’99 Workshop
on Intelligent Information Systems, pages 66-69, 1999.
• http://www4.ncsu.edu/~byu
Acknowledgements
Dr. Munindar P. Singh (my thesis advisor)
• For his advice and support.
Dr. Henry A. Kautz
• For his encouragement and discussion.
My other committee members
• Dr. James Lester, Dr. Carla Savage, especially Dr. Peter
Wurman for their time and valuable comments.
People working on the MARS project
• Mahadevan Venkatraman, Amit Chopra
• Wentao Mo, Paul Jose Palathingal
The End