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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.

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what can agents do for p2p systems
What can agents do for P2P systems

Bin Yu

Department of Computer Science

North Carolina State University

what is p2p
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
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
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
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
Napster

128.1.2.3

(xyz.mp3, 128.1.2.3)

Central Napster server

napster7
Napster

128.1.2.3

xyz.mp3 ?

128.1.2.3

Central Napster server

napster8
Napster

128.1.2.3

xyz.mp3 ?

Central Napster server

gnutella10
Gnutella

xyz.mp3 ?

gnutella12
Gnutella

xyz.mp3

challenge
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
Distributed Hashing
  • 1. Map both objects and nodes into some topology (“id space”)

Objects

Nodes

distributed hashing15
Distributed Hashing
  • 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

Objects

Nodes

distributed hashing16
Distributed Hashing
  • 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

Objects

Nodes

chord basic idea
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 idea18
Chord - Basic Idea
  • Nodes “own” the part of the ID space between their ID and their predecessor’s ID.

0

chord basic idea19
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
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
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 example27
CAN: simple example

(K,V)

(a,b)

retrieve (K)

insert (K,V)

hash(K) = (a,b)

can routing
CAN: routing

(a,b)

(x,y)

quick review
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
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
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

why is the idea feasible
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
Paths to the Expert(s)

A

Mark

B

Jenny

C

User modeling

paths to the expert s36
Paths to the Expert(s)

A

Mark

B

Jenny

C

User modeling

paths to the expert s37
Paths to the Expert(s)

A

Mark

D

B

Jenny

C

User modeling

E

Uncooperative agents

paths to the expert s38
Paths to the Expert(s)

A

Mark

D

B

Jenny

C

User modeling

E

Uncooperative agents

paths to the expert s39
Paths to the Expert(s)

A

Mark

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 graph42
Referral Graph

Ar

A1

A2

A3

A4

referral graph43
Referral Graph

Ar

A1

A2

A3

A6

A4

Ar

Root of the graph

A5

Node has been visited

A1

Node has not been visited

A5

Redundant referral

referral graph44
Referral Graph

Ar

A1

A2

A3

A6

A4

A5

Question: node A5 and A6, which should be visited first?

weighted referral graph
Weighted Referral Graph

1.0

Ar

0.5

0.6

A1

A2

0.5

0.6

1.0

0.5

A3

A6

0.3

0.8

1.0

1.0

A4

0.3

1.0

A5

0.3

credits penalties propagation
Credits/Penalties Propagation

1.0

Ar

0.5

0.6

A1

A2

0.5

0.6

1.0

0.5

A3

A6

0.3

0.8

Answer

1.0

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
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
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
  • 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
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

Agent

Contact Info

Expertise

Profile

Contact Info

Expertise

Sociability

Cooperativeness

NeighborModels

NeighborModel

Cache

User Modeling

Expertise as a term vector { EPi, LC1, LC2, … LCp}

  • Each agent learns models of others based on experience:
    • When a good service is obtained, the expertise of the provider is 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
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
  • 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
when to generate referrals
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
  • 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
  • for a value of .
architecture
Architecture

Jenny

Mark

Profile

NeighborModels

Cache

Agent

Referral

Networks

MARS is composed of a registration server and a bunch of software agents.

slide54

Feedback

Queries

Answers

Queries

Answers

Referrals

Queries/Answers/Referrals

Outgoing messages Incoming messages

GUI

Learner

Collection-P

Profile

Matchmaker

Planner

Collection-N

NeighborModels

Close Friend List

Wrapper

Classifier

Heuristics

Priority queue

control flow
Control Flow

GUI

Communication

4. Update

1. Incoming Messages

5. Get new message (Queries/answers)

New Message

Notifier

Incoming Message Processor

(Update every 5 minutes)

6. Send message

2. Create

message queue

2.Create message queue

New Message

Viewer

Send Queries

Out-going Message Processor

8. Outgoing Messages

3. Send

out message

Planner

Answer/Refer

Evaluation

Learner

7. User feedback

.

.

.

.

Referral Graph Builder

NeighborModels

Profile

conclusion
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
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
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
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
slide63
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
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