LYU 0004LYU 0004Mobile Agent’s CommunityMobile Agent’s Community

Group Member: Group Member: Cheng Tsz Hei Cheng Tsz Hei

Ho Man LamHo Man Lam

Outline of the Presentation

• Project’s system architecture

• Introduction multi-sellers & multi-buyers scenario

• The shortcoming of traditional approach

• The advantage of our system over the old one

Outline of the Presentation

• Algorithm used to handle the communication

• Difficulties

• Future plans

Mobile’s AgentMobile’s Agent

• The mobile agents can act on behalf of the user in the computer network.

• Mobile agents are programs that can be dispatched from one computer and transported to a remote computer for execution.

Mobile Agent’s CommunityMobile Agent’s Community

• Group of agents with different purposes

• Several network computers support agent’s platform

• Easy to be accessed by web client

• Form a virtual community

Model of System ArchitectureModel of System ArchitectureFront-endFront-end

InternetBrowser

InternetBrowser

Web ServerInternet

(Mobile agent'scommunity)

Request

Signal

Model of System Architecture (II)Model of System Architecture (II)Front-endFront-end

InternetBrowser

InternetBrowser

Web ServerInternet

(Mobile agent'scommunity)

Signal

Response

ResponseData Transfer

Model of System Architecture (III)Model of System Architecture (III)Developer’s ViewDeveloper’s View

Workplace Workplace

WorkplaceWorkplace

Workplace

Internet

Model of System Architecture (IV)Model of System Architecture (IV)Developer’s ViewDeveloper’s View

Workplace Workplace

WorkplaceWorkplace

Workplace

Internet

Internet

Workplace Workplace

WorkplaceWorkplace

Workplace

Model of System Architecture (V)Model of System Architecture (V)Developer’s ViewDeveloper’s View

Model of System Architecture (VI)Model of System Architecture (VI)Front-end & Developer’s ViewFront-end & Developer’s View

Workplace

Workplace

Workplace

Workplace

WebServer

WebServer

WebServer

WebServer

Model of System Architecture Model of System Architecture (VII)(VII)Inside WorkplaceInside Workplace

Workplace

Service available

Multi-sellers & Multi-buyers ScenariosMulti-sellers & Multi-buyers Scenarios

• In this scenario, the buyers or the sellers can In this scenario, the buyers or the sellers can assign their trade strategies by using graphical assign their trade strategies by using graphical user interfaces in the web site. user interfaces in the web site.

• One of workplaces connecting to the web then One of workplaces connecting to the web then delegates mobile agents to autonomously perform delegates mobile agents to autonomously perform the bargain behavior for the client.the bargain behavior for the client.

Existing ApproachExisting Approach

• Electronic market Electronic market

• Fix web serverFix web server

• Applet or CGI technologyApplet or CGI technology

• Fully controlled by usersFully controlled by users

Shortcoming of Traditional Shortcoming of Traditional ApproachApproach

• Central access pointCentral access point

• Deficiency of interaction Deficiency of interaction

• Transaction localization Transaction localization

Central Access Point• One web serverOne web server

• Low response timeLow response time

• Traffic jam for local region of networkTraffic jam for local region of network

Deficiency of Interaction Deficiency of Interaction

• Seller waits for buyer & vice versaSeller waits for buyer & vice versa

• Time consumingTime consuming

• Easy to miss time slotEasy to miss time slot

Transaction Localization

• Location boundaryLocation boundary

• Limit the potential clientsLimit the potential clients

• Hard to promote globallyHard to promote globally

Advantage of Our SystemAdvantage of Our System

• Location transparency Location transparency

• Failure transparency Failure transparency

• Scaling transparency Scaling transparency

• Fast response Fast response

Location Transparency• Hide the real location of marketplaceHide the real location of marketplace

• Agents will locate the paths of possible Agents will locate the paths of possible marketplacesmarketplaces

Failure TransparencyFailure Transparency • RedundancyRedundancy

• Agents move from one marketplace to Agents move from one marketplace to another oneanother one

• No transactions are suspended and No transactions are suspended and discardeddiscarded

Scaling TransparencyScaling Transparency • Build up list of address of workplacesBuild up list of address of workplaces

• Allow to join or leave at any timeAllow to join or leave at any time

• Expands infinityExpands infinity

Fast ResponseFast Response • Biding representative to their clients in the Biding representative to their clients in the

bargaining sites bargaining sites

• Immediate response according to client’s Immediate response according to client’s preference preference

• Maximize profit for both buyers and sellersMaximize profit for both buyers and sellers

Concept of AlgorithmConcept of Algorithm• Zero-Intelligence-Plus (ZIP) TradersZero-Intelligence-Plus (ZIP) Traders

• Dave Ciff, Hewlett Packard Laboratories, Dave Ciff, Hewlett Packard Laboratories, Bristol, England, 1997Bristol, England, 1997

• Act as double auction marketAct as double auction market

• Behave as human marketBehave as human market

• Obeys the theory of supply and demandObeys the theory of supply and demand

Concept of Algorithm (II)Concept of Algorithm (II)• Limit price is privateLimit price is private

• Shout-prices observed in the marketShout-prices observed in the market

• Each agent adjust its margins up or downEach agent adjust its margins up or down

• Accept or ignoreAccept or ignore

Algorithm for TradingSeller Behaviors.Seller Behaviors.

if (the last shout was accepted at price q).if (the last shout was accepted at price q).thenthen

          any seller any seller ssii for which for which ppii <= <= qq should raise its profit should raise its profit margin.margin. if(the last shout was a bid).if(the last shout was a bid).

then.then.

any active sellers any active sellers ssii for which for which ppii >= >= qq should lower its should lower its margin.margin.else.else.

if the(last shout was an offer).if the(last shout was an offer).thenthen

any active seller any active seller ssii for which for which ppii >= >= qq should lower its should lower its margin.margin.

  ..where where qq is the shout price of the last shout. is the shout price of the last shout.

ppi i is shout price of trader i.is shout price of trader i.

Algorithm for Trading (II)Buyer BehaviorsBuyer Behaviors if (the last shout was accepted at price if (the last shout was accepted at price qq))

thenthen

any buyer bany buyer bi i for which for which ppii >= >= qq should raise its profit should raise its profit

marginmargin if(the last shout was a offer)if(the last shout was a offer)

thenthen

any active buyers bany active buyers bi i for which for which ppii <= <= qq should lower its should lower its marginmargin

elseelse if the(last shout was an offer)if the(last shout was an offer) thenthen

any active buyer any active buyer bbii for which for which ppii <= <= qq should lower its should lower its margin margin

  where where qq is the shout price of the last shout. is the shout price of the last shout.

ppi i is shout price of trader iis shout price of trader i

Algorithm for Trading (III)• How to shout price Pi(t) ?

At time t,At time t, PPii(t) =λ(t) =λi,ji,j (1+μ (1+μ ii(t))(t))

where λwhere λi,ji,j is limit price, is limit price,

μ μ ii(t) profit-margin(t) profit-margin

For seller, profit margin constraint between 0 <= μ For seller, profit margin constraint between 0 <= μ ii(t) < ∞(t) < ∞

For buyer, profit margin constraint between -1<= μ For buyer, profit margin constraint between -1<= μ ii(t) < 0(t) < 0

Algorithm for Trading (III)• How to calculate the profit-marginμ i(t+1)?

Using Widrow-Hoff “delta rule”:Using Widrow-Hoff “delta rule”:

μ μ ii(t+1) = (P(t+1) = (Pii(t) + T(t) + Tii(t)) /λ(t)) /λi,ji,j – 1 – 1

where Twhere Tii(t) momentum-based update(t) momentum-based update

TTii(0) = 0 for all i(0) = 0 for all i

Difficulties• Problem domain Problem domain

• ConfigurationConfiguration

• Implementation of grasshopper Implementation of grasshopper

• Weakness of java SerlvetWeakness of java Serlvet

Future Plan• Real-time interaction Real-time interaction • Learning technique for agents Learning technique for agents

• Higher-order adaptation mechanismsHigher-order adaptation mechanisms• Game-theory analysis Game-theory analysis

• Support more scenarios in mobile agent’s Support more scenarios in mobile agent’s paradigm paradigm

• Security issue Security issue • Professional design of web site Professional design of web site

The End