LYU 0004 Mobile Agent’s Community
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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
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
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