LYU 0004 Mobile Agents Community Group Member Cheng

LYU 0004 Mobile Agent’s Community Group Member: Cheng Tsz Hei Ho 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 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 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 Architecture Front-end

Model of System Architecture (II) Front-end

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

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

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

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

Model of System Architecture (VII) Inside Workplace

Multi-sellers & Multi-buyers Scenarios • In this scenario, the buyers or the sellers can assign their trade strategies by using graphical user interfaces in the web site. • One of workplaces connecting to the web then delegates mobile agents to autonomously perform the bargain behavior for the client.

Existing Approach • • Electronic market Fix web server Applet or CGI technology Fully controlled by users

Shortcoming of Traditional Approach • Central access point • Deficiency of interaction • Transaction localization

Central Access Point • One web server • Low response time • Traffic jam for local region of network

Deficiency of Interaction • Seller waits for buyer & vice versa • Time consuming • Easy to miss time slot

Transaction Localization • Location boundary • Limit the potential clients • Hard to promote globally

Advantage of Our System • • Location transparency Failure transparency Scaling transparency Fast response

Location Transparency • Hide the real location of marketplace • Agents will locate the paths of possible marketplaces

Failure Transparency • Redundancy • Agents move from one marketplace to another one • No transactions are suspended and discarded

Scaling Transparency • Build up list of address of workplaces • Allow to join or leave at any time • Expands infinity

Fast Response • Biding representative to their clients in the • • bargaining sites Immediate response according to client’s preference Maximize profit for both buyers and sellers

Concept of Algorithm • Zero-Intelligence-Plus (ZIP) Traders • Dave Ciff, Hewlett Packard Laboratories, Bristol, England, 1997 • Act as double auction market • Behave as human market • Obeys theory of supply and demand

Concept of Algorithm (II) • • Limit price is private Shout-prices observed in the market Each agent adjust its margins up or down Accept or ignore

Algorithm for Trading Seller Behaviors. if (the last shout was accepted at price q). then any seller si for which pi <= q should raise its profit margin. if(the last shout was a bid). then. any active sellers si for which pi >= q should lower its margin. else. if the(last shout was an offer). then any active seller si for which pi >= q should lower its margin. . where q is the shout price of the last shout. pi is shout price of trader i.

Algorithm for Trading (II) Buyer Behaviors margin if (the last shout was accepted at price q) then any buyer bi for which pi >= q should raise its profit margin if(the last shout was a offer) then any active buyers bi for which pi <= q should lower its margin else if the(last shout was an offer) then any active buyer bi for which pi <= q should lower its where q is the shout price of the last shout. pi is shout price of trader i

Algorithm for Trading (III) • How to shout price Pi(t) ? At time t, Pi(t) =λi, j (1+μ i(t)) where λi, j is limit price, μ i(t) profit-margin For seller, profit margin constraint between 0 <= μ i(t) < ∞ For buyer, profit margin constraint between -1<= μ i(t) < 0

Algorithm for Trading (III) • How to calculate the profit-marginμ i(t+1)? Using Widrow-Hoff “delta rule”: μ i(t+1) = (Pi(t) + Ti(t)) /λi, j – 1 where Ti(t) momentum-based update Ti(0) = 0 for all i

Difficulties • • Problem domain Configuration Implementation of grasshopper Weakness of java Serlvet

Future Plan • Real-time interaction • Learning technique for agents • • Higher-order adaptation mechanisms • Game-theory analysis Support more scenarios in mobile agent’s paradigm Security issue Professional design of web site

The End