Agent as Mediators.docxkjk

8/14/2019 Agent as Mediators.docxkjk

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Software agents: are programs to which one can delegate (aspects of) a task they

differ from 'traditional" software in that they are personalized, continuously running

and semi-autonomous. These qualities make agent useful for a wide variety of

information and process management tasks.

E-Commerce encompasses a broad range of issues including security, trust,

reputation, law, payment mechanisms, electronic product catalogs (EPC),

intermediaries, multimedia, shopping experiences, and back-office management.

Agent technologies are desirable in E-Commerce areas.

Agent technologies employ in E-Commerce:

The more time and money that can be saved through automation. The easier it is to express preferences. The lesser the risks of making sub-optimal transaction decisions.

Explore the roles of agents in E-commerce in the context of a common model (CBB).

CBB (Consumer Buying Behavior) model research and comprises the actions and

decisions involved in buying and using goods and services.

CBB model is a powerful tool to help us understand the roles of agents as mediators

in E-commerce.CBB research focuses on the retail market.Its concepts pertain to business-to-business and consumer-to-consumer markets as

well.


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4-Negotiation:

This stage is about how to determine the terms of the transaction. Negotiation varies

in duration and complexity depending on the market. In traditional retail market,

prices and other aspects of transactions are fixed no room for negotiation. In other

markets, the negotiation of price or other aspects of the deal are needed to product

and merchant brokering.

5- Purchase and Delivery:

The purchase and delivery of a product can be signal the termination of the

negotiation stage or occur sometimes afterwards.

6-Product Service and Evaluation

This stage involves product service, customer service, and an evaluation of the

satisfaction of the overall buying experience and decision.

These stages represent an approximation and simplification of complex behavior.

CBB stages often overlap and migration from one to another can be non-linear.

From those, we can identify the roles of agents in E-commerce. The nature of agents

make them well suited for consumer behaviors involving information filtering and

retrieval, personalized evaluations, complex coordination and time based

transactions. Specifically these roles most notably to the Product Brokering,

Merchant Brokering, and Negotiation stages of the Consumer Buying Behavior

model.


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The Product Brokering stage of the CBB model is where consumers determine what

to buy. This occurs after a need has been identified (i.e., in the Need Identification

stage) and is achieved through a critical evaluation of retrieved product information.

Table l shows several agent systems that lower consumers' search costs when

deciding which products best meet their needs: personaLogic, Firefly and Tete-a-

Tete.

PenonaLogicis a tool that enables consumer to narrow down the products that best

meet their needs by guiding them through a large product feature space. The system

filter out unwanted products within a given domain by allowing shopper to specify

constraints on a product's features. A constraint satisfaction problem (CSP) engine


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Firefly services help consumer find products. However, instead of filtering products

based on features, Firefly recommends products via a "word of mouth"

recommendation mechanism called automated collaborative filtering (ACF). ACFfirst compares a shoppers product ratings with those of other shoppers.After

identifying the shopper's "nearest neighbors" (i.e., user with similar tastes), ACF

recommends products that they rated highly but which the shopper may not yet have

rated.


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This stage combines the consideration set from the previous stage with merchant-specific information to help determine who to buy from. Whereas the Product

Brokering stage compares product alternatives, the Merchant Brokering stage

compares merchant alternatives. Three representative agent systems that perform

merchant brokering are BargainFinder, Kasbah and Jango

BargainFinderwas the first shopping agent for on-line price comparisons. Given a

specific product, BargainFinder requests its price from each of nine different

merchant web sites using the same requests as a form of web browser.

It displays the result in a list ready to be compared from the consumer. Merchants

dont want to compete on price only so they blocked all price requests from

BargainFinder. Value added services that merchants offered in their web site were

being bypassed by BargainFinder and therefore not considered in the consumers

buying decisions.

In short, companies competing on price only wanted to be included in the

BargainFinder,but the other didnt want.


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Kasbahis an on-line, multi-agent system.A user wanting to buy or sell a goodcreates an agent, gives it some strategic direction, and sends it off into a centralized

agent marketplace.Kasbah agents pro-actively seek out potential buyers or sellersand negotiate with them. Each agent's goal is to complete a deal subject to a set ofuser-specified constraints, such as a desired price, a highest (or lowest) acceptable

price, and a date by which to complete the transaction.

This stage is about how to determine the terms of the transaction. The Negotiation

stage is where the price or other terms of the transaction are settled.

Three representative agent systems that employ differing negotiation protocols are

Kasbah, Tete-a-Tete and AuctionBot.


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Negotiation in Kasbahis straightforward.After buying agents and selling agents arematched, the only valid action in the negotiation protocol is for buying agents to offer

a bid to selling agents with no restrictions on time or price.Selling agents respondwith either a binding yes or no.

AuctionBotis a general purpose Internet auction server. AuctionBot users create new

auctions to sell products by choosing from a selection of auction types and then

specifying its parameters, such as auction time, reserved price, etc. Once the auction

is created the sellers negotiation is completely automated by the system as defined

by the auctioneer protocols and parameters of the selected auction.

Tete-a-Teteprovides a unique negotiation approach to retail sales. Unlike most other

on-line negotiation systems which competitively negotiate over price, Tete-a-Tete

agents cooperatively negotiate across multiple terms of a transaction e.g.. Warranties,

delivery times, service contracts, return policies, loan options, gift services, and othermerchant value-added services.


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This negotiation takes the form of multi-agent. Tete-a-Tete's shopping agents use the

evaluation constraints captured during the Product Brokering and Merchant

Brokering stages as dimensions of a multi-attribute utility. Tete-a-Tete integrates allthree of the Product Brokering, Merchant Brokering, and Negotiation CBB stages.


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Most of the technologies that support Agent-Mediated either directly or indirectlyfrom Artificial Intelligence. Such as:

Cognitive Models To extract meaning of ambiguous web pagesPlanning tripsLearning users Music preferencesDeciding which car to buy?

The majority of product recommender systems are developed using:

Content basedCollaborative basedConstraint based

1-Content based filtering:

The system processes information from various sources and tries to extract usefulelements about its content.

Techniques Used: can Vary Greatly in complexity

Such as:

Keyboard-based searchsimplest techniques involves matching differentcombinations of keywords.

Extracting semantic of the content by using techniques like associativenetworks of keywords in a sentence, or a directed graph of words that form the

sentence.


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Examples of systems that use Content based filtering:

Systems like BargainFinder, Jango

try to collect information from many differentweb information sources .and different sources have different input method,

presentation methods. So the recommender systems must adjust their interaction

methods depending on the web site.

They used wrappersto transform the information into a common format.

BargainFinder Jango

Hand-coded by Andersen

Consulting programmers.

was good at the beginning then it became

very hard to scale and it require

maintenance of the wrapper for each site

if the access method changed or its

catalog changed

Automatically creates wrappers bygeneralizing from example query

response to online merchant DB.

Jango method is not perfect but boasts a nearly 50% success.

2-Collaborative Filtering:

Fireflyuses Collaborative Filtering technology to recommend product to

customer. System using collaborative technique use feedback and ratings from

different consumers to filter information.

It doesnt attempt to analyze or understand the feature or the description of the

product, all its concerns about its rank.

3-Constraint Based:

Uses the feature of items to determine the relevance. It requires problem and solution

formulated in terms of variablesdomainsand constraints.


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Once formulated in this way a number of general and powerful constraint satisfaction

problem techniques can find a solution.

Has 3 types

Finite Domain: Finite set of variables Finite set of constraints : define the relationship among variables

In personaLogic CSP techniques are used in the Product Brokering CBB stage to

evaluate product alternatives

CSP techniques are core to Tete-a-Tete and are used to assist shoppers in the Product

Brokering, and Negotiation CBB stage. This achieved by providing product

constraints such as:

Price Delivery Time Warranty

Traditional shopping experiences can be quite diverse depending on the needs of the

customer for instances:

Consumer may just browsing without real intention to buy any thingKnow what to buy but not familiar with its specsMissing positive feedbackThey care about trust especially when money involvedCustomers preferred simple predictable agent with pre-determined negotiation

strategies


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Its a form of decision making where two or more parties search for possiblesolutions with the goal to reach a consensus.

Has 2 general approaches:

Distributive negotiationfocus on how each self-interested party cantake the largest piece of the available pie.

Example->AuctionBot and Kasbah.

Integrative negotiationfocus on all of the terms of a transaction andthe parties involved try to enlarge the available Pie.

The benefit of this approach over the Distributive is that its possible to

find new points in the solution space that benefit the both buyer and

seller.

ExCarnegie Mellon University which finds its roots in Distributed

artificial intelligence

Another approach is Contract Net.

Since there are a lot of agents and they use different protocols there areKnowledge Interchange Format, Knowledge Query Manipulation

Language, Ontolingua. These were designed for different agents so they

could describe knowledge and communicate it meaningfully in order to

interoperate.

In Business-to-Business world there is EDI ( Electronic DataInterchange) its a set of ANSI Protocols

Other E-Commerce protocol my need to include Internet-based EDI(EDINT) , XML/EDI , Open Buying on the Internet (OBI) for High-

volume , Low-dollar business-to-business purchase .


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Also another protocols such as Open Financial Exchange(OFX) forfinancial transactions ,Secure Electronic Transaction (SET) for credit

card transactions, and Open Profiling Standard (OPS) and PersonalPrivacy Preferences Project (P3P) for defining privacy for consumer

profile data .

EDI (Electronic Data Interchange) facilitate large-scale, repetitive, pre-arranged

transactions between businesses in specific industries with each industry adapting the

EDI protocol to its specific needs.

Standard EDI protocol are performed through expensive proprietary Value Added

Network (VAN)

EDI Disadvantages:

Ambiguous Expensive to implement and maintain used Focused on large scale business-to-business transaction


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