Multi-Agent Systems & E-Commerce

Martin Beer,

School of Computing & Management Sciences,

Sheffield Hallam University, Sheffield,

United Kingdom

m.beer@shu.ac.uk

Agents for e-Commerce

Agents for eCommerce– e-Commerce– Consumer's buying behavior– Agents as mediators in

eCommerce– Information economy

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Electronic commerce

Components• interactive business and financial transaction• electronic cataloguing• electronic order tracking services• automatic billing and payment services• electronic funds transfer• vendor registration and electronic "brand naming"• automatic ordering, contracting and procurement• data mining of consumer information for customer

profiling• advertising of products and customization of

advertisements

Transactions business-to-business

business-to-consumer

consumer-to-consumer

Difficulties of e-Commerce

The Web has a number of features that limits its use as an "information market"

Problems related to using the Web for eCommerce: Trust

Privacy and security

Billing

Reliability

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Marketing Consumer's Buying Behavior (CBB) research - a number of models of the consumer's behavior

Most common stages; a simplification; some stages may overlap

CBB - Guttman e.a., 1998

Need investigation

Product brokering

Merchant brokering

Negotiation

Purchase and delivery

Product service and evaluation

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Consumer's buying behavior

1.3 Agents as mediators in eCommerce1.3 Agents as mediators in eCommerceMost appropriate for mediating behaviors involving information filtering and retrieval, personalized evaluation, complex coordination and negotiation

Persona Bargain Auction Fish

Logic Firefly Finder Jango Kasbah Bot T@T Market

Needidentification

Productbrokering

Merchantbrokering

Negotiation

Purchaseand delivery

Productservice 6

(a) Comparison shopping agents Search online shops to find products, merchants and best deals

Product brokering

• guides the consumers through a large product feature space

• allows shoppers to specify constraints on a product and scores the products

• CSP engine: hard constraints and soft constraints

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Persona Logic

• helps consumers find products• uses "word of mouth" recommendations• ACF = Automated Collaborative Filtering• identifies the shopper's "nearest neighbours" and offers

products highly rated by them

Merchant brokering

• the first agent for price comparison• given a specific product, the agent requests its price from

each of nine different merchant Web sites using the same http request as a Web browser

• Problem: some merchants block access to their prices; other merchants volunteer their prices

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BargainFinder

Firefly

• helps users decide what to buy• finds specifications and product reviews• makes recommendations to the user• performs comparison shopping for the best buy• monitors "what's new" lists, watches for special offers• Problem = Web pages are different; exploits:

Navigation regularities Corporate regularities Vertical separation

• has 2 key components: a component to learn vendor description a comparison shopping component

• Solves the merchant blocking issue by having the product requests originating from each consumer's Web browser instead of a centralised site as in BargainFinder appear as requests from real customers

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Jango

Product brokering and merchant Product brokering and merchant brokering agents use information brokering agents use information filtering techniquesfiltering techniques

• content-based filtering, e.g. associative networks of keywords as in Jango

• constraint-based filtering, like in PersonaLogic, T@T

• collaborative-based filtering, like in Firefly

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(b) Auction bots Agents that can organize and/or participate in online auctions for goods

Aim = develop a Web-based system in which users can create their own agents to buy and sell goods on their behalf

User options:

Create a new buying agent

Create a new selling agent

See currently active agents

Create a new finding agent

Browse the marketplace for active agents11

Kasbah

• Selling agent parameters set by the user:

- desired date to sell the good

- desired price to sell the good

- minimum price to sell at

- "decay" function of the price over time to determine the current offer price

• anxious - linear function

• cool headed - quadratic function

• frugal - exponential function• Buying agent parameters set by the user

- date to buy the item by

- desired price

- maximum price

- "growth" function of price over time12

• Kasbah agents operate in a marketplace• The marketplace manages a number of ongoing auctions

matching requests for goods with offers• Negotiation protocol

- buying agents offer bids to sellers

- selling agents respond with yes or no

• User agents negotiate across multiple attributes of a transaction, e.g., warranty length and options, shipping time and cost, service contract, return policy, quantity, accessories, credit options, payment options

• Agents quantify those aspects using a multi-attribute utility function

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Tête-à-tête

• A virtual institutions corresponding to a traditional fish market which exists in Blanes (Girona) a small fishermen's village in Spain

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Fishmarket

BA

Auct

BMSM

SA

Buyer'sregister

Credits andgoods delivery

Goods'register

Sellers'settlements

Goods showand auction

5 basic scenes

BA = buyer's admitterSA = seller's admitterBM =buyer's managerSM = seller's managerAuct = auctioner

Market operation (simplified)

1. Open auction and register sellers (SA)

2. Collect products from sellers (SM)

3. Collect buyers (BA)

4. Present products at price w (4.. 7 - Auct)

5. if silence then

decrease w

go to 4

6. if first bid w' w

then adjudicate product 8. Verify credit (BM)

go to 8 9. if not solvable (BM)

7. if two equal bids then fine or expell

then increase w increase w to x * w'

go to 4 10. else sell product

update buyer's credit (BM)

update seller's credit (SM)

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The first valid offer is the one to win the round An offer is valid if the bidder has enough credit to pay for that

bid Fishmarket was also tested for closed bid auctions and Vickrey

auctions Does not automate negotiation

Problems with auction botsProblems with auction botsMain difficulty - trust if:

• the agent really understands what the user wants• the agent is not going to be exploited by other agent• the agent does not end up with a poor agreement

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1.4 Information economy1.4 Information economy

• University of Michigan Digital Library (UMDL) is structured as a collection of agents that can buy and sell services from each other

• Treating a library as an information economy provides a framework for making decentralised decisions about allocation of limited information goods and services available

• The services and protocols offered by UMDL infrastructure are called SMS = Service Market Society

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The Service Market SocietyService Market Society implements a multi-agent information economy where agents buy and sell services from each other.

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UIA

SCA

AMA

QPA

CIARegistry

Auction

QPA

Bid phase

Query phase

Find phase

Query

Query

Label

Match me witha seller at a price

Match me witha buyer at a price

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3

4

5 6

62

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8

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Inforesources

• Ontology of services• SCA classifies the service description into a sub-

sumption-based taxonomy SCA matches requests for services to "semantically close" descriptions

• Auction specification type of good timing requirements terms

- per-query or subscription (how is bundled)

- topic, audience

- redistribute or read-only (terms)

- individual or library or group (to whom is sold) how often the auction is cleared price determination rule what info is publically available

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• QPAs bid their marginal cost = what it would cost them to provide another unit of the product

Cost(query) = A * load2 + B * load

MarginalCost(query) = 2 * A * load + B

• The Auction matches current lowest price seller with a buyer if the buyer's bid is above that price

• Once a transaction occurs, both buyers and sellers are removed from the active list and the QPA recomputes its marginal cost based on having an additional query to process

• Then QPA submits a new, higher sell offer to the auction

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ReferencesReferences

• M. Wooldrige. An Introduction to MultiAgent Systems, John Wiley&Sons, 2002, Ch.11, p.243-266.

• R. Guttman, A. Mokas, P. Maes. Agents as mediators in electronic commerce. In Intelligent Information Agents, M. Klush (Ed.), Springer Verlag 1999, p.131-152.

• P. Noriega, C. Sierra. Auctions and multi-agent systems. In Intelligent Information Agents, M. Klush (Ed.), Springer Verlag 1999, p.153-175.

• E. Durfee, e.a.. Strategic reasoning and adaptation in an information economy. In Intelligent Information Agents, M. Klush (Ed.), Springer Verlag 1999, p.176-203.

• W. Brenner, R. Zarnekov, H. Witting. Intelligent Software Agents, Springer Verlag, 1998, Ch.6, p.267-299.

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Agent systems referencesAgent systems references• BargainFinder - part of "Smart Store Virtual" by Anderson Consulting• Jango - Netbot Inc., Seattle, USA• PersonaLogic - Reordan, Soresen, 1995

Software Agents Group, MIT Media Labhttp://agents.media.mit.edu/projects/

• Kasbah - project of MIT Media Lab, Chaves, Maes, 1996• Tête-à-Tête - Guttman, Maes, 1998• Firefly - Shardanand, Maes, 1995

Firefly Networks (does not exist any more)AgentBuilder

• Auction Agents for the Electric Power Industryhttp://www.agentbuilder.com/Documentation/EPRI/index.html

• Fishmarket - Noriega, Sierra, 1997• UMDL - University of Michigan, Durfee e.a., 1997

• InfoSleuthhttp://www.argreenhouse.com/InfoSleuth/index.shtml

• Retsinahttp://www-2.cs.cmu.edu/~softagents/retsina_agent_arch.html

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