Multi-Agent Systems

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Multi-Agent Systems E-Commerce

• Martin Beer,
• School of Computing Management Sciences,
• Sheffield Hallam University, Sheffield,
• United Kingdom
• m.beer_at_shu.ac.uk

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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

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• 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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Consumer's buying behavior

• 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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• 1.3 Agents as mediators in eCommerce
• Most 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_at_T Market
• Need
• identification
• Product
• brokering
• Merchant
• brokering
• Negotiation
• Purchase
• and delivery
• Product
• service

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• (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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• 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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• 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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• Product brokering and merchant brokering agents
  use information filtering techniques
• content-based filtering, e.g. associative
  networks of keywords as in Jango
• constraint-based filtering, like in PersonaLogic,
  T_at_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 agents

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• 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 time

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

BA
SA
5 basic scenes BA buyer's admitter SA
seller's admitter BM buyer's manager SM
seller's manager Auct auctioner
Buyer's register
Goods' register
Goods show and auction
Auct
SM
BM
Sellers' settlements
Credits and goods delivery
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• 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 bots
• Main 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 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 Society implements a
  multi-agent information economy where agents buy
  and sell services from each other.

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1
Label
Info resources
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Query
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5
6
Match me with a buyer at a price
2
Match me with a seller at a price
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6
9
Query
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• 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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• References
• M. Wooldrige. An Introduction to MultiAgent
  Systems, John WileySons, 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 references
• BargainFinder - part of "Smart Store Virtual" by
  Anderson Consulting
• Jango - Netbot Inc., Seattle, USA
• PersonaLogic - Reordan, Soresen, 1995
• Software Agents Group, MIT Media Lab
• http//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 Industry
• http//www.agentbuilder.com/Documentation/EPRI/i
  ndex.html
• Fishmarket - Noriega, Sierra, 1997
• UMDL - University of Michigan, Durfee e.a., 1997
• InfoSleuth

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