Chapter 14B

Chapter 14B

Agents in Electronic Commerce

Stand 7.2.01

- 2 - Prof. Dr. Michael M. Richter and Sascha Schmitt, Universität Kaiserslautern

Recommended References (I)

• P. Braun: Würmer, Spinnen und Agenten. Electronic Commerce der Zukunft. Vortrag im Rahmen der Ringvorlesung „Elektronischer Geschäftsverkehr“, Universität Jena, Institut für Informatik, 1999. Zu finden unter http://www.minet.uni-jena.de/~braunpet

• A. Chavez, P. Maes: Kasbah: An agent marketplace for buying and selling goods. In: Proc. of the 1st Conference on Practical Applications of MAS, PAAM-96, London, 1996.

• U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth: From Data Mining To Knowledge Discovery: An Overview. In: U. M. Fayyad, G. Piatetsky-Shapi-ro, P. Smyth, R. Uthurusamy (eds.). Advances In Knowledge Discovery And Data Mining, AAAI Press/The MIT Press, Menlo Park, CA., S. 1-34, 1996.

• S. Franklin, A. Graesser: Is it an agent, or just a program?: A taxonomy for autonomous agents. In: Proc. of the 3rd Internat. Workshop on Agent Theories, Architectures and Languages, ATAI-96. Springer, LNAI 1996.

• R. Guttman, P. Maes: Cooperative vs. Competitive Multi-Agent Negotiations in Retail Electronic Commerce. In: Proc. of the 2nd International Workshop on Cooperative Information Agents, CIA’98, Paris, France, July 3-8, 1998.

• R. Guttman, A. Moukas, P. Maes: Agent-mediated Electronic Commerce: A Survey. In: Knowledge Engineering Review, Volume 13:3, June 1998.


Recommended References (II)• M. Klusch, W. Benn: Intelligente Informationsagenten im Internet. In: KI - Zeitschrift für

Künstliche Intelligenz, Themenheft Intelligente Informationsagenten, Heft 3/98. • Y. Lallement, M. S. Fox: IntelliServe™: Automating Customer Service. In: AI for

Electronic Commerce, a AAAI-99 Workshop, Orlando, Florida, USA, July 18, 1998. Zu finden unter http://www.csee.umbc.edu/aiec/papers/fox.pdf

• P. Maes. Tutorial on Software Agents and the Future of Electronic Commerce. 1998. Zu finden unter http://pattie.www.media.mit.edu/people/pattie/ECOM/

• P. Maes, R. H. Guttman, A. G. Moukas. Agents that Buy and Sell: Transforming Commerce as we Know It. In: Communications of the ACM, Volume 42, No. 3, March 1999. Zu finden unter http://ecommerce.media.mit.edu/papers/cacm98.pdf


Agents: Overview• Agents and multi-agents play a major role in artificial

intelligent systems.• Agents can be viewed as software systems which perform

“intelligent actions” which are traditionally done by humans.

• Agents do not only make intelligent logical conclusions but they use thei abilities also to– take initiative– to communicate– have a certain responsibilty etc.

• We give an short introduction into aspects of multi-agent theory motivated by application in e-commerce.


Contents in Brief

• Agents in General– Motivation– The notion of an Agent– Classification 1st view– A closer look at the different Agent types

• Agents in Electronic Commerce– Classification 2nd view– The stages of the sales process and their support by Agents

• Conclusions and Future Work


Agents in General


Information Search on the Internet• Vast amount of information, electronic services, etc.

available but: essential problems– mass– distribution of semi-structured data on the web– diversity

• Missing: Web-based integration of different autonomous information systems on demand

Usage of technologies from AI & database systems for intelligent cooporation of loosely coupled information systems

User should have the possibility of comfortable, flexible, and transparent access on different databases


Notion “Agent”• Software Agent, Agent-based System, Multi Agent System

distinctly used in research areas:– Software Engineering– Distributed Systems– Distributed Artificial Intelligence

• No standardized definition• Use of the notion “Agent” as a metaphor:

– kind of (software) program; – its function and execution in an open, distributed environment is

similar to certain human properties like • autonomous, goal-directed behaviour, • social co-operation, • intelligence, and • learning.


Agent Deployment

• Tasks, knowledge, and abilities of Agents depend heavily on the application for which the Agents were developed– tools available from AgentSoft, IBM, etc.

• Current areas of deployment:– logistics and production planning– cash and stock management– decentralized organization of electricity networks– ...

• At present most attractive: intelligent information agents

active, knowledge-based search for relevant information


Intelligent Search for Information

• Many resources available in the Internet– hard to find what someone is looking for– hard to remember where someone has found something at

the beginning of his search

• Information services available for a search in heterogeneous, autonomous databases connected with each other, e.g., Gopher, WWW.

• But: hardly any active and intelligent support

special software agent:

information agents


What’s an Information Agent?

• At present, no uniform idea about IAs:– notion of “agent” interpreted distinctly– requirements on agents widely spread

• But: characteristic property exists:

Searches actively and autonomously for relevant information in a set of different available information sources

• Compare difference to intelligent database front ends


Classification for Agents - 1st ViewAutonomous Agents

Biological Agents Computational Agents Robotic Agents

Software Agents Artificial Life Agents

Viruses Task-Specific Agents Entertainment Agents

Information Agents

Non-Cooperative Cooperative

Adaptive Rational Mobile Adaptive Rational Mobile

[Franklin & Graesser, 1996]


Non-Cooperative IAs• Search actively for relevant information in different databases

on the Internet on behalf of their user– mostly based on individual user profiles

– using available Internet information services; especially search engines on WWW

• Index-based techniques of information retrieval• IA checks knowledge about relevant information on possible

changes and modifies it appropriately• Characteristic: IAs do not cooperate with each other to

complete their user’s search task• Prerequisite for success of search:

– extensive knowledge about structure and semantics of data in the different databases

– access rights to the databases

• Examples: software robots (softbots)


Non-Cooperative IAs: ExamplesSoftBots (most well-known)• SearchBots

– simple, index-based information search on the Web, e.g., Excite/Jango, Lycos, InfoSeek, HotBot, etc.

– meta search engines using selected SearchBots for query answering, e.g., MetaCrawler, SavvySearch, etc.

• ShopBots– offer electronic shopping of certain products to their user, e.g.,

BargainFinder, Fido, etc.

• Personal Assistants (Interface Agents)– observe the user during his work on the computer (information

search or news selection) and learn his preferences used as experiences for personal recommendations, e.g., WebWatcher, NewsWeeder, Letizia, Remembrance, etc.


Cooperative IAs (I)• Starting point: different, autonomous databases which are

connected amongst each other– each database is attached to an IA

– IA protected against unauthorized access from outside

• Many problems to solve because of– distribution and autonomy of the databases

– heterogeneity of the data in the databases

• Characteristics:– can work together goal-directed with other IAs if required

– “understands” its potential cooperation partners

– can negotiate intelligently the way of cooperation

• Prerequisites:– IA has enough knowledge

– IA has enough inference and cooperation abilities


Cooperative IAs (II)

• IAs can be considered as part of middleware for the development of distributed information systems because all application-dependent cooperation is done via Agents

• Database standards can be used: CORBA, JDBC, ODBC

• Problems for development:– How can an IA recognize semantic and/or structural

relationships between data and information in a set of different, autonomous databases?

– Up to what degree does the IA have to cooperate with other IAs?


Cooperative IAs: Examples (I)• Knowledge-based search for relevant information

• Technologies:

terminological knowledge representation, inferences

• Examples: InfoSleuth and RETSINA

InfoSleuth: contains Agents for different tasks– based on ontological representation of so-called resources, which are

mainly databases

– User Agent: intelligent interface to the system

– Task Execution Agent: generates an execution plan for user queries and coordinates a collection of pieces of information on a high abstract level

– Broker Agent: looks for adequate service providers (Agents) to execute the users‘ commands and mediates between them

– Resource Agent: interface to an information source (e.g., database) and provides access to this resource for retrieval and update services


Cooperative IAs: Examples (II)InfoSleuth (continued)• Each agent has its own application-specific and domain-

specific notions as a local ontology• Local ontology is made public to a global (central)

ontology during a registration process at the beginning of the agent‘s activities

• Administration of the central ontology is managed by a special resource agent: Ontology Agent

• Special properties of the semantically-controlled service broking:– intelligent forwarding of queries– dynamic resource binding– scalability


Cooperative IAs: Examples (III)RETSINA• Multi agent system: three kinds of cooperating agents• Interface Agents:

– the only access for users to the system

– help their users interactively to specify the tasks and return results

• Task Agents:– have comprehensive domain-specific knowledge

– each agent plans and controls the execution of tasks which it gets from one or more Interface Agents

– eventually decomposition of tasks to forward the sub-tasks to other Task Agents or directly to Information Agents

• Information Agent:– has direct access on one or more information sources

– can fulfil tasks/queries from the Task Agent once or periodically

– can check data in the information sources

– special type: MatchMaker Agents


Cooperative IAs: Examples (IV)RETSINA (continued)• Basic idea for Agent development:

reuseability of the (Java) program code

• Domain-dependent and independent control structures for communication, cooperation, scheduling, and monitoring are appropriately instantiated

• Examples for deployment:– WARREN: trading of shares and information on shares at

different stock markets– PLEIDES: appointment planning and office automation– THALES: satellite position and visibility prediction


Adaptive IAs (I)• Have the ability to autonomously adapt to changes in

their environment and requirements in their domain/tasks; e.g., changing relevance of information, classification of contents of added information sources

• Generally, learning mechanisms used to search for information more flexibly and user-friendly

• Distinguish:– learning of a single agent in a multi agent environment– adaptation of a multi agent system in a whole

• For development, special methods from the fields machine learning, adaptive information retrieval, learning in organizations


Adaptive IAs (II)

• Questions:– Under which conditions does a certain learning method work for a single

Agent in a multi agent environment? Or would it work better for the whole system?

– When can an Agent’s learning harm the system?

– Which methods are adequate for the adaptation of an Agent to changing environments, work loads etc. with respect to the execution of a set of tasks with limited resources or economically rational negotiations between Agents in an electronic market?

– How can Agents learn their behavior with respect to cooperation with other Agents on a basis of experiences already made? Which kind of knowledge and observations about the environment is essential?

– How can an Agent learn the semantics of data in different databases without harming the autonomy of these systems? In how far can methods from Data Mining and Knowledge Discovery in distributed and open environments of autonomous IAs help?


Adaptive IAs: Examples (I)

• Currently, more adaptive technologies from the area of Machine Learning (e.g., reinforcement learning, genetic algorithms, neural networks) used for non-cooperative IAs

• Only very few approaches for cooperative IAs

• Examples: AMALTHAEA and ARACHNID– both systems use genetic algorithms– the number of those agents providing relevant information

grows, the others die out


Adaptive IAs: Examples (II)AMALTHAEA• Set of two types of information agents

– Information Filtering Agents (IFA)– Information Discovery Agents (IDA)

• User sends his query to one or more IFAs– arbitrary text query– IFA generates a vector of numerically weighted key words

• Each IFA sends the query (i.e. the text vector) to IDAs, which try to find relevant documents on the WWW using search engines

• IFA selects the maximally relevant document among the returned results

user has to assess this document

IFA‘s success determined


Adaptive IAs: Examples (III)

AMALTHAEA (continued)• Evolutionary selection strategy known from genetic

algorithms and evolutionary programming (artificial life):– only those Agents that are most appropriate for the user can

survive and augment their abilities or proliferate them (e.g., by crossing and/or mutation): new IFAs will be generated

– less successful Agents will be removed from the system

• Simulations have shown that the success of such an evolutionary search process heavily depends on the number of IFA generations after that the users assesses the system


Adaptive IAs: Examples (IV)ARACHNID• Multi agent system similar to AMALTHAEA• Only one kind of Information Agent• Each user query will be identified with an Agent that will

process the query on its own (also using Information Retrieval methods on WWW search engines)

• Each relevant document raises the Agent`s „energy level“ and from a certain threshold on this Agent can create new Agents

• Waste of network resources (i.e. transmission of irrelevant documents) results in a loss of energy

• Agents with insufficient energy will die


Rational (Cooperative) Agents

• Is paid for its information services– advancing commercialization of the Internet

• During information search, Agents communicate rationally with each other: “information for money”

• Necessary negotiations can be handled centralized or decentralized under market-oriented principles and methods from operations decision theory; (Operations Research, Game Theory, AI, …)

• Adequate software technology for electronic commerce on the Internet


Rational Agents: Examples (I)KASBAH (http://kasbah.media.mit.edu)• A set of Information Agents that provide very simple benefit-oriented

information search in the Internet• All Agents have the same functionality

– offer a product for selling to other Agents or

– search for a product to buy for their user

• Sell and buy offers are negotiated in a „virtual market place“ kind of global black board

• Offering Agent reduces the price of the product by preceding time to encourage the Agents looking for the same kind of product to buy this product

• Searching Agent raises the price it is willing to pay by preceding time• All Agents have a maximum resp. minimum offer for an individually

defined period of time


Rational Agents: Examples (II)KASBAH (continued)• Trading is done as soon as both Agents accept their

offers, i.e. if the selling offer is not higher than the current maximum buying offer and the buying offer is not lower than the minimum selling offer

• Central market place plays an important and crucial role• All offers and changes are sent to the market place• All Agents are provided with the necessary information

about the other Agents• the order of notifications is essential for the Agents‘

decisions: they always take the first appropriate offer instead of an optimal one

(c.f. section „Negotiations“)


Rational Agents: Examples (III)FCSI• A set of Information Agents that search knowledge-based for relevant

information for their users and cooperate with each other on a benefit-oriented basis

• Each FCSI Agent is connected to one database

• All Agents have the same functionality

• Information search is based on terminological knowledge representation and inference

• Each Agent translates the conceptual database schema of its connected database into a terminological knowledge base

• User sends one or more search queries or tasks to an Agent; the Agent then tries to fulfill these tasks locally and cooperatively together with other Agents


Rational Agents: Examples (IV)

FCSI (continued)• Finding non-local pieces of information is based on a

mutual classification of parts of the local knowledge bases

• The Agents are paid for their services and cooperate individually rational in stable coalitions

• For the decentralized negotiations between the Agents, polynomial coalition algorithms based on concepts from cooperative game theory are used that are feasible for arbitrary environments


Mobile IAs (I)• Changing physically from one local information system to another

on the Internet• Standards in database systems and the necessity to access data

and programs in mobile systems (e.g., PDA = Personal Digital Assistant) any time will bring more importance to these systems

• But: tradeoff between costs and benefit– cost-effective load balancing in C/S networks and distributed systems

– cost models for transactions of huge data amounts

– query optimization in distributed database systems

• Solutions:– local processing of non-local data by migration of an adequate IA for

reduction of transfer and communication costs (but: migration of code and data brings up new problems, e.g., persistency)

– dynamical determination of IA’s mobility degree depending on the requirements


Mobile IAs (II)• Major problem: comparison of IA to viruses

question of security

• Current technologies: Java (platform independence)

• Advantages:– is personalized (knows its owner)– influence on the number of shops to be visited – can buy and pay the products immediately– works asynchronously

• Disadvantages:– security cannot be guaranteed– online shops have to offer appropriate interfaces


Agents in Electronic Commerce


Classification for Agents - 2nd View

• Categorization based on models from Consumer Buying Behavior (CBB), a marketing theory

• Six fundamental stages resp. processes of sales (see chapter 1)

categories for Agents:

1. Identification of Demands

2. Product Mediation

3. Dealer Mediation

4. Negotiation

5. Purchase and Delivery

6. Product Service and Evaluation


Identification of Demands• The buyer gets aware of an unsatisfied demand.• This stage stimulates the buyer by providing more

information• Agents play an important role for repetetive (e.g., re-

orderings, accessories) or predictable (e.g., because of customs) purchases

Notification Agents• Simple example: Monitors

– steadily checking a set of sensors or data streams and reacting with a certain action (e.g., sending of an e-mail) as soon as predefined conditions are fulfilled

– examples: • FastParts („AutoWatch“, http://www.fastparts.com/), • Amazon („Amazon bringt‘s“, http://www.amazon.de)


Product Mediation• In this stage, information is gained that helps with the

decision what should be bought• Product alternatives will be evaluated based on criteria

given by the buyer• Resulting in a set of possible products

Recommendation Agents• Recommendation is a prediction based on a user profile

and certain business rules (business intelligence; under certain conditions gained with Data Mining methods)

• Examples:– Amazon („people who bought this book also bought that one“)– FireFly (out of order?, http://www.firefly.com)– c.f. PersonaLogic approach (http://www.personalogic.com)


Dealer Mediation (I)• This stage combines the possible products with dealer-

specific information with the aim to help making the decision from whom one will buy

• Contains evaluation of dealer alternatives, also based on criteria given by the buyer (e.g., price, guarantee, availability)

Shopping Agents

• Comparing possibilities; extracting information about the searched products from different sites on the WWW


Dealer Mediation (II)• Problems:

– many shops do not like the idea that their products are compared for the price only

– adverts on the page are not considered– only a subset of the online shop is considered for comparison– how does the buyer know that he really has found the most

inexpensive offer?– confidence, security– heterogeneity and frequent changes make analysing the pages

difficult– for every single online shop the query and the interpretation of the

results have to be programmed

• Examples:– BargainFinder (Andersen Consulting/Accenture; out of order)– Jango (Excite; http://www.jango.com/)


Negotiation (I)• In the negotiation stage, it will be agreed on the aims of

the transaction• Markets exist without any degrees of liberty with

respect to, e.g., price• Markets exist where negotiation is an integral part of

the buying process (e.g., stock market)

Negotiation Agents• Meet in the scenario of a virtual market place or auction

(e.g., http://www.auktionsindex.de for an overview)• User initiates Agent with a strategy• Agents search pro-actively for potential buyers resp.

sellers and negotiate at their owners place with the aim to accomplish an acceptable deal


Negotiation (II)

• Auction types:– standard English auction: an auctioneer increases the price

stepwise; a bidder stays in the auction as long as he bids. The last bidder wins. Problems:

• raises the price• takes very long (in the online version)

– Dutch aution: an auctioneer fixes a maximum price (starting price) and lowers it by a fixed value at certain time intervals. The first bidder wins.

• Examples:– AuctionBot (U. of Michigan; http://auction.eecs.umich.edu)– MarketMaker (Kasbah, MIT; http://www.maker.media.mit.edu), – Tete-a-Tete, T@T (MIT; http://ecommerce.media.mit.edu/tete-a-tete/)– eBay (http://www.ebay.com)


Purchase and Delivery

• This stage either terminates the negotiation process or happens a little bit later

• In some cases, payment or delivery options can influence the product or dealer mediation process

• There is no direct support by Agents, however preconditions from this stage can influence or even determine the behavior of the Agents in other stages.


Product Service and Evaluation• This stage contains product and customer service as

well as an evaluation about the degree of satisfaction with the whole buying process and the buying decision

• The stages product and dealer mediation can contribute important aspects to the evaluation after the sales of the product

• At least, Agent-based approaches can be found– for users to exchange experiences and knowledge

• Examples:– to make knowledge usable about the dealers and the products,

e.g., Kasbah‘s Better Business Bureau, – IntelliServe (customer service; evaluated at Florist‘s Transworld

Delivery, http://www.ftd.com)


Conclusions and Future Work


Final Remarks (I)

• Development of intelligent IAs is a challenge for interdisciplinary research:– AI, Distributed AI– Database systems– Computer Supported Collaborative Work, CSCW – Human Computer Interaction, HCI

• Already one of the key technologies for Internet and Intranet applications

• Future will show which kind of the presented Agents will be most successful in the different kinds of applications


Final Remarks (II)• Interesting topics for future research:

– Reliability of Agents towards both their users and other Agents• presently, everything based on the assumption that Agents do

not show harmful behavior, e.g., Agents in KASBAH do not lie (which they could do to gain more benefit) or Agents do not reveal their users‘ private information (they could sell user profiles to other companies)

– Learning and self-organizing Agents in open, distributed environments

• dynamical and effective organization of multi agent systems through the Agents themselves as well as their adaptive behavior

– User-friendly interfaces• methods for comfortable and intuitive interaction between

Agent and user in real time

Documents

Chapter 14B