DRAFT: SUBMITTED FOR PUBLICATION (PAAM 98). PLEASE DON T EXCERPT, CITE OR QUOTE WITHOUT AUTHOR PERMISSION

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1 DRAFT: SUBMITTED FOR PUBLICATION (PAAM 98). PLEASE DON T EXCERPT, CITE OR QUOTE WITHOUT AUTHOR PERMISSION An agent system for comparative shopping at the point of sale Giorgos Zacharia, Alexandros Moukas, Robert Guttman and Pattie Maes Software Agents Group, E15-305, MIT Media Laboratory, 20 Ames Street, E15-305, Cambridge, MA {lysi,moux,guttman,pattie}@media.mit.edu Abstract In this paper we combine ideas of electronic commerce and mobile environments in agent-based transaction systems. Shopping for goods and services can be highly time-consuming. Although there is increasingly more information available via the Internet to make educated buying decisions, there are still computational limitations on gathering, filtering, and analyzing such data. This is especially the case at point-of-sale where consumers lack expedient resources to perform comparison shopping activities. In this paper we present a framework for agent based electronic commerce, we discuss how we applied this framework to the domains of classified ads and comparative shopping, and we present a set of experiments we performed on the behavior of the agents and the feasibility of our underlying architecture. 1. Introduction Shopping activities require a large effort from a user and include searching for parties interested in selling or buying what the user wants to buy or sell (e.g., by sifting through catalogs, advertisements in newspapers and television, shelves in stores, etc.), comparing prices and other features of the good or service to help make an optimal purchase decision, and exchanging currency for a product through some agreed upon, and ideally secure, channels. Electronic commerce - in particular, the buying and selling of goods, financial vehicles, and services on the Internet - has so far fallen short of its potential of redefining the marketplace. Reasons for this include consumer buying habits, security and trust concerns, and uncertain market models. So far electronic markets and physical markets have been totally separated. In this paper, we introduce a multiagent marketplace system, PDA@shop 1, which incorporates a layer of functionality that merges the virtual and the physical marketplaces for the benefit of the shopper. The user can use the PDA-comparative shopping system to obtain alternative prices, as well as reputation ratings of the potential online seller. When the user finds himself in a physical vendor s store he can use his PDA to communicate with the Kasbah electronic marketplace 1. PDA stands for Personal Digital Assistant.

2 [Chavez96] and create a buying agent. When the user creates a new broker-agent, he/she must set several parameters that the agent will use when it negotiates with the different selling agents. PDAs have several limitations that are relevant to resources required for comparison shopping, namely -- bandwidth, processing speed, and sustainable network connectivity. The goal of PDA@shop is to provide consumers with robust point-of-sale comparison shopping capabilities. Our prototype uses a commercial PDA with an intermittent network connection and the ability to spawn agents. These semi-intelligent shopping programs visit online marketplaces and acquire price offers for a set of user-requested items. In PDA@shop, using the experience gained from our Kasbah [Chavez96] agent marketplace project on matchmaking and brokering, we designed APIs for agent / marketplace negotiations. In this paper we explain these APIs and discuss the advantages and the disadvantages of our agent-based system for comparison shopping using a PDA as the communication device. The goal of the Kasbah system [Chavez96] is to help realize a fundamental transformation in the way people transact goods - from requiring constant monitoring and effort, to a system where a software agent does most of the work on the user s behalf. A user wanting to buy or sell a good creates an agent, gives it some strategic direction and sends it off to the agent market place. The Kasbah agents pro-actively seek out potential buyers or sellers and negotiate with them on their creator's behalf. The agent's goal is to make the best deal possible, subject to a set of user-specified constraints, such as a desired price, a highest (or lowest) acceptable price, a date to sell (or buy) by, as well as the lowest acceptable reputation of the potential buyer or seller. The Kasbah project is investigating the application of various AI techniques for building agents which are intelligent enough to perform well in a complex, dynamic marketplace. A key feature of Kasbah is that it is open to adding new types of agents, using different selling strategies. The aim of the PDA@shop project is to build a domain-independent system, which uses minimum domain-specific knowledge. The system uses a Personal Digital Assistant (PDA) -- in our system we are using US Robotics Pilot Pro [PalmPilot] -- as an interface communicating with a Kasbah Server for the back-end computational work in order to facilitate search for product types, their names and prices, as well as company names and related reputation quotes. The user can use his/her PDA while looking on a particular product in a shop, or a product advertisement, or even while browsing the web, in order to retrieve relevant information about the specific model and the producing company, as well as alternative brands available in the market and their respective prices and specifications. Thus the user increases his/her options, and can choose between the price offered in the physical market place or the alternative one on the electronic marketplace. Our current system performs two basic functions: It processes queries for creating customized find agents for specific products (e.g. used Introduction to AI books, by Patrick H. Winston, sold by people within the MIT district) that monitor the market conditions and report to the user whether there is something available that satisfies his/her requirements. The user can also dispatch buying agents that can actually engage in negotiations with potential sellers and give updates about their activity to the user. The agent can give more information on the particular brand retrieving available reviews

3 (like data from consumer reports), specifications and other data about the product and the vendor. The agent can proposes alternative similar products that will possibly match better with the user s different criteria, like prize availability, reputation and locality. The main contribution of PDA@shop with respect to our previous version of the Kasbah system is that: We add merchants into the picture, as they have selling agents, just like the end users. we are introducing a different type of buying agent which has no starting price, but rather a maximum price and asks the selling agents for lower bids. We make additional information, like the reputation of an individual and his/her locality, available to help him/her make the purchase decision. We are introducing PDAs as an interface to the whole thing. 2. Related work A quick search on the Internet will reveal a host of Web sites where people can find, buy, and sell goods [BF] [Fido] [UCE] [OnSale]. However, none of these sites use autonomous agents to negotiate on behalf of their owners. The closest system to ours we are aware of is MAGMA [Tsvetovatyy96], although there are significant differences. For example, MAGMA attempts to completely model the real world, representing things such as banks, accounts, currency, rainchecks, etc. Our approach is to add only as much infrastructure as necessary for agents to negotiate and complete transactions. We believe this allows our system to be integrated into already existing transaction-based systems such as real vendors and financial institutions. Autonomous and intelligent agents have been actively researched for many years. The predominant goal of this work is to help people manage information and work overload [Maes95] [Bradshaw97]. Early work in Distributed AI, focused on multi-agent collaboration to help solve general problems [Demazeau90]. The field of Decentralized AI shifted this focus slightly to the interactions among agents with different motivations. The underlying concept, however, was still to further some organizational goals [Demazeau90]. Agent cooperation is also seen in today's Web-based systems such as Firefly [Shardanand95], where agents cooperate and exchange information to recommend music to their owners. There has also been substantial research and analysis of market-based systems [Clearwater96] [Rosenschein94]. The typical approach is to model an optimization problem (e.g., resource allocation) for a marketplace consisting of multiple agents, each trying to maximize its own utility. This differs from the traditional AI approach mentioned above in that agents are now competing instead of cooperating. Modeling optimization problems as multiagent marketplace systems has been shown to be an effective technique. Examples of such systems include Enterprise [Malone88] and Challenger [Chavez97a], a system for allocating processing resources within a network of workstations, and Michael Wellman's WALRAS, an algorithm used to calculate competitive equilibria where agents submit single-good demand functions to market-clearing auctions [Mullen96]. Our multi-agent electronic marketplace is similar to these market-based systems in that it attempts to efficiently and optimally match buyers and sellers. However, our system operates at a different time-scale and needs to deal

4 with human factors not considered in more artificial systems. A multi-agent system implies agent communication. This is also an area which has been extensively researched. KQML is perhaps the most notable attempt to design a general-purpose agent language [Labrou94]. We chose not to use KQML, since our agents are all locally built and thus can be made to communicate via a pre-defined set of methods. We are currently working on more sophisticated agents which may require a richer semantics of communication to enable more complex and subtle negotiations. The field of speech acts has investigated such theoretical issues in depth [Winograd86]. Finally, if marketplaces are to be distributed, agents may need to be mobile in order to negotiate at the different sites. IBM has created Aglets, a mobile agent system [Aglets96] and General Magic has developed a Telescript language and explores several pertinent marketplace scenarios in their white paper [Telescript96]. As discussed in the previous section, we are now exploring the potential of distributed marketplaces and mobile commerce agents. 3. The Architecture The major components of the system are the PDA / agent template front end, the Kasbah Marketplace consisting of the registry service and the electronic marketplace, where the buying and the selling agents operate. In the classified ads scenario the behavior of a buying agent is as follows: The agent will try to sell the specified good. It will initially start by asking the desired price. If it is able to find someone willing to pay that price or more, then it makes a deal (at the highest price). Otherwise, the agent lowers its asking price and continues to negotiate. By the user-specified date to sell by, if the agent has not yet made a deal, it will have lowered its asking price to the lowest acceptable price. The way in which a selling agent lowers its price throughout the time variable is dependent upon its strategy. An anxious agent, in a big hurry to sell, will lower its price very quickly towards the lowest acceptable price. A greedy agent, on the other hand, will lower its price slowly, waiting until the very end of the day before lowering its price substantially. A cool-headed agent will strike a balance between these extreme strategies. In the comparative shopping scenario, the user creates an agent in order to find a better price than what has been offered to him/her so far. The agent finds all the possible vendors that are selling the goods that the user is interested in, contacts them and gets the lowest price. Consequently, the agent communicates again with all the selling agents, presents to them the best price it has found so far and requests to give a better offer. The agent iterates this behavior until the time allocated by the user is up. The PDA@shop system consists of three major networked components: the Kasbah Server where the marketplace runs and the agents meet and negotiate, the mail-server where the server who acts as the translator and the router of the messages exchanged between the Kasbah server and the PDA, and the PDA itself which is basically the interface to the whole system. The Kasbah server, which runs on an NT-server, is implemented in java 1.1 and uses a persistant database to store the agents of the system. The server, which runs on a unix workstation, uses a perl script to parse the s received by the PDA and submits them to

5 the Kasbah server through socket connections. The mail server is also used as the SMTP server from both the Kasbah server and the PDA, and as the POP3 server from the PDA for the messages exchange The Kasbah Server The Kasbah server consists of two major components: a) the front-end webserver, and b) the back-end database. Although the two components could be integrated to one, we chose to keep them separate for the sake of modularity, and extensibility. Therefore, the two components communicate with each other through socket calls, and they are not required to run on the same workstation. The front-end to the Kasbah server is a set of cgi-scripts running on an SSL protected webserver. The front-end provides the interface to the marketplace s activity. It allows the user to create an account, browse and search through the sellers or the buyers marketplace, and of course create, monitor, and terminate buying, selling, or find agents. The back-end is a java database on which the two market places (namely the buying and the selling marketplaces) are built, where the various agents reside and negotiate. The back-end also stores the user data, and logs the marketplaces activity for statistical analyses. For security reasons, the back-end allows connections only from a predefined set of IP addresses Buying Agents The comparative shopping agent is a special instance of a more general class of buying agents. A buying agent can be used both for buying goods from vendors and buying goods from end customers. The buying agents have the following set of parameters that define and constrain their behavior. Description of the good to buy. This includes a detailed description including category, condition, type, model, etc. (examples can be found in Section 3.3). Anxious Agent Cool-headed Agent Frugal Agent Figure 1. Depictions of buying agents strategies of negotiating price over time. Minimum price: desired price to buy for, the price for which the user ideally would like to buy the good, i.e. the initial price which the agent will offer. Note that a comparative shopping agent does not have a lower bound, since its aim is to find the lowest possible price. A generic buying agent though, must have a minimum price because it constitutes the start of its negotiation strategy.

6 Maximum Price: this is the highest price for which the user would ultimately be willing to buy the good. Best price: the best price offered by a vendor so far. This is not useful to a generic buying agent since it makes deals with the first selling agent within its price range. Time constraints: The timespan of an agent s life can vary from a few minutes for immediate feedback in the comparative shopping scenario to a much greater amount of time (for instance three months) if the user is not interested in buying the product instantly but instead wants an agent that will search the market for a period of time and return to him the best deals. Agent strategy: this allows the user to specify the negotiating behavior of their agent. There were three options to choose from: anxious, cool-headed, and frugal [Figure 1.] How often the agent should report to its owner about its activity. Condition of the product to be bought: this is a variable chosen among a specified list of options. For example if you are trying to buy a CD the condition categories are Seal/Mint, Used but not damaged, Payable but slightly damaged, Good to own, but not to play. The user may chose multiple categories, for each agent. Minimum reputation of counterpart: this allows the user to specify the minimum acceptable reputation sellers. The allowed choices are: Horrible, Difficult, Average, Good and Great. Locality: this option allows the user to specify his locality with respect to the MIT campus. Given that our current system is open only to the MIT community we allow only two options, either on campus or Anywhere Selling Agents The vendors can create their Selling agents by providing the following parameters: Description of the product. Time constraints. Just like the case of the buying agents, we user has to Initial Price. This price is returned to the Buying agents as the initial offer. Lowest acceptable price. Negotiation strategy: the vendor can choose one of the three strategies provided by the system. The curves of these three strategies look like the buying agents strategy curves, turned upside down. How often the agent should report to its owner about its activity. Condition of the product to be sold. The selling agents are allowed to belong to only one condition category (for example a particular CD cannot belong to the categories Seal/Mint and Payable but slightly damaged at the same time Minimum reputation of buyer.

7 Locality: Either on campus or off campus Both the buying and the selling agents are hierarchically indexed and categorized with respect to alphabetical order, category or condition of the product in interest. The hierarchical structure of the vendors directory speeds up the search process of the broker agents and facilitates the visualization by the user of the global electronic market. Thus the user can actually browse through the directory tree, and once he/she finds some interesting item being sold, he can create a buying or comparison shopping agent for it on the fly. Likewise if he/she finds buying agent that tries to buy a product that the user would like to sell, he can create a selling agent for it, from the current page Find Agents Users may also create find agents that will monitor the market for specific products, within customizable timespan and price domains. The find agents report to their owners the availability of the supply or the demand of their product of interest and the current best price in the market, without engaging in any negotiation process with any other agents. The Kasbah system supports two kinds of find agents, one for each of the two marketplaces. The find agents of the buying-agents marketplace monitor the status of the buying agents (so they are essentially instances of selling agents). Likewise the find agents of the selling-agents marketplace, monitor the status of the selling agents Comparative-Shopping Agents The comparative shopping agents can in fact be instances of either a buying or find agent. However, the user of the PDA@shop can not stay in the physical market place, waiting for days until his agents get him a better price. Thus, the comparative shopping agents have a very limited lifespan (of the order of 5 to 10 minutes). During such a short lifespan it is rather unlikely that some selling agent will lower its price significantly. Therefore, incorporating customizable shopping agent strategies would not make any significant difference in the shopping agents performance, (the price offered by the different selling agents will be roughly constant through the whole lifetime of the comparative shopping agent). Therefore the user does not define any strategy for his/her comparative shopping agent. Since we are getting rid of the agent strategy, and the user of the PDA@shop has already an offered price, he/she only has to submit the best price offered so far (e.g., the labeled price of the particular CD). Thereafter the comparative shopping agent keeps track of the best-price-so-far which is equivalent with the maximum price in an instance of a generic buying agent. Table 1 illustrates the similarities and differences between the generic buying agent, a find agent of the selling and the comparative shopping agent. The comparative shopping agent is very much like a find agent. Their most significant difference is that the find agents report to the user all the selling offers that satisfy his/her requirements, while the comparative shopping agent ranks all the offers and reports only the one with the best-price-so-far.

8 Table 1: Generic Buying and Comparative Shopping Agents Attribute Generic Buying Agent Find Buying Agent Comparative Shopping Agent Description X X X Minimum Price X Maximum Price X X X Best Price so far X Time Constraints X X X Strategy X Reputation X X X Locality X X X 3.6. PDA Front-End The shopper has the ability to generate new comparative shopping agents, examine the status of deployed agents, view the results of completed agents operations and purge agents that are not useful to him/her any more. A typical interaction scenario with the user includes the completion of an agent generation form on the PDA. The PDA software subsequently sends a command to kasbah@kasbah.media.mit.edu for generating an agent in the Kasbah marketplace. Since the front-end of the Kasbah system is just an SSL web interface, the user accesses his/ her account through any computer with network connection and a suitable web-browser. The comparative shopping at the point-of-sale is targeted for Personal Digital Assistants (PDA), equipped with a CDMA radio modem, or just a serial modem, that provides connection to the Internet (the PDAs we are using can establish PPP connections and have their own IP addresses). However, the quite compact size of the screen of a PDA, and its quite often flaky connectivity make the prospect of using it as a portable web interface really cumbersome. Even worse, the currently available web-browsers for PDAs are in a rather primitive stage, with no support for text-input forms. Therefore we need a compact interface, that can fit in the small screen of a PDA, that should not require a stable connectivity to the network. Therefore, unlike the case of the web-based interface of the Kasbah front-end, we decided to avoid using a continuous direct socket communication protocol. We also decided to avoid using the Hotsync TM process, to exchange data between the PDA and the Kasbah system for three significant reasons:

9 The HotSync process requires the PDA to synchronize its data with the desktop of the user s computer, therefore in case the desktop is down, the system becomes unoperational, The Hotsync process would involve the timing overhead of requiring the desktop to synchronize its data with the PDA before exchanging them with the back-end. The user would either have to manually disable the synchronizing of the other applications of the PDA, or subject himself to the additional delay due to the synchronizing of the data of the other applications. Instead we chose to use an the SMTP protocol to submit specially-formatted messages directly to the Kasbah server. In our current system, the front-end server is also the server of the system. The PDA application sends all the commands to the same address (in our case kasbah@kasbah.media.mit.edu) where it is processed. The incoming messages from the PDA are processed by a perl-script that checks their authenticity and parses them to extract the commands that need to be submitted to the backend of the system. Thus we assign most of the required computation work to the server of the system and we minimize the computation required by the mobile device. We also minimize the required connectivity time, since the user can simply disconnect his PDA from the network for as long as he wants, and connect again later to check for messages from the Kasbah system about his/her pending queries. Since the SMTP protocol seems to be the most appropriate for our system, we currently investigate the option of using Motorola s FLEX TM Pager Card [PageCard] Broker / marketplace service The marketplace provides a useful brokering service to the agents which inhabit it. By providing this service, the marketplace frees the agents from having to do this additional work. This brokering service works as follows. A buying agent can ask the marketplace to find other selling agents in the marketplace that fulfill the criteria imposed by the user. After identifying possible matches the Kasbah broker informs the agent, which in turn starts communicating with them. In order for the brokering service to be efficient it must incorporate a complete and easily accessible description of all the goods it has information about Communication Schemes The process of comparative shopping procedure is broken down in four major steps: ASK/ OFFER/ACCEPT/COMMIT The first step is that of the customer decides to create a comparative shopping agent. If the customer is located in a physical market place observing the product of interest and the offered price by the physical selling agent, using his PDA he/she can fill out an agent creation form, that will submit the ASK request. Since the communication between the user and the Kasbah Server is committed through formatted requests, a user can even create his Buying agents from his personal workstation or just any computer connected to the network. This way,

10 the user can perform comparative shopping between electronic marketplaces and printed product catalogs. The user ASKs for comparable product information by submitting a request for the creation of the comparative shopping agent. When the comparative shopping agent is created, it establishes the communication process with the selling agents on the selling agents marketplace. The communication process is initiated when the marketplace matches the comparative shopping agent with a number of selling agents that can give an OFFER for the product of interest. The comparative shopping agents who are ASKed for a comparable product, reply with an offer including the price and the relevant data describing their product. Once the eligible selling agents return their initial offers the comparative shopping agents chooses the lowest price offered for the different categories of the products that match the ASKed request. During the communication process the electronic marketplace agents compete on an equal basis with the physical marketplace offers. The matching algorithm of the marketplace allows some fuzziness in the item description with respect to both the keyword order and the keyword choice and spelling. Often the ASK request will match more than one item, genre or condition category. We are facing this situation because the user is allowed to leave unspecified some categories that describe the product, or even create multiple agents under several categories, for the same product. Thus, we are achieving a market equilibrium with product differentiation, enforced by the variety of the sellers participating in the system. The comparative shopping agent compares the various OFFERs and saves the lowest one from the different matching product categories. Then the communication process enters a loop that iterates until the comparative shopping agent runs out of the time it was allocated by the customer. The loop is performed by presenting the lowest offer so far, to all the initially eligible comparative shopping and asking them for new offers, and broadcasting the query to the whole marketplace so that it inquires the newcomers as well. When the time allocated to the comparative shopping agent is exhausted the agent reports the best OFFER it could get from the Selling agents. The representative of a physical marketplace can participate in this iteration process by making lower offers, given the other responses the customer has received so far. The comparative shopping agent proposes the brokered alternatives to the physical market OFFER and the customer evaluates the available options and chooses the OFFER that suits him/her best. If the chosen OFFER comes from the electronic marketplace, the user issues an ACCEPT message for the chosen OFFER and the comparative shopping agent proceeds to COM- MIT the transaction by dispatching a price offer that meets the one currently demanded by the particular selling so that the two agents make an agreement.

11 actual path of Agent messages Mail Server PDA Price Description Create Comparative Shopping Agent Comparative Shopping Agent Initial Offer Physical Marketplace Min Offer Iteration Request for comparable products Selling Agents Online Online Marketplaces Marketplaces Figure 2. An Overview of the Architecture 4. Results The system has been recently released to the MIT Media Laboratory community for beta testing and we plan to release it to whole MIT community before the beginning of the Spring term. This will be our real world experiment that will provide us with useful results for evaluation of the system. We have performed a first set of tests, where we built merchant agents with fixed prices, (we can do this by creating a selling agent with the same initial offer and the minimum). Our tests show the user total of the interaction with the PDA and the waiting time remains below our ad hoc threshold of 5 minutes. We still need to gather more data in order to investigate how much the users can actually trust the system, and thus, how useful it can be in merging the online with the physical marketplace.

12 5. Conclusions and Future Work We created an electronic marketplace where agents buy and sell goods and negotiate with interested parties on behalf of their users. Our PDA-based system integrates the physical market place with the electronic one. Thus we are increasing the available options for the consumer. In general, we see such agent marketplaces as helping reduce transaction costs associated with certain types of transactions, where currently financial, time, and trust issues can impede negotiations and commerce. User testing of such agents has revealed that the level of intelligence or sophistication which a buying/selling agent could ultimately demonstrate is limited more by user-agent trust issues than by limitations of AI technology. In particular, in order for these agents to be widely accepted, it is crucial that the agent's behavior can easily be understood and controlled by the user. Our future work will focus on security issues, and ways to enhance user trust. We have to address the security concerns of the vendors that choose to participate in this centralized marketplace and want to run their selling agents on our servers. The vendors need to be confident that their bargaining strategies are not revealed to their competitors or to the customers themselves. The vendors that choose the distributed solution and want to run their Selling Agents on their own sites maybe concerned about the communication protocol security. Finally we want to investigate how the customers trust towards their comparative shopping agents will evolve through longer use of the system. Our experiments raise a lot of issues about the implications of this type of marketplace for our society and economy. While the experiments were limited to a very small set of goods, one could envision setting up similar marketplaces for all consumer goods as well as for services. It is still an open issue what the effects of such wide deployment would be. One could argue that this type of market will approach the concept of an ideal market more so than our current markets resulting in a more efficient and effective economy. Another potential effect may be that more types of goods will be bought and sold because it is easier in this kind of system to find a buyer/seller for a good, even if it's a good that very few parties might be interested in. For example, we hope that the wider deployment of this technology will encourage people to buy and sell more second-hand goods, thereby supporting the reuse of objects in our society. Finally, it is an open issue what happens to current brokers in this future scenario, since an important role of brokers, namely to bring buyers and sellers together, is automated by this agent system. 6. References [Aglets96] Aglets Workbench. [BF] BargainFinder. [Bradshaw97] J. Bradshaw. Software Agents. MIT Press, Cambridge, MA, [Chavez96] A. Chavez and P. Maes. Kasbah: An Agent Marketplace for Buying and Selling Goods. Proceedings of PAAM'96, pp London, UK, April [Chavez97a] A. Chavez, A. Moukas, and P. Maes. Challenger: A Multi-agent System for

13 Distributed Resource Allocation. Proceedings of the First International Conference on Autonomous Agents (Agents 97). Marina del Ray, California, February [Chavez97b] A. Chavez, D. Dreilinger, R. Guttman, and P. Maes. A Real-Life Experiment in Creating an Agent Marketplace. Proceedings of PAAM 97. London, UK, April [Clearwater96] S. Clearwater. Market-Based Control: A Paradigm for Distributed Resource Allocation. World Scientific Publishing, Singapore, [Demazeau90] J. Demazeau and J. Muller. Decentralized Artificial Intelligence. Elsevier Science Publishers, North Holland, [Fido] FIDO: the Shopping Doggie! [Friedman93] D. Friedman and J. Rust, editors. The Double Auction Market: Institutions, Theories, and Evidence. Addison-Wesley, New York, [Labrou94] Y. Labrou and T. Finin. A Semantics Approach For KQML: A General Purpose Communication Language for Software Agents. Proceedings of CIKM 94, New York, [Maes95] P. Maes. Intelligent Software. Scientific American, Vol. 273, No. 3, pp Scientific American, Inc., September [Malone88] Malone, T.W., Fikes, R.E., Grant, K.R., and Howard, M.T. \Enterprise: A Market-like Task Scheduler for Distributed Computing Environments" In: The Ecology of Computation. Ed. Huberman, B.A. Elsevier, Holland.. [Milgrom89] P. Milgrom. Auctions and Bidding: A Primer. Journal of Economic Perspectives, pp Summer [OnSale] OnSale: Live Online Auction House. [PalmPilot] PalmPilot: [PageCard] PageCard: [Rosenschein94] J. Rosenschein and G. Zlotkin. Rules of Encounter: Designing Conventions for Automated Negotiation among Computers. MIT Press, Cambridge, MA, [Shardanand95] U. Shardanand and P. Maes. Social Information Filtering: Algorithms for Automating Word of Mouth. Proceedings of CHI'95. Denver, CO, [Telescript96] Telescript Technology: The Foundation for the Electronic Marketplace. / [Tsvetovatyy96] M. Tsvetovatyy and M. Gini. Toward a Virtual Marketplace: Architectures and Strategies. Proceedings of PAAM'96, pp London, UK, April [Winograd86] T. Winograd and F. Flores. Understanding computers and cognition: A New Foundation for Design. Addison-Wesley, Reading, MA, [UCE] United Computer Exchange.

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