A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of 16th International Joint Conference on Artificial Intelligence, volume 1, pages 506--511, August 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....settings, because sellers retain a degree of monopoly power over their information. 2.2. Applications to multiagent systems There has been much interest in the design and development of multiagent systems comprised of economically motivated software agents [22] Primitive multiagent economies [8] shed light into future forms of online shopping and multiagent, market based, solutions are tested in fields ranging from train scheduling to temperature control with the goal of achieving better resource allocation than traditional solutions [15] 7] A common problem arises in many ....

Amy R. Greenwald and Jeffrey O. Kephart. Shopbots and pricebots. In Proceedings of the 16th International Joint Conference on Artificial Intelligence, 1999.

....loss of generality, be discarded. add an amplifying mechanism to the orthogonal strategy. As the amplifying mechanism we use the derivative follower with adaptive step size (ADF) Henceforth we will call this the orthogonal DF. The derivative follower (DF) is a local search algorithm (cf. [8, 7, 10]) It adjust the variable price pv found by the orthogonal strategy by either subtracting or adding # to it, where # is called the step size of the DF. Consequently also the fixed price pf changes because the adjusted o#er still needs to generate the same utility level (specified by the concession ....

A. R. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, volume 1, Stockholm, Augusts 1999.

....may have different strategies for selecting the seller, ranging from random to the selection of the cheapest seller on the market (bargain hunters) while the sellers use the same pricing strategy. A similar model but with populations of sellers using different strategies has been studied in [14]. The pricing problem can be viewed as a very simple instance of our topic selection task (namely, as a single topic case with some modifications to the performance model) 6 Conclusions and Future Work The successful deployment of heterogeneous Web search environments will require that ....

Greenwald, A., Kephart, J.: Shopbots and pricebots. In: Proc. of the 16th Intl. Joint Conf. on AI. (1999) 506--511

....have written a survey article aimed at understanding collective interactions among agents that dynamically price services or goods [12] They discuss and compare several pricing strategies. Examples of price wars caused by agents that dynamically price their information bundles are described in [11]. The data used for the experiments are not real data, but are generated synthetically making some economic assumptions and using random distributions. Because of the complexity in analyzing experimental results, experiments are limited to two agents. Understanding collective interactions among ....

J. O. Kephart and A. R. Greenwald. Shopbots and pricebots. In A. Moukas, C. Sierra, and F. Ygge, editors, Agent Mediated Electronic Commerce II, volume LNAI1788. Springer-Verlag, 2000.

....may have different strategies for selecting the seller, ranging from random to the selection of the cheapest seller on the market (bargain hunters) while the sellers use the same pricing strategy. A similar model but with populations of sellers using different strategies has been studied in [14, 15, 16] The pricing problem can be viewed as a very simple instance of our topic selection task (namely, as a single topic case with some modifications to the performance model) 7 Conclusions and Future Work Heterogeneous search environments provide access to arguably much larger volumes of ....

A. R. Greenwald and J. O. Kephart, "Shopbots and pricebots, " in Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99-Vol1) (D. Thomas, ed.), (S.F.), pp. 506--511, Morgan Kaufmann Publishers, July 31--Aug. 6 1999.

.... the solution to hard optimization problems, e.g. scheduling problems, by the computational agent [34] In fact, electronic markets may make the valuation problem more difficult, because of mitigating factors such as decreased aggregation, increased product differentiation, and increased dynamics [2, 13, 15]. Just as careful market design can reduce the complexity of the bidding problem, for example by providing incentives that make truthful bidding a A preliminary version of this paper appeared in the Proc. of the IJCAI 99 Workshop on Agent Mediated Electronic Commerce. Kluwer Academic ....

Greenwald, A. and J. O. Kephart: 1999, `Shopbots and Pricebots'. In: Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99). pp. 506--511.

....We believe that our work strikes a balance between resource considerations and customers value for the content. To the best of our knowledge there has been very little research on online price discovering algorithms [11, 12] Most research has been focussed on agent based market economies [3, 6, 9]. 7. CONCLUSIONS AND FUTURE WORK We have developed an analytical framework for pricing of ondemand content. The framework models customer behavior as well as resource constraints. Based on this framework, we have developed an algorithm that suggests prices to the content provider. We have ....

A. Greenwald and J. Kephart. Shopbots and pricebots. In Sixteenth International Joint Conference on Artificial Intelligence, August 1999.

....can be applied to the decision making process used by crawlers capable of exchanging information by assuming that mutual cooperation can yield the greatest benefit. Nash equilibria are especially useful for dealing with cooperative crawlers that mutually exchange information with one another[19]. Mutual cooperation can a#ect the entire system of crawlers. If a set of strategies are being used to exchange information, a Nash Equilibrium occurs when no single crawler can change their A version of the Prisoner s Dilemma can be read at http: williamking. ....

Amy R. Greenwald and Je#rey O. Kephart. Shopbots and Pricebots. In Agent Mediated Electronic Commerce (IJCAI Workshop), pages 1--23, 1999.

....proxy bid programs cannot participate in multiple auction sites. One of the most famous agents is ShopBot[2] ShopBot helps users to find desired shops or goods from the Internet. The main function of ShopBot is to find a web site or a description of goods based on the user s preference. Greenwald[3] analyzed a future situation in which there were many ShopBots and consequently proposed PriceBot in order to enable sellers to price dynamically. However, ShopBot cannot make a bid in multiple auctions. ShopBot mainly retrieves information from shop sites. In contrast, we focus on bidding support ....

....proposed PriceBot in order to enable sellers to price dynamically. However, ShopBot cannot make a bid in multiple auctions. ShopBot mainly retrieves information from shop sites. In contrast, we focus on bidding support agents that can make bids in multiple auctions. Furthermore, while Greenwald s[3] analysis is based on using a lot of information gathering agents, i.e. ShopBot, our analysis is based on using a lot of bidding support agents. This paper consists of four sections. In Section 2, we formalize an electronic commerce model in which people trade their goods via multiple auction ....

Greenwald, R. R., and Kephart., J. O., "Shopbots and pricebots", in Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 506--511, 1999.

....systems are also likely to reduce the size of the marketplace and to introduce bias, as it is di#cult to obtain a su#cient number of ratings for every existing vendor, and to control the reliability of the sources. Alternatively, the search process could be further automated and generalized [7, 4]. As early as 1995, comparison shopping agents (also known as shopping bots) were proposed as a solution to find a product under the best terms (where price was typically the most important feature) among di#erent vendor sites. A shopping agent queries multiple sites on behalf of a shopper to ....

AR Greenwald and JO Kephart. Shopbots and pricebots. In Proc. 16th Intl. Joint Conference on Artificial Intelligence, pages 506--511, 1999.

....The effects of shopbots and similar software agents in electronic markets have been studied using several approaches. In microeconomic theory, simple models of the relationship between information of price, utility and consumer choice are applied. Based upon these models, Greenwald and Kephart [5] have made predictions of increased efficiency in electronic markets as a result of shopbot use. Their predictions correspond well to the analytic predictions of Bakos [2] Another approach is the application of software agents with a cognitive architecture consisting of beliefs, preferences and ....

Greenwald, A.R., & Kephart, J.O. Shopbots and pricebots. Proceedings of the International Joint Conference on Artificial Intelligence, Stockholm, July 31 - August 6, 1999.

.... sell the good it is interested in, picks out ones that o er prices less than its valuation, and then selects a seller at random from this lot (this is the same as picking a seller at random and then transacting if the price o ered by seller is less than the price buyer s valuation, as suggested in [8] (since both the events are unrelated) 2. Bargain Hunter: buyer checks the price of all sellers having prices lower than its valuation, determines the seller with the lowest price, and then purchases the good (This type of buyer agents corresponds to people who take advantage of shopbots) The ....

A.R. Greenwald and J.O. Kephart. Shopbots and Pricebots, Proceeding of the First ACM Conference on Electronic Commerce. ACM press, November 1999.

....price in the market at time t, and, t = p t ( BRS S ) b s if the seller is not charging the minimum price in the market. The expression in parentheses in the above equations represent the expected number of times a seller is selected by the buyers during the time interval t. In [5, 6], Greenwald and Kephart view the price setting problem as a one shot game, and provide a detailed game theoretic analysis of the shopbot economy showing that although there is no pure strategy Nash equilibrium, there exists a symmetric mixed strategy Nash equilibrium. Greenwald and Kephart also ....

A. R. Greenwald and J. O. Kephart. Shopbots and pricebots. In Lecture Notes on Artificial Intelligence: Proceedings of the IJCAI Workshop on Agent-mediated Electronic Commerce. Springer-Verlag, 2000.

.... good in multiple auctions (Ito, Fukuta, Shintani, Sycara, 2000; Anthony, Hall, Dang, Jennings, Preist, Bartolini, Phillips, 2001) Outside of, but related to, the auction scenario, automatic shopping and pricing agents for internet commerce have been studied within a simplified model (Greenwald Kephart, 1999). Twenty two agents from 6 countries entered TAC, 12 of which qualified to compete in the semi finals and finals in Boston. The designs of these agents were motivated by a wide variety of research interests including machine learning, artificial life, experimental economics, real time systems, ....

Greenwald, A., & Kephart, J. O. (1999). Shopbots and pricebots. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 506--511.

....are likely to reduce the size of the marketplace and to introduce bias, as it is di#cult to obtain a su#cient number of ratings for every existing vendor, and to control the reliability of the sources. Finally, a fourth general approach is to further automate and generalize the search process [11, 7]. As early as 1995, shopping agents (also referred to as comparison shopping agents) were proposed as a solution to find a product under the best terms (where price was the most important feature early on) among di#erent e commerce sites. A shopping agent queries multiple sites on behalf of a ....

A. R. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proc. 16th Intl. Joint Conference on Artificial Intelligence, pages 506--511, 1999.

....of PriceBots and ShopBots, intelligent agents which can be programmed to implement a particular strategy for a retailer or consumer. Kephart and his colleagues have also studied the role of reinforcement learning, information filtering, and information bundling in an e commerce environment [16]. Intelligent agents technology has also been used for supply chain management and planning and scheduling problems. Sesh Murthy, Richard Goodwin, Pinar Keskonoak, and their colleagues have examined the difficult problem of scheduling multiple machines when there are multiple objectives and ....

A. Greenwald and J. Kephart. Shopbots and Pricebots. In Thomas Dean, editor, Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 506--511. Morgan Kaufmann, 1999.

.... on fishmarket auctions [Rodrguez Aguilar et al. 2000] Automatic bidding agents have also been created in this domain [Gimenez Funes, 1998] Outside of, but related to, the auction scenario, automatic shopping and pricing agents for internet commerce have been studied within a simplified model [Greenwald and Kephart, 1999] . FAucS addresses a much more complex scenario than has been previously studied with autonomous bidding agents: the FCC spectrum auctions. Spectrum auctions have been analyzed retrospectively [Weber, 1996; Cramton, 1997] but little is known about them from a theoretical perspective. As ....

Amy Greenwald and Jeffrey O. Kephart. Shopbots and pricebots. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.

.... following definitions of these terms: A shopbot is a software agent attached to a single user and has the ability to query multiple servers on the network (on behalf of the user) to gather information about prices and other service characteristics, like service quality or expected waiting time [6]. We assume that shopbot and user interests are identical and the shopbot s sole purpose is to serve the user s needs. A pricebot is a software agent attached to a single service provider and has the ability to dynamically change the price of the product service to maximize the provider s ....

Amy Greenwald and Jeffrey O. Kephart. Shopbots and pricebots. IJCAI-99, 1999.

....that an agent solves a hard optimization problem, or interacts with a busy and expensive human expert. In fact, electronic markets may make the valuation problem more difficult, because of mitigating factors such as decreased aggregation, increased product differentiation, and increased dynamics [1, 4, 5]. In this paper we compare auction performance for agents that have hard local problems, and uncertain values for goods. Just as careful market design can reduce the complexity of the bidding problem, for example by providing incentives for agents to reveal their true value for a good [28] ....

Greenwald, A., and Kephart, J. O. 1999. Shopbots and pricebots. In Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99), 506--511.

.... of the supply chain [16, 33, 37, 45] Moreover, in the business to customer area, agent based technologies are exploited in the development of complex systems where agents search for products and services on behalf of a user, compare the solutions offered by different providers, and so forth [19, 22]. In addition to the above issues, agent based technologies can be successfully applied to the enhancement of other features of electronic commerce systems, among which the adaptability of the interfaces to the users needs: the popularity of Web shopping is increasing and very different types of ....

A.R. Greenwald, , and J.O. Kephart. Shopbots and pricebots. In Proc. 16th IJCAI, pages 506--511, Stockholm, 1999.

....) given to sellers 1 and 2 respectively. It is assumed that both sellers have full knowledge of the expected consumer response for any given price pair, and in fact have full knowledge of both utility functions. This work builds on prior research reported in (Tesauro and Kephart, 1998; Tesauro and Kephart, 1999). Those papers examined the effect of including foresight, i.e. an ability to anticipate longer term consequences of an agent s current action. Two different algorithms for agent foresight were presented: i) a generalization of the minimax search procedure in two player zero sum games; ii) a ....

....The second advantage of Q learning is that the solutions should correspond to deep lookahead: in principle, the Q function represents the expected reward looking infinitely far ahead in time, exponentially weighted by a discount parameter 0 fl 1. In contrast, the prior work of (Tesauro and Kephart, 1999) was based on shallow finite lookahead. Finally, in comparison to directly modeling agent policies, the Q function approach seems more extensible to the situation of very large economies with many competing sellers. Approximating Q functions with nonlinear function approximators such as neural ....

[Article contains additional citation context not shown here]

A. Greenwald and J. O. Kephart, "Shopbots and pricebots." To appear in Proceedings of IJCAI '99 (International Joint Conferences on Artificial Intelligence) , July 31- August 6, 1999, Stockholm, Sweden.

....at: www.research.ibm.com infoecon researchpapers.html. extent that the categories overlap, there can be direct price competition, and to the extent that they differ, there are asymmetries that again lead to the potential for cyclic price wars. The third model is the Shopbot model described in (Greenwald and Kephart, 1999), which models the situation on the Internet in which some consumers use a shopbot to compare prices of all sellers offering a given product, and select the lowest priced seller. In this model, the sellers products are identical, and their profit functions are symmetric. Myoptimal pricing leads ....

A. Greenwald and J. O. Kephart, "Shopbots and pricebots. " Proceedings of IJCAI-99, 506-511, 1999.

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A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of 16th International Joint Conference on Artificial Intelligence, volume 1, pages 506--511, August 1999.

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A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Arti cial Intelligence, volume 1, pages 506-511, August 1999.

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A.R. Greenwald and J.O. Kephart. Shopbots and pricebots. In Agent-mediated Electronic Commerce II, volume 1788 of Lecture Notes on Arti cial Intelligence. Springer-Verlag, 2000.

....about a book s price can be about 6 to 10 as valuable as the book itself. Keywords shopbots, brand, price dispersion, information value 1. INTRODUCTION Shopbots comparison shopping web sites that collate information on products from multiple vendors can be a very valuable tool for buyers [1, 8]. Typically, they permit buyers to sort product and vendor information along desired dimensions, such as price, delivery time, or vendor reputation. The most sophisticated shopbots even provide personalized rankings that take into account an individual buyer s product and vendor preferences. ....

A. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proceedings of IJCAI '99, 1999.

....is constrained; likewise, his behavior is taken to be boundedly rational. Assume the mayor sets a fee according to his beliefs about the population of Santa Fe, and updates the fee weekly according to the trend in utility. One simple algorithm for adjusting the fee is derivative following [5], which experiments with incremental changes, continuing to move in the same direction until average utility decreases, at which point the direction of movement is reversed: given increment 0, the fee f t 1 = f t [sign(f t f t 1 )sign(u t u t 1 ) where u t is the agents average utility ....

A.R. Greenwald and J.O. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, volume 1, pages 506--511, August 1999.

....paper is concerned with the dynamics of interaction among pricebot algorithms. Ultimately, this line of research aims to identify those pricebot algorithms that are most likely to be pro table, from both an individual and a collective standpoint. Recently, a simple model of shopbots and pricebots [7] was introduced, and a variety of (mostly deterministic) pricing algorithms were simulated [8] Motivated in part by a gametheoretic analysis of this model which yields only mixedstrategy Nash equilibria, this paper explores the use of probabilistic pricing based on no regret learning [4, 5] in ....

....Section 6 is the concluding section, in which we discuss the pro tability of deterministic pricing algorithms in comparison with no regret learning in both high and low information settings. 2. MODEL In this section, we present a summary of the model of shopbots and pricebots; for details, see [7]. Consider an economy in which there is a single homogeneous good that is o ered for sale by S sellers and of interest to B buyers, with B S. Each buyer b generates purchase orders at random times, with rate b , while each seller s reconsiders (and potentially resets) its price ps at random ....

[Article contains additional citation context not shown here]

A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Articial Intelligence, volume 1, pages 506-511, August 1999.

....in the short term, up until the moment when some other seller resets its parameters. The myopic best response strategy, sometimes referred to as the myoptimal strategy, is attractively simple to describe and implement, and has been studied in several other models of software agent markets [22, 27, 35, 20]. In order to study the behavior of a market in which the sellers use an asynchronous, myoptimal strategy, we simulate its evolution from a given initial condition. The simulation proceeds as follows. At each discrete time step, a buyer or seller is randomly selected to act. If the selected agent ....

....7, r and ae are reduced from 0.2 to 0. 125, the sellers bundle compositions cycle irregularly through (112) 121) 211) 102) 111) 101) and (011) Cyclical price wars have been observed previously in a variety of models of agent economies in which the agents employ a myoptimal strategy [22, 35, 20]. The more complex cycles observed here, which involve bundle composition as well, are reminiscent of behavior seen previously in studies of a related information filtering model that included information categories [27, 26] 8 Cyclical price and bundle composition wars are symptomatic of an ....

A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of 16th International Joint Conference on Artificial Intelligence, volume 1, pages 506--511, August 1999.

....we rederive Eq. 3 of Section 3, which describes the probability h s ( p; w) that buyers choose seller s. As in Section 3, h s ( p; w) S X i=0 w i h s;i ( p) 27) 13 The case of small, discrete price sets is considered for a related shopbot model in Appendix A of Greenwald and Kephart [12]. In that case it is possible to obtain pure strategy Nash equilibria. shopbot economics.tex; 6 10 2000; 18:30; p.35 36 Je rey O. Kephart and Amy R. Greenwald where h s;i ( p) denotes the demand for seller s by buyers of type i. Since we are now assuming pure rather than mixed strategies, it is ....

Greenwald, A. and J. Kephart: 2000, `Shopbots and Pricebots'. In: A. Moukas, C. Sierra, and F. Ygge (eds.): Lecture Notes on Articial Intelligence: Proceedings of the IJCAI Workshop on Agent-mediated Electronic Commerce. Springer-Verlag. To Appear.

.... of type i with probability S 1 i 1 = S i = i=S, and seller s is the lowest 2 If w1 = 1, then the unique Nash equilibrium is such that all sellers charge the monopoly price v; if w1 = 0, then the unique Nash equilibrium is such that all sellers charge the competitive price r (see [10, 11]) 3 The authors are currently investigating the conditions under which asymmetric Nash equilibria dynamically arise in this model. shopbot economics.tex; 6 10 2000; 18:30; p.5 6 Je rey O. Kephart and Amy R. Greenwald priced among the i sellers selected with probability [1 F (p) i 1 , since ....

....are chosen from a suciently large (or continuous) set, and (ii) 0 w 1 1. 13 If w 1 = 1, then the unique Nash equilibrium is such that all sellers charge the monopoly price v, while if w 1 = 0, then the unique Nash equilibrium is such that all sellers charge the competitive price r (see [10, 11]) Our proof proceeds by contradiction: we assume the existence of a pure strategy Nash equilibrium, derive the unique form of such an equilibrium, and then argue that a strategy pro le of this form is not individually optimizing for all sellers. Assuming the existence of a pure strategy Nash ....

Greenwald, A. and J. Kephart: 1999, `Shopbots and Pricebots'. In: Proceedings of Sixteenth International Joint Conference on Articial Intelligence, Vol. 1. pp. 506-511.

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A. Greenwald and J. O. Kephart, "Shopbots and pricebots." To appear in: Proceedings of IJCAI-99, 1999.

....implications and directions for future research. 1. Introduction Within the next few years, we expect electronic commerce to be an important multi agent domain in which reinforcement learning will find numerous applications. One such application is automated dynamic pricing by software agents (Greenwald Kephart, 1999). Suppose that each seller agent individually attempts to maximize profits through judicious setting of prices and other product parameters. Even if the seller agents do not communicate with one another directly, market forces may strongly couple their actions, resulting in a highly dynamic ....

....were asymmetric even though the two agents were identical in every respect, including their payoffs. In this paper, we delve into the nature and behavior of simultaneous Q learning by two interacting agents. As a vehicle for our study, we focus on the shopbot model originally introduced in (Greenwald Kephart, 1999). In any given training run, we had observed (Tesauro Kephart, 1999) that either of two solutions could be obtained: symmetric and stable, or asymmetric and pseudo convergent. We also found that the asymmetric solution was increasingly favored as the discount parameter fl was increased. Having ....

[Article contains additional citation context not shown here]

Greenwald, A., & Kephart, J. O. (1999). Shopbots and pricebots. Proceedings of Sixteenth International Joint Conference on Artificial Intelligence Greenwald, A. R., Kephart, J. O., & Tesauro, G. J. (1999). Strategic pricebot dynamics. Proceedings of the First ACM Conference on Electronic Commerce ACM Press.

.... informational and computational demands: game theoretic pricing (GT) myoptimal pricing (MY) derivative following (DF) and no regret learning (NR) Previously, we studied the dynamics that ensue when shopbot assisted buyers interact with pricebots utilizing only a subset of these strategies [19, 25, 29]. In this work, we simulate additional, more sophisticated, pricebot strategies, and nd that the game theoretic equilibrium can arise dynamically as the outcome of adaptive learning in our model of shopbots and pricebots. This paper is organized as follows. The next section presents our model of ....

A.R. Greenwald and J.O. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Articial Intelligence, volume 1, pages 506-511, August 1999.

....vs. time for five different pricing structures using the amoeba algorithm. Figure 9 shows the time averaged profit extracted by a monopolist seller that uses amoeba to learn the optimal setting of its parameters. The five curves represent various pricing structures ranging in complexity from 1 to 10 parameters. Although the nonlinear pricing structure with 10 parameters yields the highest profit asymptotically, it takes much longer to learn than the simpler pricing structures. If the time scale on which the market changes is shorter than the amount of time it takes the amoeba to conduct 1000 ....

....algorithm. Figure 9 shows the time averaged profit extracted by a monopolist seller that uses amoeba to learn the optimal setting of its parameters. The five curves represent various pricing structures ranging in complexity from 1 to 10 parameters. Although the nonlinear pricing structure with 10 parameters yields the highest profit asymptotically, it takes much longer to learn than the simpler pricing structures. If the time scale on which the market changes is shorter than the amount of time it takes the amoeba to conduct 1000 or more evaluations, the two part tariff scheme may be ....

[Article contains additional citation context not shown here]

A. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, August 1999.

....arising from such behavior rather than properties of convergence. The convergence of asynchronous machine learning algorithms, however, where players moves are determined by discrete random processes, have been investigated recently in e commerce pricing models by Greenwald and Kephart [25]. Finally, regarding theoretical computer science, the algorithms which we refer to as satisfying no regret optimality criteria, are also often described as achieving reasonable competitive ratios. Borodin and El Yaniv [6] present a thorough discussion of the competitive analysis of on line ....

A.R. Greenwald and J.O. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Articial Intelligence, To Appear, August 1999.

.... informational and computational demands: game theoretic pricing (GT) myoptimal pricing (MY) derivative following (DF) and no regret learning (NR) Previously, we studied the dynamics that ensue when shopbot assisted buyers interact with pricebots utilizing only a subset of these strategies [19, 25, 29]. In this work, we simulate additional, more sophisticated, pricebot strategies, and find that the game theoretic equilibrium can arise dynamically as the outcome of adaptive learning in our model of shopbots and pricebots. This paper is organized as follows. The next section presents our model ....

A.R. Greenwald and J.O. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, volume 1, pages 506--511, August 1999.

.... The cumulative distribution function F (p) is computed in terms of the quantity h s (p; w) 2 If w1 = 1, then the unique Nash equilibrium is such that all sellers charge the monopoly price v; if w1 = 0, then the unique Nash equilibrium is such that all sellers charge the competitive price r (see [8, 9]) Recall that h s (p; w) represents the probability that buyers select seller s as their potential seller. This function is expressed in terms of the probabilistic demand for seller s by buyers of type i, namely h s;i (p) for 0 i S. The first component h s;0 (p) 0. Consider the next ....

.... pure strategy Nash equilibrium in our model of shopbot economics whenever 0 w 1 1; if w 1 = 1, then the unique Nash equilibrium is such that all sellers charge the monopoly price v, if w 1 = 0, then the unique Nash equilibrium is such that all sellers charge price the competitive price r (see [8, 9]) The proof proceeds by contradiction: we assume the existence of a pure strategy Nash equilibrium and we derive the unique form of such an equilibrium, but we argue that a strategy profile of this form is not in fact an equilibrium. Assuming the existence of a pure strategy Nash equilibrium, we ....

A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, volume 1, pages 506--511, August 1999.

.... and computational needs: game theoretic pricing (GT) myoptimal pricing (MY) derivative following (DF) and Q learning (Q) Previously, we studied the price and profit dynamics that ensue when shopbotassisted buyers interact with homogeneous collections of pricebots that utilize these algorithms [9, 10, 15]. In this work, we first establish that our previous results are not significantly altered when buyers valuations are inhomogeneous rather than identical. Later, we examine the behavior that ensues when pricebots employing different strategies are pitted against one another. This paper is ....

....that seller s price is less than the buyer s valuation v b . Price ties are broken randomly. A few special cases are worth mentioning. A buyer of type i = 0 simply opts out of the market without checking any prices. Buyers of types i = 1, i = 2, and i = S have been referred to in previous work [9, 10] as employing the Any Seller, Compare Pair and Bargain Hunter strategies, respectively; the latter corresponds to buyers who take advantage of shopbots. 1 The buyer population is assumed to consist of a mixture of buyers employing one or another of these strategies. Specifically, a fixed, ....

[Article contains additional citation context not shown here]

A. Greenwald and J. Kephart. Shopbots and pricebots. In Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, To Appear, August 1999.

No context found.

A. R. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proceedings of the 16th International Joint Conference on Artificial Intelligence , 1999.

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Greenwald, A. and J. O. Kephart: 1999, `Shopbots and Pricebots'. In: Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99). pp. 506--511.

No context found.

A. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99), pages 506--511, 1999.

No context found.

A. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99), pages 506--511, 1999.

No context found.

Amy Greenwald and Jeffrey O Kephart. Shopbots and pricebots. In Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI-99).

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Amy R. Greenwald, and Jeffrey O. Kephart, Shopbots and Pricebots, Proceedings of Sixteenth International Joint Conference on Artificial Intelligence, Stockholm, Sweden, August 1999 58

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Amy R. Greenwald and Je#rey O. Kephart. Shopbots and pricebots. In Agent Mediated Electronic Commerce (IJCAI Workshop), pages 1--23, 1999.

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A. R. Greenwald and J. O. Kephart. Shopbots and pricebots. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 506--511. Morgan Kaufmann Publishers Inc., 1999.

No context found.

Greenwald AR, Kepgart JO. Shopbots and pricebots. Proc 16th Int Conference on Artificial Intelligence, 1999, p 506--511.

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Greenwald, A., and Kephart, J. O. 1999. Shopbots and pricebots. In Proc. 16th International Joint ConferenceonArti#cial Intelligence #IJCAI-99#, 506#511.

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Greenwald, A., and Kephart, J. O. 1999. Shopbots and Pricebots. In Proc. 16th International Joint ConferenceonArti#cial Intelligence #IJCAI-99#.

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