R. Schwartz and S. Kraus. Bidding mechanisms for data allocation in multi-agent environments. In Agent Theories, Architectures, and Languages, pages 61--75, 1997.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....base competence assessment. We use a very simple measure comparing individual with global distribution of cases; we do not try to assess the aeras of competence of (individual) case bases as proposed by Smyth and McKenna [13] This work focuses on finding groups of cases that are competent. In [12] Schwartz and Kraus discuss negotiation protocols for data allocation. They propose two protocols, the sequential protocol, and the simultaneous protocol. These two protocols can be compared respectively to our Token Passing Case Bartering Protocol and Simultaneous Case Bartering Protocol, ....

R. Schwartz and S. Kraus. Bidding mechanisms for data allocation in multi-agent environments. In Agent Theories, Architectures, and Languages, pages 61--75, 1997.

....There has been extensive work on negotiation in multiagent systems, based on the initial idea of contract nets, due to Smith and Davis [93] In this paradigm, an agent seeking a service invites bids from other agents, and selects the bid that most closely matches its own. Schwartz and Kraus [87] present a model of agent decision making where one agent invites bids (this is an action ) and others evaluate the bids (another action) and respond; this kind of behavior is encodable through agent programs together with underlying data structures. This body of work is complementary to 56 ....

.... to 56 ours: an agent negotiates by taking certain actions in accordance with its negotiation strategy, while we provide the hooks to include such actions within our framework, but do not explicitly study how the negotiation actions are performed, as this has been well done by others [93, 87]. Coalition formation mechanisms where agents dynamically team up with other agents has been intensely studied by many researchers [88, 84, 106] Determining which agents to team with is a sort of decision making capability. Inverno et al. 29] present a framework for dMARS based on the BDI ....

R. Schwartz and S. Kraus. Bidding Mechanisms for Data Allocation in Multi-Agent Environments. In Wooldridge and Jennings [107], pages 56--70.

....must be addressed, including how to determine the value of resources to participants and how to conduct the auction as a distributed protocol [12] These are some of the questions we address for the specific domain of reliable data replication in this paper. Other distributed computing systems [26, 22, 13, 11] have used market oriented principles (such as auctions) in order to allocate resources. Our work differs from these previous systems in several ways. First, most systems have a concept of money distinct from the resources that are being bought and sold. In our system, there is no concept of ....

....added, and archives, which must 23 make copies as soon as possible to avoid failures, cannot wait until all resources and bids are known. Several systems have attempted to apply market oriented programming, and specifically auction techniques, to resource allocation problems. Schwartz and Kraus [26] survey methods for using auctions to distribute data collections. They assume that there is a common currency, that there is one copy of each collection, and that the performance metric is access time. Some or all of these assumptions are shared by computational economies such as the Blue Skies ....

R. Schwartz and S. Kraus. Bidding mechanisms for data allocation in multi-agent environments. In Proc. Int. Workshop on Agent Theories, Architectures and Languages, July 1997.

....that monitor user interests and changing news events and create personalized news reports. They have also been used extensively to make trades in financial markets for different users as stock conditions change. They have been used to dynamically bid for a variety of clients in online auctions [Schwartz and Kraus, 1997] . There are many impressive agent systems these include Internet Softbot [Etzioni and Weld, 1994] Retzina [Decker et al. 1997] Infosleuth [Bayardo et al. 1997] SIMS [Arens et al. 1993] and many others. There have been important efforts to optimize the performance of such agents. For ....

R. Schwartz and S. Kraus. Bidding Mechanisms for Data Allocation in Multi-Agent Environments. In Intl. Workshop on Agent Theories, Architectures, and Languages, pages 56--70, Providence, RI, 1997.

....There has been extensive work on negotiation in multiagent systems, based on the initial idea of contract nets, due to Smith and Davis [93] In this paradigm, an agent seeking a service invites bids from other agents, and selects the bid that most closely matches its own. Schwartz and Kraus [87] present a model of agent decision making where one agent invites bids (this is an action ) and others evaluate the bids (another action) and respond; this kind of behavior is encodable through agent programs together with underlying data structures. This body of work is complementary to ours: an ....

.... agent negotiates by taking certain actions in accordance with its negotiation strategy, while we provide the hooks to include such actions within our framework, but do not explicitly study how the negotiation actions are performed, as this has been well done INFSYS RR 1843 98 02 55 by others [93, 87]. Coalition formation mechanisms where agents dynamically team up with other agents has been intensely studied by many researchers [88, 84, 106] Determining which agents to team with is a sort of decision making capability. Inverno et al. 29] present a framework for dMARS based on the BDI model. ....

R. Schwartz and S. Kraus. Bidding Mechanisms for Data Allocation in Multi-Agent Environments. In Wooldridge and Jennings [107], pages 56--70.

....There has been extensive work on negotiation in multiagent systems, based on the initial idea of contract nets, due to Smith and Davis [99] In this paradigm, an agent seeking a service invites bids from other agents, and selects the bid that most closely matches its own. Schwartz and Kraus [93] present a model of agent decision making where one agent invites bids (this is an action ) and others evaluate the bids (another action) and respond; this kind of behavior is encodable through agent programs together with underlying data structures. This body of work is complementary to ours: an ....

.... complementary to ours: an agent negotiates by taking certain actions in accordance with its negotiation strategy, while we provide the hooks to include such actions within our framework, but do not explicitly study how the negotiation actions are performed, as this has been well done by others [99, 93]. Coalition formation mechanisms where agents dynamically team up with other agents has been intensely studied by many researchers [94, 91, 111] Determining which agents to team with is a sort of decision making capability. Inverno et al. 58] present a framework for dMARS based on the BDI model. ....

R. Schwartz and S. Kraus. Bidding Mechanisms for Data Allocation in Multi-Agent Environments, In : Proc. 1997 Intl. Workshop on Agent Theories, Architectures, and Languages, Providence, RI, pp 56--70, 1997. IFIG RR 9802 103

....that we consider, the servers are selfmotivated, have no common preferences and no central controller. In addition, a server is concerned with the data stored locally, but does not have preferences concerning the exact location of data stored in remote servers. Following our previous work [16], we suggest using a bidding mechanism in order to decide about the data allocation. 1 According to our approach, the location of each data unit will be determined using a bidding mechanism, where the server bidding the higher price for obtaining the data will actually obtain it. 2 We show in ....

....we suggest using a bidding mechanism in order to decide about the data allocation. 1 According to our approach, the location of each data unit will be determined using a bidding mechanism, where the server bidding the higher price for obtaining the data will actually obtain it. 2 We show in [16] that this approach yields an efficient and fair solution, its implementation is simple, and the bidders are motivated to offer efficient prices. The utility of a server from storing a dataset strongly depends on its usage by clients from different geographic areas. However, the servers are ....

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R. Schwartz and S. Kraus. Bidding mechanisms for data allocation in multi-agent environments. In M. P. Singh, A. S. Rao, and M. J. Wooldridge, editors, Intelligent Agents IV, LNAI, volume 1365, pages 61--75. Springer-Verlag, 1998.

....C per time period, due to the negotiation process. Example 3.1 The following utility functions can be used in environments that we consider. 5 There are cases when clients are autonomous and send their queries directly to server j, which has the requested documents. For such cases, we suggest in [53] the use of a bidding mechanism for data allocation. 6 For details, see Section 4. 7 Constant discount ration: Consider a server i 2 SERV ERS. For alloc 2 OFFERS and t 2 Time, U i (alloc; t) 1 Gamma p) t Delta U i (alloc; 0) Gamma t Delta C, where C 0 is the constant negotiation cost ....

....We found that the hillclimbing algorithm achieves the best results and that the backtracking algorithm on subproblems can be used when a deterministic algorithm is needed. We also checked the influence of various parameters on the results. 6.3 Bidding v.s. Alternating Offers Negotiation In [53], we considered a different variation of the data allocation problem. In the model described in [53] each server is concerned with the data stored locally, but does not have preferences concerning the exact storage location of data stored in remote servers. This situation occurs, for example, ....

[Article contains additional citation context not shown here]

R. Schwartz and S. Kraus. Bidding mechanisms for data allocation in multi-agent environments. In Munindar P. Singh, Anand S. Rao, and Michael J. Wooldridge, editors, Intelligent Agents IV: Agent Theories, Architectures, and Languages, pages 61--75. Springer-Verlag, 1998.

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