1,119 research outputs found

    Using fuzzy set approach in multi-attribute automated auctions

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    This paper designs a novel fuzzy attributes and competition based bidding strategy (FAC-Bid), in which the final best bid is calculated on the basis of the assessment of multiple attributes of the goods and the competition for the goods in the market. The assessment of attributes adapts the fuzzy sets technique to handle uncertainty of the bidding process. The bidding strategy also uses and determines competition in the market (based on the two factors i.e. no. of the bidders participating and the total time elapsed for an auction) using Mamdani's Direct Method. Then the final price of the best bid will be determined based on the assessed attributes and the competition in the market using fuzzy reasoning technique

    Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

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    The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms ā€“ a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

    Auctions and Electronic Markets

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    KEMNAD: A Knowledge Engineering Methodology for Negotiating Agent Development

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    Automated negotiation is widely applied in various domains. However, the development of such systems is a complex knowledge and software engineering task. So, a methodology there will be helpful. Unfortunately, none of existing methodologies can offer sufficient, detailed support for such system development. To remove this limitation, this paper develops a new methodology made up of: (1) a generic framework (architectural pattern) for the main task, and (2) a library of modular and reusable design pattern (templates) of subtasks. Thus, it is much easier to build a negotiating agent by assembling these standardised components rather than reinventing the wheel each time. Moreover, since these patterns are identified from a wide variety of existing negotiating agents(especially high impact ones), they can also improve the quality of the final systems developed. In addition, our methodology reveals what types of domain knowledge need to be input into the negotiating agents. This in turn provides a basis for developing techniques to acquire the domain knowledge from human users. This is important because negotiation agents act faithfully on the behalf of their human users and thus the relevant domain knowledge must be acquired from the human users. Finally, our methodology is validated with one high impact system

    Automated Negotiations Under Uncertain Preferences

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    Automated Negotiation is an emerging field of electronic markets and multi-agent system research. Market engineers are faced in this connection with computational as well as economic issues, such as individual rationality and incentive compatibility. Most literature is focused on autonomous agents and negotiation protocols regarding these issues. However, common protocols show two deficiencies: (1) neglected consideration of agentsā€™ incentives to strive for social welfare, (2) underemphasised acknowledgement that agents build their decision upon preference information delivered by human principals. Since human beings make use of heuristics for preference elicitation, their preferences are subject to informational uncertainty. The contribution of this paper is the proposition of a research agenda that aims at overcoming these research deficiencies. Our research agenda draws theoretically and methodologically on auctions, iterative bargaining, and fuzzy set theory. We complement our agenda with simulation-based preliminary results regarding differences in the application of auctions and iterative bargaining

    Rational bidding using reinforcement learning: an application in automated resource allocation

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    The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms ā€“ one centralized and one decentralized

    Fuzzy Logic Based Negotiation in E-Commerce

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    The evolution of multi-agent system (MAS) presents new challenges in computer science and software engineering. A particularly challenging problem is the design of various forms of interaction among agents. Interaction may be aimed at enabling agents to coordinate their activities, cooperate to reach common objectives, or exchange resources to better achieve their individual objectives. This thesis is dealing with negotiation in e-commerce: a process through which multiple self-interested agents can reach agreement over the exchange of scarce resources. In particular, we present a fuzzy logic-based negotiation approach to automate multi-issue bilateral negotiation in e-marketplaces. In such frameworks issues to negotiate on can be multiple, interrelated, and may not be fixed in advance. Therefore, we use fuzzy inference system to model relations among issues and to allow agents express their preferences on them. We focus on settings where agents have limited or uncertain information, ruling them out from making optimal decisions. Since agents make decisions based on particular underlying reasons, namely their interests, beliefs then applying logic (by using fuzzy logic) over these reasons can enable agents to refine their decisions and consequently reach better agreements. I refer to this form of negotiation as: Fuzzy logic based negotiation in e-commerce. The contributions of the thesis begin with the use of fuzzy logic to design a reasoning model through which negotiation tactics and strategy are expressed throughout the process of negotiation. Then, an exploration of the differences between this approach and the more traditional bargaining-based approaches is presented. Strategic issues are then explored and a methodology for designing negotiation strategies is developed. Finally, the applicability of the framework is simulated using MATLAB toolbox

    Addressing stability issues in mediated complex contract negotiations for constraint-based, non-monotonic utility spaces

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    Negotiating contracts with multiple interdependent issues may yield non- monotonic, highly uncorrelated preference spaces for the participating agents. These scenarios are specially challenging because the complexity of the agentsā€™ utility functions makes traditional negotiation mechanisms not applicable. There is a number of recent research lines addressing complex negotiations in uncorrelated utility spaces. However, most of them focus on overcoming the problems imposed by the complexity of the scenario, without analyzing the potential consequences of the strategic behavior of the negotiating agents in the models they propose. Analyzing the dynamics of the negotiation process when agents with different strategies interact is necessary to apply these models to real, competitive environments. Specially problematic are high price of anarchy situations, which imply that individual rationality drives the agents towards strategies which yield low individual and social welfares. In scenarios involving highly uncorrelated utility spaces, ā€œlow social welfareā€ usually means that the negotiations fail, and therefore high price of anarchy situations should be avoided in the negotiation mechanisms. In our previous work, we proposed an auction-based negotiation model designed for negotiations about complex contracts when highly uncorrelated, constraint-based utility spaces are involved. This paper performs a strategy analysis of this model, revealing that the approach raises stability concerns, leading to situations with a high (or even infinite) price of anarchy. In addition, a set of techniques to solve this problem are proposed, and an experimental evaluation is performed to validate the adequacy of the proposed approaches to improve the strategic stability of the negotiation process. Finally, incentive-compatibility of the model is studied.Spain. Ministerio de EducaciĆ³n y Ciencia (grant TIN2008-06739-C04-04

    A model of preference elicitation: The case of distributed resource allocation

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    Market mechanisms are deemed promising for distributed resource allocation settings by explicitly involving users into the allocation process. The market considers the usersā€™ and providersā€™ valuations to generate efficient resource allocations and prices. In theory, valuations are assumed to be known to the user. In practice, however, this is not the case. It is a complex burden for both users and providers to assess their true valuation for a certain combination of resources and services and to efficiently communicate this valuation to the market. This paper contributes to the theory of designing distributed allocation models in that (i) we propose a model for preference elicitation, which allows users and providers to assess their valuations as a function of their resource requirements and strategic considerations, (ii) we show how this model can be encoded within so-called bidding agents which interact with the market on behalf of the user, and (iii) we evaluate our approach in a numerical experiment to illustrate how the bidding agent adapts to the dynamic market situation. As this evaluation shows, the model outperforms technical schedulers and can thus be used for decision support in electronic markets
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