Preference Ordering in Agenda Based Multi-issue Negotiation for Service Level Agreement

Abedin, Fahmida; Chao, Kuo‐Ming; Godwin, Nick; Arochena, Hisbel

doi:10.1109/waina.2009.168

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…Regarding the construction and evaluation of preference models, the essential difference with related works [2,4,8,23], see section 2, is that we adopt the point of view of the customer. Since the customer knows her real preferences, the construction and evaluation of these models can be performed by the interested party to check the efficacy of the strategies evaluated in this paper.…”

Section: Building and Evaluating Preference Modelsmentioning

confidence: 99%

“…(1) If the customer is negotiating with a pseudonym associated with a preferred model P m i and the seller offers a product that does not fit P m i but is acceptable considering the totality of the preferences P , the customer should just ignore such a product, and buy it when she is using the pseudonym for her part of the preferences corresponding to such product. (2) This strategy is particularly useful to detect abusive prices imposed by the seller according to customers' preferences. This is because the customer can use a pseudonym to query prices of products associated with other identities.…”

Section: Pseudonym Per Preferencementioning

confidence: 99%

“…Indeed, much of the agent-based e-commerce literature has been precisely focusing on processing customers' information to achieve more effective negotiation strategies from the point of view of the seller [4,8,23,2]. All these approaches try to construct a detailed profile of the customer so that sellers can tailor their offers based on customers' tastes.…”

Section: Introductionmentioning

confidence: 99%

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Strategies for avoiding preference profiling in agent-based e-commerce environments

et al. 2013

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Agent-based electronic commerce is known to offer many advantages to users. However, very few studies have been devoted to deal with privacy issues in this domain. Nowadays, privacy is of great concern and preserving users' privacy plays a crucial role to promote their trust in agent-based technologies. In this paper, we focus on preference profiling, which is a wellknown threat to users' privacy. Specifically, we review strategies for customers' agents to prevent seller agents from obtaining accurate preference profiles of the former group by using data mining techniques. We experimentally show the efficacy of each of these strategies and discuss their suitability in different situations. Our experimental results show that customers can improve their privacy notably with these strategies.

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Section: Building and Evaluating Preference Modelsmentioning

confidence: 99%

Section: Pseudonym Per Preferencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Strategies for avoiding preference profiling in agent-based e-commerce environments

et al. 2013

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“…Sequential negotiation agenda management (Pendergast 1990) has been studied for simple contracts (Fatima, Wooldridge, and Jennings 2004) where independence is assumed among multiple issues or where negotiation problems are restricted (e.g. resource allocation (Fatima and Wooldridge 2013) or service-level agreements (Abedin et al 2009)). Research work also has been performed on managing concurrent one-to-many negotiations, which also may improve scalability by parallelization (Mansour, Kowalczyk, and Wosko 2012;Sim and Shi 2010).…”

Section: Introductionmentioning

confidence: 99%

Scalable Complex Contract Negotiation with Structured Search and Agenda Management

Zhang

Klein

Marsa-Maestre

2014

AAAI

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A large number of interdependent issues in complex contract negotiation poses a significant challenge for current approaches, which becomes even more apparent when negotiation problems scale up. To address this challenge, we present a structured anytime search process with an agenda management mechanism using a hierarchical negotiation model, where agents search at various levels during the negotiation with the guidance of a mediator. This structured negotiation process increases computational efficiency, making negotiations scalable for large number of interdependent issues. To validate the contributions of our approach, 1) we developed our proposed negotiation model using a hierarchical problem structure and a constraint-based preference model for real-world applications; 2) we defined a scenario matrix to capture various characteristics of negotiation scenarios and developed a scenario generator that produces test cases according to this matrix; and 3) we performed an extensive set of experiments to study the performance of this structured negotiation protocol and the influence of different scenario parameters, and investigated the Pareto efficiency and social welfare optimality of the negotiation outcomes. The experimental result supports the hypothesis that this hierarchical negotiation approach greatly improves scalability with the complexity of the negotiation scenarios.

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“…Two agents interact to settle on a value for that single issue. The more complex negotiations take place over multiple issues [1,26]. In a multi-issue negotiation the importance of the issues may vary for the participants.…”

mentioning

confidence: 99%

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

Aydoğan

Yolum

2010

Auton Agent Multi-Agent Syst

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We consider automated negotiation as a process carried out by software agents to reach a consensus. To automate negotiation, we expect agents to understand their user's preferences, generate offers that will satisfy their user, and decide whether counter offers are satisfactory. For this purpose, a crucial aspect is the treatment of preferences. An agent not only needs to understand its own user's preferences, but also its opponent's preferences so that agreements can be reached. Accordingly, this paper proposes a learning algorithm that can be used by a producer during negotiation to understand consumer's needs and to offer services that respect consumer's preferences. Our proposed algorithm is based on inductive learning but also incorporates the idea of revision. Thus, as the negotiation proceeds, a producer can revise its idea of the consumer's preferences. The learning is enhanced with the use of ontologies so that similar service requests can be identified and treated similarly. Further, the algorithm is targeted to learning both conjunctive as well as disjunctive preferences. Hence, even if the consumer's preferences are specified in complex ways, our algorithm can learn and guide the producer to create well-targeted offers. Further, our algorithm can detect whether some preferences cannot be satisfied early and thus consensus cannot be reached. Our experimental results show that the producer using our learning algorithm negotiates faster and more successfully with customers compared to several other algorithms.

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Preference Ordering in Agenda Based Multi-issue Negotiation for Service Level Agreement

Cited by 4 publications

References 20 publications

Strategies for avoiding preference profiling in agent-based e-commerce environments

Strategies for avoiding preference profiling in agent-based e-commerce environments

Scalable Complex Contract Negotiation with Structured Search and Agenda Management

Learning opponent’s preferences for effective negotiation: an approach based on concept learning

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