Improving bilateral negotiation with evolutionary learning

Romanhuki, E.; Fuckner, Marcio; Enembreck, Fabrício; Avila, Braulio C.; Scalabrin, Edson Emílio

doi:10.1109/cscwd.2008.4537005

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…For instance, the field of machine learning has supplied techniques that make it possible to discover behavior patterns dynamically using a set of offers and counter-offers from the parties involved in a negotiation [11]; the games theory has made a contribution of the definition and application of utility functions [14]; the field of distributed artificial intelligence has supplied that conceptual and practical basis for creating systems whose control and data can be physically and geographically distributed; and the web has equipped the basic platform to enable a distributed electronic market system [13].…”

Section: Related Workmentioning

confidence: 99%

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Multi-phase negotiation for single-item bidding

Benicio

Possebom

Avila

et al. 2014

Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

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This article presents a multi-phase bidding model that is primarily for reverse bidding (1:N) and can also be used later for bilateral bidding (1:1). This multi-phase approach excels by competing for the lowest price and relativizes a subtle "losewin" relationship of its own for the reverse auction through a second phase of negotiation. The latter is limited to a bilateral relationship and is applied, if necessary, between the purchaser and the second or third best offer of the reverse auction. Experiments were conducted with stationary or adaptive negotiation agents using learning techniques to conduct negotiation policy, using a genetic algorithm to characterize the opponent's preferences and configure the generation of interesting offers. The results showed the influence that an aggressive bidder has on the process as a whole and also what can be done to minimize this effect Keywords-Reverse auction bilateral negotiation computational learning

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Section: Related Workmentioning

confidence: 99%

“…This form is based on an exchange of offers [7] and can have a single criterion [13] or multiple criteria [2] [14]. For the negotiation to be successful, each party involved must make concessions and be capable of determining how interesting a given offer is.…”

Section: B Bilateral Negotiationmentioning

confidence: 99%

Multi-phase negotiation for single-item bidding

Benicio

Possebom

Avila

et al. 2014

Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Self Cite

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“…In nonspecific compensation, one of the players accomplishes his goals and pays the other for fulfilling its interests. Strategies can also be classified as one of the following three types [12,13]:…”

Section: Related Workmentioning

confidence: 99%

Automated combination of bilateral energy contracts negotiation tactics

Pinto

Silva

et al. 2018

2018 IEEE Power &Amp; Energy Society General Meeting (PESGM)

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This paper addresses the theme automated bilateral negotiation of energy contracts. In this work, the automatic combination between different negotiation tactics is proposed. This combination is done dynamically throughout the negotiation process, as result from the online assessment that is performed after each proposal and counter-proposal. The proposed method is integrated in a decision support system for bilateral negotiations, called Decision Support for Energy Contracts Negotiations (DECON), which in turn is integrated with the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM). This integration enables testing and validating the proposed methodology in a realistic market negotiation environment. A case study is presented, demonstrating the advantages of the proposed approach.

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“…Works as [11] and [12] add knowledge of computer science to Faratin model in their applications and adaptations. The former used software called EmallBargainer selected people to negotiate between themselves or with independent agents without knowing if its negotiator is with another person or a machine.…”

Section: A Heuristic Approach For Negotiation Modelsmentioning

confidence: 99%

A bilateral negotiation model for supply chain

Leão

Morais

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Studies about negotiation in logistics has getting more importance in supply chain community. A good agreement is able to bring competitive advantages besides low cost to the organization. This paper discusses some existing heuristic models and proposes a bilateral negotiation model for logistic suppliers in supply chain based on Faratin procedure. It then examines some adjustments to the issues of supply chain and performs simulations to test the consistency of the model.

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Improving bilateral negotiation with evolutionary learning

Cited by 4 publications

References 10 publications

Multi-phase negotiation for single-item bidding

Multi-phase negotiation for single-item bidding

Automated combination of bilateral energy contracts negotiation tactics

A bilateral negotiation model for supply chain

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