Towards genetically optimised responsive negotiation agents

Lau, Raymond Y. K.; Tang, Maolin; Wong, On

doi:10.1109/iat.2004.1342958

Cited by 18 publications

(10 citation statements)

References 17 publications

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“…14 In addition, because GAs are based on the evolution principle of "natural selection," they are effective in modeling dynamic negotiation environments, in which good negotiation strategies evolve according to negotiators' changing preferences. As a matter of fact, GAs have been successfully applied to develop automated negotiation systems [15][16][17] before. GA-based negotiation approaches fulfill the general requirements of developing practical negotiation systems in terms of computational efficiency, bounded agent rationality, and the assumption of limited information about the negotiation spaces.…”

Section: Justifications Of the Proposed Approachmentioning

confidence: 99%

An evolutionary learning approach for adaptive negotiation agents

Lau

Tang

Wong

et al. 2005

Int. J. Intell. Syst.

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Developing effective and efficient negotiation mechanisms for real-world applications such as e-business is challenging because negotiations in such a context are characterized by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This article illustrates our adaptive negotiation agents, which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism that guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications.

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Section: Justifications Of the Proposed Approachmentioning

confidence: 99%

An evolutionary learning approach for adaptive negotiation agents

Lau

Tang

Wong

et al. 2005

Int. J. Intell. Syst.

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“…Unlike other negotiation models based on genetic algorithms, this proposal adapts to the environment by dynamically modifying its mutation rate. Lau et al [45] have also reported a negotiation mechanism for non-mediated automated negotiations based on genetic algorithms. The fitness function relies on three aspects: an agent's own preference, the distance of a candidate offer to the previous opponent's offer, and time pressure.…”

Section: Negotiation Optimization and Complexitymentioning

confidence: 99%

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

López-Carmona

Marsa-Maestre

Klein

et al. 2010

Auton Agent Multi-Agent Syst

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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 agents' 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, "low social welfare" 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.

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“…The notion of agency can be applied to build robust architectures for automated negotiation systems within which a group of software agents communicate and autonomously make negotiation decisions on behalf of their human users. Recent research in intelligent agent mediated electronic commerce has highlighted the importance and the benefits of agent-based negotiation support for e-Business [10,12,19,17]. These negotiation agents can considerably reduce human negotiation time and identify optimal or near optimal solutions from combinatorial complex negotiation spaces.…”

Section: Introductionmentioning

confidence: 98%

Adaptive negotiation agents for e-business

Lau

2005

Proceedings of the 7th International Conference on Electronic Commerce - ICEC '05

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Negotiation has been identified as one of the key steps in Business-to-Business (B2B) transaction models. However, developing effective and efficient negotiation mechanisms for e-Business is quite challenging since negotiations in such a context are characterized by combinatorial complex negotiation spaces, tough deadlines, incomplete information about the opponents, and volatile negotiator preferences. Classical negotiation models are not able to offer a satisfactory solution to address all these issues. This paper illustrates our adaptive negotiation agents which are underpinned by a robust evolutionary learning mechanism to deal with complex and dynamic negotiation situations often encountered in e-Business applications. Our experimental results show that the proposed evolutionary negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for e-Business.

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Towards genetically optimised responsive negotiation agents

Cited by 18 publications

References 17 publications

An evolutionary learning approach for adaptive negotiation agents

An evolutionary learning approach for adaptive negotiation agents

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

Adaptive negotiation agents for e-business

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