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Trends in Multiagent Negotiation: From Bilateral Bargaining to Consensus Policies

  • Enrique de la Hoz
  • Miguel A. López-Carmona
  • Iván Marsá-Maestre
Chapter
Part of the Law, Governance and Technology Series book series (LGTS, volume 8)

Abstract

Automated negotiation provides an important mechanism to reach agreements among distributed decision makers. It has been extensively studied from the perspective of e-commerce, though it can be seen from a more general perspective as a paradigm to solve coordination and cooperation problems in complex systems, e.g., task allocation, resource sharing, or surplus division. A variety of negotiation models have been proposed according to the many different parameters which may characterize a negotiation scenario. In this chapter, we briefly review the key concepts about multi-attribute negotiation and the most relevant works in the field, and then we focus on one of the more challenging topics on the field in the last few years, namely complex negotiations. In particular, we focus on situations where unanimous agreement is not possible or simply not desired, which is very common in negotiations involving complex, non-monotonic utility spaces. We describe a framework with which to perform multiagent negotiations where we can specify the type of agreements needed in terms of utility sharing among the agents. The proposed multi-round mediation process is based on the analysis of the agents’ offers at each negotiation round. At each round, the mediator applies Global Pattern Search (GPS) to the offers and then a linguistic expressed mediation rule based on Ordered Weighted Averaging Operators (OWA) that formalizes the consensus policy. At each round this mediation process generates a social contract that is submitted as feedback to the agents.

Keywords

Order Weight Average Utility Space Negotiation Protocol Negotiation Model Order Weight Average Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht. 2013

Authors and Affiliations

  • Enrique de la Hoz
    • 1
  • Miguel A. López-Carmona
    • 1
  • Iván Marsá-Maestre
    • 1
  1. 1.Computer Engineering DepartmentUniversidad de Alcalá, Escuela PolitécnicaAlcalá de Henares (Madrid)Spain

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