Approximating Constraint-Based Utility Spaces Using Generalized Gaussian Mixture Models

  • Rafik Hadfi
  • Takayuki Ito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8861)

Abstract

Complex negotiations are characterized by a particular type of utility spaces that is usually non-linear and non-monotonic. An example of such utility spaces are constraint-based utility spaces. The multitude of constraints’ shapes that could potentially be used by the negotiating agents makes any opponent modeling attempt more challenging. The same problem persists even when the agent is exploring her own utility space as to find her optimal contracts. Seeking a unified form for constraint-based utility representation might shed some light on how to tackle these problems.

In this paper, we propose to find an approximation for constraint-based preferences, used mainly in complex negotiation with non-linear utility spaces. The proposed approximation yields a compact form that unifies a whole family of constraints (Cubic, Bell, Conic, etc.). Results show that the new canonical form can in fact be an alternative representation for all known constraint-based utility functions. Additionally, it leads us to a potential parametric model that could be used for opponent modeling in complex non-linear negotiations.

Keywords

Utility Function Multiagent System Optimal Contract Constant Relative Risk Aversion Utility Space 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Rafik Hadfi
    • 1
  • Takayuki Ito
    • 1
  1. 1.Department of Computer Science and EngineeringNagoya Institute of TechnologyShowa-kuJapan

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