A Change Model for Credibility Partial Order

  • Luciano H. Tamargo
  • Marcelo A. Falappa
  • Alejandro J. García
  • Guillermo R. Simari
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6929)

Abstract

In a multi-agent system (MAS), an agent may often receive information through a potentially large number of informants. We will consider the case where the informants are independent agents who have their own interests and, therefore, are not necessarily completely reliable; in this setup, it will be natural for some agent to believe an informant more than other. The use of the notion of credibility will allow agents to organize their peers in a partial order that will reflect the relative credibility of their informants. It is also natural that the assigned credibility will change dynamically, leading to changes in the associated partial order. We will investigate the problem of updating the credibility order to reflect the change in the perceived agent’s credibility, seeking to define a complete change theory over the agents’ trust and reputation. The focus will be on the characterization and development of change operators (expansion, contraction, and revision) for modeling the dynamics of this partial order of agents. These operators, characterized through postulates and representation theorems, can be used to dynamically modify the credibility of informants to reflect a new perception of informant’s plausibility, or admit the arrival of a new agent to the system.

Keywords

Partial Order Transitive Closure Belief Revision Change Operator Contraction 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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Luciano H. Tamargo
    • 1
  • Marcelo A. Falappa
    • 1
  • Alejandro J. García
    • 1
  • Guillermo R. Simari
    • 1
  1. 1.National Council of Scientific and Technical Research (CONICET), Artificial Intelligence Research & Development Laboratory (LIDIA)Universidad Nacional del Sur (UNS)Bahía BlancaArgentina

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