Multi-Agent Preference Combination using Subjective Logic

@inproceedings{Jsang2013MultiAgentPC,
  title={Multi-Agent Preference Combination using Subjective Logic},
  author={Audun J{\o}sang},
  year={2013}
}
Situations where agents with different preferences try to agree on a single choice occur frequently. This must not be confused with fusion of evidence from different agents to determine the most likely correct hypothesis or actual event. Multi-agent preference combination assumes that each agent has already made up her mind, and is about determining the most acceptable decision or choice for the group of agents. This paper formalises and expresses preferences for a state variable in the form of… CONTINUE READING

Figures and Tables from this paper.

Citations

Publications citing this paper.
SHOWING 1-7 OF 7 CITATIONS

Categories of Belief Fusion

VIEW 2 EXCERPTS
CITES BACKGROUND & METHODS

Biometric data fusion based on subjective logic

  • 17th International Conference on Information Fusion (FUSION)
  • 2014
VIEW 1 EXCERPT
CITES BACKGROUND

Determining model correctness for situations of belief fusion

  • Proceedings of the 16th International Conference on Information Fusion
  • 2013
VIEW 1 EXCERPT
CITES BACKGROUND

Interpretation and fusion of hyper opinions in subjective logic

  • 2012 15th International Conference on Information Fusion
  • 2012
VIEW 2 EXCERPTS
CITES BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 14 REFERENCES

A Mathematical Theory of Evidence

G. Shafer
  • 1976
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Dempster's Rule as Seen by Little Colored Balls

  • Computational Intelligence
  • 2010
VIEW 3 EXCERPTS

Cumulative and averaging fusion of beliefs

Audun Jøsang, Javier Díaz, Maria Rifqi
  • Information Fusion
  • 2007
VIEW 2 EXCERPTS

Positive and negative preferences

Stefano Bistarelli, Maria Silvia Pini, K. Brent Venable
  • In Proceedings of the 7th International Workshop on Preferences and Soft Constraints,
  • 2005
VIEW 3 EXCERPTS

A Logic for Uncertain Probabilities

  • International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
  • 2001
VIEW 2 EXCERPTS