Belief Fusion: Aggregating Pedigreed Belief States

Authors:
Pedrito Maynard-Reid, II;Yoav Shoham
Affiliations:
Computer Science Department, Stanford University, Stanford, CA 94305, U.S.A. E-mail: pedmayn@cs.stanford.edu;Computer Science Department, Stanford University, Stanford, CA 94305, U.S.A. E-mail: shoham@cs.stanford.edu
Venue:
Journal of Logic, Language and Information
Year:
2001

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Abstract

We introduce a new operator – belief fusion– which aggregates the beliefs of two agents, each informed by asubset of sources ranked by reliability. In the process we definepedigreed belief states, which enrich standard belief states withthe source of each piece of information. We note that the fusionoperator satisfies the invariants of idempotence, associativity, andcommutativity. As a result, it can be iterated without difficulty. Wealso define belief diffusion; whereas fusion generally produces abelief state with more information than is possessed by either of itstwo arguments, diffusion produces a state with less information. Fusionand diffusion are symmetric operators, and together define adistributive lattice. Finally, we show that AGM revision can be viewedas fusion between a novice and an expert.