Defining new approximations of belief function by means of Dempster's combination

Thomas Burger (1)

1 Lab-STICC - Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (UMR 3192)

Approximating a belief function (with a probability distribution or with another belief function with a restricted number of focal elements) is an important issue in Dempster-Shafer Theory. The reason is that such approximations are really useful in two different situations: (1) decision making and (2) computational saving. In this paper, we propose to consider the definition of a proxy for a belief function as the result of the Dempster's combination of two belief functions: The first one is the belief function to approximate and the second one is a Bayesian belief function which encodes a meta-information describing the support of the approximation (i.e. the set of the potential focal elements of the proxy).

Domaines: Intelligence artificielle [cs.AI]

Thomas Burger. Defining new approximations of belief function by means of Dempster's combination. Workshop on the theory of belief functions, Apr 2010, Brest, France. ⟨hal-00458697⟩