A Belief-Function Based Decision Support System
This work provides a decision support tool for users needing to make informed choices based on uncertain information, but it appears incremental as it combines existing methods without major breakthroughs.
The paper tackles the problem of decision support by integrating belief function propagation with Bayesian decision analysis via the pignistic transformation, resulting in a system that processes user inputs to suggest testing sequences with a user-friendly interface.
In this paper, we present a decision support system based on belief functions and the pignistic transformation. The system is an integration of an evidential system for belief function propagation and a valuation-based system for Bayesian decision analysis. The two subsystems are connected through the pignistic transformation. The system takes as inputs the user's "gut feelings" about a situation and suggests what, if any, are to be tested and in what order, and it does so with a user friendly interface.