Learning and decision-making in the framework of fuzzy lattices
New learning paradigms in soft computing
Editorial: Special issue in memory of Philippe Smets (1938--2005)
International Journal of Approximate Reasoning
Credal semantics of Bayesian transformations in terms of probability intervals
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the relative belief transform
International Journal of Approximate Reasoning
Belief functions on distributive lattices
Artificial Intelligence
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We present a decision support system which is based on the transferable belief model (TBM), a model to represent someone's degree of belief based on belief functions. The system performs reasoning and decision making by integrating a system for belief propagation and a system for Bayesian decision analysis. The two subsystems are developed within the framework of the valuation-based systems. They are connected through the pignistic transformation as described in the context of the TBM. The system takes as inputs the user's beliefs and utilities, and suggests either the optimal decision or the optimal sequence of decisions. An example concerning a nuclear waste disposal problem is given to demonstrate the applicability of the system in a real-world domain