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A Deterministic Algorithm for Solving Imprecise Decision Problems

AAAI Conferences

Today there are numerous tools for decision analysis, suitable both for human and artificial decision makers. Most of these tools require the decision maker to provide precise numerical estimates of probabilities and utilities. Furthermore, they lack the capability to handle inconsistency in the decision models, and will fail to deliver an answer unless the formulation of the decision problem is consistent. In this paper we present an algorithm for evaluating imprecise decision problems expressed using belief distributions, that also can handle inconsistency in the model. The same algorithm can be applied to decision models where probabilities and utilities are given as intervals or point values, which gives us a general method for evaluating inconsistent decision models with varying degree of expressiveness.



A Belief-Function Based Decision Support System

arXiv.org Artificial Intelligence

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.


Globalisation of Belief Distributions

AAAI Conferences

The probability and utility estimates involw d in such t situation is expressed tm sets of distributions, tel)resenting beliefs in various vectors in tim decision space.