This paper describes a direct revelation mechanism for eliciting (a) Decision makers’ range of subjective priors under Knightian uncertainty and their second-order introspective belief and (b) Bayesian decision makers’ range of posteriors and their subjective information

Application of Bayesian belief networks in systems that interact directly with human users, such as decision support systems, requires eeective user interfaces. The principal task of such interfaces is bridging the gap between probabilistic models and human intuitive approaches to modeling uncertainty. We describe several methods for automatic generation of qualitative verbal explanations in sy...

This paper studies probability problems of intuitionistic fuzzy sets and the belief structure of general intuitionistic fuzzy information systems. First, for some special intuitionistic fuzzy sets, a probability is defined by using the integral operations on the level sets, and its properties are discussed. Then using this probability, a mass function of an intuitionistic fuzzy information syst...

This paper investigates the induction of decision trees based on the theory of belief functions. This framework allows to handle training examples whose labeling is uncertain or imprecise. A former proposal to build decision trees for twoclass problems is extended to multiple classes. The method consists in combining trees obtained from various two-class coarsenings of the initial frame.

Dempster−Shafer belief function theory can address a wider class of uncertainty than the standard probability theory does, and this fact appeals the researchers in operations research society for potential application areas. However, the lack of a decision theory of belief functions gives rise to the need to use the probability transformation methods for decision making. For representation of s...

We describe a point-based approximate value iteration algorithm for partially observable Markov decision processes. The algorithm performs value function updates ensuring that in each iteration the new value function is an upper bound to the previous value function, as estimated on a sampled set of belief points. A randomized belief-point selection scheme allows for fast update steps. Results i...

In this paper logical techniques developed to formalise the analysis of multi-interpretable information, in particular belief set operators and selection operators, are applied to an ecological domain. A knowledge-based decision support system is described that determines the abiotic (chemical and physical) characteristics of a site on the basis of samples of plant species that are observed. Th...

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate...

This article studies situations in which information is ambiguous and only part of it can be probabilized. It is shown that the information can be modeled through belief functions if and only if the nonprobabilizable information is subject to the principles of complete ignorance. Next the representability of decisions by belief functions on outcomes is justified by means of a neutrality axiom. ...

The transferable belief model is a model to represent quantified beliefs based on the use of belief functions, as initially proposed by Shafer. It is developed independently from any underlying related probability model. We summarize our interpretation of the model and present several recent results that characterize the model. We show how rational decision must be made when beliefs are represe...