This work aims to recognize signs which have both manual and nonmanual components by providing a sequential belief-based fusion mechanism. We propose a methodology based on belief functions for fusing extracted manual and non-manual features in a sequential two-step approach. The belief functions based on the likelihoods of the hidden Markov models are used to decide whether there is an uncerta...

Call the following the storage hypothesis : degreed beliefs are stable attitudes of agents that can be appealed to for predicting and explaining patterns of behavior, judgment, and decision making. [1] the storage hypothesis is that humans have, or it is as if we have, degrees of belief, and our judgment and decision making across a wide range of situations is systematically predicted by them (...

The aim of the article is to show a stochastic approach for both modelling and optimizing the statistical agent belief in a probability model. Two networks are defined: a decision network D of the agent belief state and a utility network U, presenting the utility structure of the agent belief problem. The agent belief is presented via the following three items (B,D,U), where B is a Bayesian net...

Although a number of related algorithms have been developed to evaluate influence diagrams, exploiting the conditional independence in the diagram, the exact solution has remained intractable for many important problems. In this paper we introduce decision circuits as a means to exploit the local structure usually found in decision problems and to improve the performance of influence diagram an...

Prospect theory assumes nonadditive decision weights for preferences over risky gambles. Such decision weights generalize additive probabilities. This article proposes a decomposition of decision weights into a component reflecting risk attitude and a new component depending on belief. The decomposition is based on an observable preference condition and does not use other empirical primitives s...

Prospect theory assumes nonadditive decision weights for preferences over risky gambles. Such decision weights generalize additive probabilities. This article proposes a decomposition of decision weights into a component reflecting risk attitude and a new component depending on belief. The decomposition is based on an observable preference condition and does not use other empirical primitives s...

Partially observable Markov decision process (POMDP) is a commonly adopted mathematical framework for solving planning problems in stochastic environments. However, computing the optimal policy of POMDP for large-scale problems is known to be intractable, where the high dimensionality of the underlying belief state space is one of the major causes. Our research focuses on studying two different...

The theory of belief functions has been successfully used in many classification tasks. It is especially useful when combining multiple classifiers and when dealing with high uncertainty. Many classification approaches such as k-nearest neighbors, neural network or decision trees have been formulated with belief functions. In this paper, we propose an evidential calibration method that transfor...