Collective decision problems are considered with a finite number of agents who have single-peaked preferences on the real line. A probabilistic decision scheme assigns a probability distribution over the real line to every profile of reported preferences. The main result of the paper is a characterization of the class of unanimous and strategy-proof probabilistic schemes with the aid of fixed p...

Criteria containing a barrier function i.e., an unbounded function at the boundary of the feasible solution domain are frequently encountered in the optimization framework, in particular in interior point methods for constrained optimization. Barrier function has to be carefully handled in the optimization algorithm. When an iterative descent method is used for the minimization, a search along ...

In this paper, the connection between Menger probabilistic norms and H"{o}hle probabilistic norms is discussed. In addition, the correspondence between probabilistic norms and Wu-Fang fuzzy (semi-) norms is established. It is shown that a probabilistic norm (with triangular norm $min$) can generate a Wu-Fang fuzzy semi-norm and conversely, a Wu-Fang fuzzy norm can generate a probabilistic norm.

In this paper, we present a probabilistic framework for grasping. In the framework, we consider grasp and object attributes, on-line sensor information and the stability of a grasp, through probabilistic models. We describe how sensorbased grasp planning can be formulated in a probabilistic framework and how information about object attributes can be updated simultaneously using on-line sensor ...

We present a new semantics sensitive sampling algorithm for probabilistic programs, which are “usual” programs endowed with statements to sample from distributions, and condition executions based on observations. Since probabilistic programs are executable, sampling can be performed by repeatedly executing them. However, in the case of programs with a large number of random variables and observ...

In previous work, we have introduced probabilistic description logic programs for the Semantic Web, which combine description logics, normal programs under the answer set (resp., well-founded) semantics, and probabilistic uncertainty. In this paper, we continue this line of research. We propose an approach to probabilistic data integration for the Semantic Web that is based on probabilistic des...