The analysis of decision making under uncertainty is closely related to the analysis of probabilistic inference. Indeed, much of the research into efficient methods for probabilistic inference in expert systems has been motivated by the fundamental nonnative arguments of decision theory. In this paper we show how the developments underlying those efficient methods can be applied immediately to ...

A learner who has already acquired a solution-method can not always use it adequately. Therefore, the practice to use a solution-method is indispensable to master it. The problem solving practice is the most popular, but it is not the only way to make the learner master the solution-method. To make problems which can be solved by a solution-method is a promising way to master the solution-metho...

This paper aims to present new methods for group decision making that take into account different decision making styles and their implementation in decision making using the Group Multichoice system.

In many bird monitoring surveys, no attempt is made to estimate bird densities or abundance. Instead, counts of one form or another are made, and these are assumed to correlate with bird density. Unless complete counts on sample plots are feasible, this approach can easily lead to false conclusions, because detectability of birds varies by species, habitat, observer and many other factors. Tren...

In this work we consider probabilistic approaches to sequential decision making. The ultimate goal is to provide methods by which decision making problems can be attacked by approaches and algorithms originally built for probabilistic inference. This in turn allows us to directly apply a wide variety of popular, practical algorithms to these tasks. In Chapter 1 we provide an overview of the gen...

Making decisions concerning the use of energy sources is a very difficult and complicated task [...]