Generally, ERA, like most risk assessment methodology, is founded on the need to address uncertainty in the decision process. Management decisions regarding the aquatic ecosystem have to deal with both epistemic (knowledge base) uncertainty and innate variability pertaining to both the biotic and abiotic components of the ecosystem. Conventional ERA methodology depends on a statistical approach...

Similarly to many areas of Artificial Intelligence, Logic as well has approached the definition of inferential systems that take into account elements from real-life situations. In particular, logical treatments have been trying to deal with the phenomena of vagueness and uncertainty. While a degree-based computational model of vagueness has been investigated through fuzzy set theory [88] and f...

Support logic programming and its practical implementation (Fril) integrates probabilistic and fuzzy uncertainty into logic programming using mass assignments. This paper presents a snapshot of current research, aimed at combining the best aspects of inductive logic programming with the uncertainty representation of Fril to create a sophisticated and novel approach to knowledge discovery. An ex...

The problem of computer vision is to automatically characterize the contents of digitized images. Applications include factory automation, navigation, digital libraries, and medicine. Not only is recognition an “inverse problem” with no single mathematical solution, but it is also complicated by external sources of uncertainty such as the conditions of image formation. Thus, the need for dealin...

Health risk assessments have been carried out worldwide to examine potential health risk due to exposure to toxic contaminants in various environments. In risk assessment, it is most important to know the nature of all available information, data or model parameters. It is observed that available information/data are tainted with uncertainty and variability in the same time, i.e., uncertainty a...

Probabilistic logic and credibilistic logic are two branches of multi-valued logic for dealing with random knowledge and fuzzy knowledge, respectively. In this paper, a hybrid logic is introduced for dealing with random knowledge and fuzzy knowledge simultaneously. First, a hybrid formula is introduced on the basis of random proposition and fuzzy proposition. Furthermore, a hybrid truth value i...

In this paper we deal with a new approach to probabilistic reasoning in a logical frame­ work. Nearly almost all logics of probabil­ ity that have been proposed in the litera­ ture are based on classical two-valued logic. After making clear the differences between fuzzy logic and probability theory, here we propose a fuzzy logic of probability for which completeness results (in a probabilistic ...

One of the most efficient techniques for processing interval and fuzzy data is a Monte-Carlo type technique of Cauchy deviates that uses Cauchy distributions. This technique is mathematically valid, but somewhat counterintuitive. In this paper, following the ideas of Paul Werbos, we provide a natural neural network explanation for this technique. Keywords— Cauchy deviate method, fuzzy uncertain...

Risk became the crucial decision making criteria in evaluation of some control actions in power systems, but very often, these decisions are made in a highly uncertain environment. In this paper, a new graphical tool for risk assessment and decision making under uncertainty – hybrid influence diagram with fuzzy probability values and fuzzy random variables is proposed. Influence diagram is a ge...