Fusion Techniques for Reliable Information: A Survey <s> Fuzzy Set Theory (FST) <s> Context simplifies and enriches human-human interaction. However, enhancing human-computer interaction through the use of context remains a difficult task. Applications in pervasive and mobile environments need to be context-aware so that they can adapt themselves to rapidly changing situations. One of the problems is that there is no common, reusable model for context used by these environments. In this paper, we propose a model of context that is based on first order predicate calculus. The first order model allows complex rules involving contexts to be written. It also enables automated inductive and deductive reasoning to be done on contextual information. The first order model allows an expressive description of context using Boolean operators and existential and universal quantifiers. Based on this model, an infrastructure to enable context-awareness in ubiquitous computing environments has been developed. The infrastructure allows the easy development and deployment of various sensors and context-aware applications. It also allows distributed reasoning to take place. This context infrastructure is part of our smart spaces framework for ubiquitous computing, Gaia. <s> BIB001 </s> Fusion Techniques for Reliable Information: A Survey <s> Fuzzy Set Theory (FST) <s> The aim of this Chapter has been to show in what sense fuzzy set theory can be useful in operationalizing multidimensional well-being analysis based on the capability approach. With their capacity to make formal logic and verbal formulations much closer to one another, and to establish close connections between theory and data analysis, fuzzy methodologies seem to be a useful and flexible tool for operationalizing the capability approach while preserving its richness and complexity. <s> BIB002
FST has a set, whose elements have degrees of a membership. It is represented by a pair (A; m) where A is a set and m: A [0; 1]. It permits gradual assessments of the membership of elements in a set BIB002 . It defines set membership as a possibility distribution in real unit interval [0, BIB001 . This is used for modeling which needs more than the PrT with uncertainty. For instance, the FST is used to capture a clinical uncertainty in medical data of pervasive computing applications  .