Miscellaneous/Divers in areas (2024-03-15)

The content of experiments must be semantically described. This topic has already been largely covered. However, some neglected benefits of such an approach provide more arguments in favour of scientific knowledge graphs. Beyond being searchable through flat metadata, a knowledge graph of experiment descriptions may be able to provide answers to scientific and methodological questions. This includes identifying non experimented conditions or retrieving specific techniques used in experiments. In turn, this is useful for researchers as this information can be used for repurposing experiments, checking claimed results or performing meta-analyses.

While symbolic knowledge representation and reasoning methods are necessary for almost any kind of knowledge-based application, they often lack numerically represented uncertainty and vagueness. Meanwhile, different applications would require different numeric calculi. SMECI Uncertain Module (SUM) enables to embed an uncertain (or graded) calculus into a multi-paradigm environment (including tasks, rules, objects and multiple-worlds), allowing therefore the object model to take into account uncertain values so that the inference engine can draw uncertain inferences from uncertain and vague premises. The originality of SUM is that it does not make strong assumptions about the calculus used, which only has to respect some fundamental "format" expressed through the design of basic objects and the instantiation of a set of generic primitives. Therefore, SUM is not restricted to numeric truth values but can deal with any kind of values provided with an implementation of the generic interface.

Modular constraint satisfaction organizes a constraint satisfaction problem (CSP) into a hierarchically linked set of modules. Using a modular description of a CSP brings the advantages of classical modular development methodology such as problem decomposition or incremental problem definition. A module can be seen as either a CSP or a constraint. Moreover, modular constraint satisfaction environments can be build on top of existing constraint satisfaction packages. Stating CSP in terms of modules does not bring any computational advantage in itself, but can help to state problems in a way that emphasizes the computational advantages of "tree clustered" CSP. Down and upward strategies are presented which allow to take into account, during the constraint solving process, the hierarchical structure of modular CSP. Moreover, modular CSP has been designed in order to implement dynamic CSP by grouping dynamic components into related clusters. This is shown through applications to configuration design and story understanding. Nevertheless, modular CSP is a first step toward generic modular CSP enabling to develop hierarchies of components which share the same interface.