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Since they are used in various contexts and for different purposes, their typical components and levels of complexity varies in literature (compare for example the W.K. Kellogg Foundation [10] presentation of logic model, mainly aimed for evaluation, and the numerous types of logic models in the intervention mapping framework [11]). In addition ...
The core of the Logical Framework is the "temporal logic model" that runs through the matrix. This takes the form of a series of connected propositions: If these Activities are implemented, and these Assumptions hold, then these Outputs will be delivered. If these Outputs are delivered, and these Assumptions hold, then this Purpose will be ...
In logic, a logical framework provides a means to define (or present) a logic as a signature in a higher-order type theory in such a way that provability of a formula in the original logic reduces to a type inhabitation problem in the framework type theory. [1] [2] This approach has been used successfully for (interactive) automated theorem ...
Logical Data Modelling The process of identifying, modelling and documenting the data requirements of the system being designed. The result is a data model containing entities (things about which a business needs to record information), attributes (facts about the entities) and relationships (associations between the entities).
Fuzzy logic is an important concept in medical decision making. Since medical and healthcare data can be subjective or fuzzy, applications in this domain have a great potential to benefit a lot by using fuzzy-logic-based approaches. Fuzzy logic can be used in many different aspects within the medical decision making framework.
A graphical representation of a partially built propositional tableau. In proof theory, the semantic tableau [1] (/ t æ ˈ b l oʊ, ˈ t æ b l oʊ /; plural: tableaux), also called an analytic tableau, [2] truth tree, [1] or simply tree, [2] is a decision procedure for sentential and related logics, and a proof procedure for formulae of first-order logic. [1]
Probabilistic Soft Logic (PSL) is a statistical relational learning (SRL) framework for modeling probabilistic and relational domains. [ 2 ] It is applicable to a variety of machine learning problems, such as collective classification , entity resolution , link prediction , and ontology alignment .
Another technique for primitives is to define languages that are modeled after First Order Logic (FOL). The most well known example is Prolog, but there are also many special-purpose theorem-proving environments. These environments can validate logical models and can deduce new theories from existing models.