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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 ...
Knowledge representation makes complex software easier to define and maintain than procedural code and can be used in expert systems. For example, talking to experts in terms of business rules rather than code lessens the semantic gap between users and developers and makes development of complex systems more practical.
Abductive logic programming (ALP) is a high-level knowledge-representation framework that can be used to solve problems declaratively, based on abductive reasoning.It extends normal logic programming by allowing some predicates to be incompletely defined, declared as abducible predicates.
Finally, a logic model of the intervention is developed. This model describes the various activities that will happen and the cascades of effects they are expected to cause toward the desired outcome. Evaluators thereafter use the logic model of the intervention to design a proper evaluation plan to assess implementation, impact and efficiency.
Inductive logic programming (ILP) is an approach to machine learning that induces logic programs as hypothetical generalisations of positive and negative examples. Given a logic program representing background knowledge and positive examples together with constraints representing negative examples, an ILP system induces a logic program that ...
The four views of the model are logical, development, process, and physical view. In addition, selected use cases or scenarios are used to illustrate the architecture serving as the 'plus one' view. Hence, the model contains 4+1 views: [1] Logical view: The logical view is concerned with the functionality that the system provides to end-users.
Automated reasoning programs are being applied to solve a growing number of problems in formal logic, mathematics and computer science, logic programming, software and hardware verification, circuit design, and many others. The TPTP (Sutcliffe and Suttner 1998) is a library of such problems that is updated on a regular basis.
Inductive logic programming has adopted several different learning settings, the most common of which are learning from entailment and learning from interpretations. [16] In both cases, the input is provided in the form of background knowledge B, a logical theory (commonly in the form of clauses used in logic programming), as well as positive and negative examples, denoted + and respectively.