Abstract
Goal of this paper is to develop a meta-model, which provides the basis for developing highly scalable artificial intelligence systems that should be able to make autonomously decisions based on different dynamic and specific influences. An artificial neural network builds the entry point for developing a multi-layered human readable model that serves as knowledge base and can be used for further investigations in deductive and inductive reasoning. A graph-theoretical consideration gives a detailed view into the model structure. In addition to it the model is introduced using the example of large software development projects. The integration of Constraints and Deductive Reasoning Element Pruning are illustrated, which are required for executing deductive reasoning efficiently.
Highlights
IT technologies are subjects to a fast changeable field of application
Examples are [2], the “scikit-learn” library [3], the “Mlpy” library [4] or “Orange” library [5]. As opposed to these projects and publications the project behind this paper focuses on large software developments that typically have many various influences and a large set of required or requested software tools and business artifacts
Currently in the domain of software developments there is no appropriated model for illustrating knowledge bases (KB) [6], which are necessary for automated handling machine learning and deductive reasoning
Summary
IT technologies are subjects to a fast changeable field of application. Software development teams have to adapt continuously for fitting newest stakeholder needs and finding success in the market. Success of large software development projects for example product line developments depends on many different influencing factors, introduced in [1]. Currently in the domain of software developments there is no appropriated model for illustrating knowledge bases (KB) [6], which are necessary for automated handling machine learning and deductive reasoning. For this reason an abstract human readable meta-model (defined in [7]) should be developed that deals as architectural basis for further investigations in autonomous decision making having regard to different specific influences as projectspecific, personal, economic-driven, product-related or technology-based
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