Download Intelligence-Based Systems Engineering by Andreas Tolk, Kevin MacG. Adams (auth.), Andreas Tolk, PDF

By Andreas Tolk, Kevin MacG. Adams (auth.), Andreas Tolk, Lakhmi C. Jain (eds.)

The overseas Council on platforms Engineering (INCOSE) defines structures Engineering as an interdisciplinary method and potential to allow the conclusion of winning platforms.

Researchers are utilizing intelligence-based options to help the practices of platforms engineering in an leading edge means. This examine quantity features a number of contributions via topic specialists to layout higher systems.

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M. B. Keating These distinguishing characteristics begin to place some degree of formality on the notion of system-of-systems, but something is missing. In order to go beyond the traditional perspective of a fully integrated system-of-systems which perfectly shares data in what we call hard interoperability, we must invoke a more systemic view. The ideal state for a system-of-systems requires what we will call systemic interoperability. Systemic interoperability is a holistic view of interoperability and requires compatibility in worldview and conceptual, contextual, and cultural interoperability, allowing the system-of-systems to act consistently with regard to purpose, function, and form.

The Discrete Event System Specification (DEVS) formalism for simulation systems emerged from systems engineering efforts two decades ago. Using the results of the simulation community to improve DEVS and merging various trends into the DEVS Unified Process (DUNIP) allows now the development of Agile Net-centric Systems using DUNIP-Based Event Driven Architectures described by Saurabh Mittal. As intelligent agents can be part of this simulation-based systems engineering approach, this chapter connects both worlds.

As one of the focal points of intelligence-based systems engineering is the support by intelligent machines, understanding within such machines is absolutely essential to be effective and efficient and to avoid significant errors by misinterpretations. The work on Defining and Validating Semantic Machine to Machine Interoperability by Claudia Szabo and Saikou Diallo is based on awarded academic research in this domain helps to better understand the challenges and provides first solutions of interest to the scholars and practitioners in the field.

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