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Artificial intelligence engineering (AI engineering) is a technical discipline that focuses on the design, development, and deployment of AI systems. AI engineering involves applying engineering principles and methodologies to create scalable, efficient, and reliable AI-based solutions.
The V-model is a graphical representation of a systems development lifecycle.It is used to produce rigorous development lifecycle models and project management models. The V-model falls into three broad categories, the German V-Modell, a general testing model, and the US government standard.
In systems engineering and software engineering, requirements analysis focuses on the tasks that determine the needs or conditions to meet the new or altered product or project, taking account of the possibly conflicting requirements of the various stakeholders, analyzing, documenting, validating, and managing software or system requirements.
In later publications, [1] Boehm describes the spiral model as a "process model generator," where choices based on a project's risks generate an appropriate process model for the project. Thus, the incremental, waterfall, prototyping, and other process models are special cases of the spiral model that fit the risk patterns of certain projects.
Given a description of the possible initial states of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem is to synthesize a plan that is guaranteed (when applied to any of the initial states) to generate a state which contains the desired goals (such a state is called a goal state).
The first known prominent public usage of the term "Model-Based Systems Engineering" is a book by A. Wayne Wymore with the same name. [8] The MBSE term was also commonly used among the SysML Partners consortium during the formative years of their Systems Modeling Language (SysML) open source specification project during 2003-2005, so they could distinguish SysML from its parent language UML v2 ...