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Autonomy-oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve difficult computational problems. For example, ant colony optimization could be studied in this paradigm.
Four types of autonomy, which promote loose coupling, are: reference autonomy, time autonomy, format autonomy, and platform autonomy. [3] Loose coupling is an architectural principle and design goal in service-oriented architectures. Eleven forms of loose coupling and their tight coupling counterparts are listed in: [4]
Each SCS is an autonomous web application. Each SCS is owned by one team. Communication with other SCSs or third-party systems is asynchronous wherever possible. An SCS can have an optional service API. Each SCS must include data and logic. An SCS should make its features usable to end-users by its own UI.
"Autonomous agents are systems capable of autonomous, purposeful action in the real world." [2] According to Maes (1995): "Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed." [3]
LOBs have full autonomy to develop standards for applications and infrastructure and to define enterprise architectures. The goal of the LOB is to optimize performance at LOB level. Federated Architectures define common or shared architecture standards across autonomous program areas, enabling, e.g., state government entities to maintain ...
Also simply application or app. Computer software designed to perform a group of coordinated functions, tasks, or activities for the benefit of the user. Common examples of applications include word processors, spreadsheets, accounting applications, web browsers, media players, aeronautical flight simulators, console games, and photo editors. This contrasts with system software, which is ...
A neural processing unit (NPU), also known as AI accelerator or deep learning processor, is a class of specialized hardware accelerator [1] or computer system [2] [3] designed to accelerate artificial intelligence (AI) and machine learning applications, including artificial neural networks and computer vision.
A language for specifying state transition systems, and is commonly used to create formal models of the effects of actions on the world. [6] Action languages are commonly used in the artificial intelligence and robotics domains, where they describe how actions affect the states of systems over time, and may be used for automated planning.