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Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems.It is embarrassingly parallel, thus able to exploit large scale computation and spatial distribution of computing resources.
Distributed practice (also known as spaced repetition, the spacing effect, or spaced practice) is a learning strategy, where practice is broken up into a number of short sessions over a longer period of time.
Distributed cognition is an approach to cognitive science research that was developed by cognitive anthropologist Edwin Hutchins during the 1990s. [1]From cognitive ethnography, Hutchins argues that mental representations, which classical cognitive science held that are within the individual brain, are actually distributed in sociocultural systems that constitute the tools to think and ...
Distributed computing is a field of computer science that studies distributed systems, defined as computer systems whose inter-communicating components are located on different networked computers. [1] [2] The components of a distributed system communicate and coordinate their actions by passing messages to
Stream processing is essentially a compromise, driven by a data-centric model that works very well for traditional DSP or GPU-type applications (such as image, video and digital signal processing) but less so for general purpose processing with more randomized data access (such as databases). By sacrificing some flexibility in the model, the ...
In the middle of Sternberg's theory is cognition and with that is information processing. In Sternberg's theory, he says that information processing is made up of three different parts, meta components, performance components, and knowledge-acquisition components. [2] These processes move from higher-order executive functions to lower-order ...
Distributed data processing. Distributed data processing [1] (DDP) [2] was the term that IBM used for the IBM 3790 (1975) and its successor, the IBM 8100 (1979). Datamation described the 3790 in March 1979 as "less than successful." [3] [4] Distributed data processing was used by IBM to refer to two environments: IMS DB/DC; CICS/DL/I [5] [6]
These could be distributed around plant, and communicate with the graphic display in the control room or rooms. The distributed control system was born. The introduction of DCSs allowed easy interconnection and re-configuration of plant controls such as cascaded loops and interlocks, and easy interfacing with other production computer systems.