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Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, [[automated planning|planning]], and decision-making problems. It is [[embarrassingly parallel]], thus able to exploit large scale computation and [[spatial distribution]] of [[System resource|computing resources]]. These properties allow it to solve problems that require the processing of very large [[data set]]s. DAI systems consist of autonomous learning [[Processing mode|processing nodes]] ([[Intelligent agent|agents]]), that are distributed, often at a very large scale. DAI nodes can act independently, and partial solutions are integrated by communication between nodes, [[Asynchrony (computer programming)|often asynchronously]]. By virtue of their scale, DAI systems are robust and elastic, and by necessity, loosely coupled. Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment.
DAI systems do not require all the relevant data to be [[Aggregate data|aggregated]] in a single ___location, in contrast to [[Monolithic application|monolithic]] or [[Centralized computing|centralized]] Artificial Intelligence systems which have tightly coupled and geographically close processing nodes. Therefore, DAI systems often operate on sub-samples or hashed impressions of very large [[Training, validation, and test data sets|datasets]]. In addition, the source dataset may change or be updated during the course of the execution of a DAI system.
==Development==
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