Distributed artificial intelligence: Difference between revisions

Revision as of 08:09, 22 November 2016

Revision as of 12:40, 30 November 2016

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==Definition==

Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision making problems. It is [[Embarrassingly parallel|embarrassingly parallel]], thus able to exploit large scale computation and spatial distribution of computing resources. These properties allow it to solve problems that require the processing of very large data sets. DAI systems consist of autonomous learning 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, 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 aggregated in a single ___location, in contrast to monolithic or 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 datasets. In addition, the source dataset may change or be updated during the course of the execution of a DAI system.