Distributed artificial intelligence: Difference between revisions

Revision as of 08:39, 15 March 2018

Revision as of 01:13, 12 April 2018

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==Goals==

The objectives of Distributed Artificial Intelligence are to solve the [[automated reasoning|reasoning]], planning, learning and perception problems of [[Artificial intelligence|Artificial Intelligence]], especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requirerequires:

* A [[Distributed computing|distributed system]] with robust and elastic computation on unreliable and failing resources that are loosely coupled

* Coordination of the actions and communication of the nodes

* Subsamples of large data sets and [[online machine learning]]

There are many reasons for wanting to distribute intelligence or cope with multi-agent systems. MainstreamsMainstream problems in DAI research include the following:

* Parallel problem solving: mainly deals with how classic artificial intelligence concepts can be modified, so that [[multiprocessor]] systems and clusters of computers can be used to speed up calculation.

* Distributed problem solving (DPS): the concept of [[Intelligent agent|agent]], autonomous entities that can communicate with each other, was developed to serve as an [[abstraction]] for developing DPS systems. See below for further details.