Editing Distributed artificial intelligence (section)

==Goals==
The objectives of Distributed Artificial Intelligence are to solve the [[automated reasoning|reasoning]], planning, learning and perception problems of [[artificial intelligence]], especially if they require large data, by distributing the problem to autonomous processing nodes (agents). To reach the objective, DAI requires:
* 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. Mainstream 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.
* Multi-Agent Based Simulation (MABS): a branch of DAI that builds the foundation for simulations that need to analyze not only phenomena at [[macroscopic|macro]] level but also at [[microscopic scale|micro]] level, as it is in many [[social simulation]] scenarios.