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{{Short description|Subfield of artificial intelligence}}
{{more refs|date=February 2023}}
{{Artificial intelligence}}
{{Multi-agent system}}
'''Distributed
Multi-agent systems and distributed problem solving are the two main DAI approaches. There are numerous applications and tools.
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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==
In 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interactions of intelligent agents.<ref name=Chaib>{{cite journal |last1=Chaib-Draa |first1=Brahim |last2=Moulin |first2=B. |last3=Mandiau |first3=R. |last4=Millot |first4=P. |title=Trends in distributed artificial intelligence |journal=Artificial Intelligence Review |date=1992 |volume=6 |issue=1 |pages=35–66 |doi=10.1007/BF00155579|s2cid=15730245 }}</ref> Distributed artificial intelligence systems were conceived as a group of intelligent entities, called agents, that interacted by cooperation, by coexistence or by competition. DAI is categorized into multi-agent systems and distributed problem solving.<ref name="Bond Gasser">{{cite book |editor1-last=Bond |editor1-first=Alan H. |editor2-last=Gasser |editor2-first=Les |title=Readings in distributed artificial intelligence |date=1988 |publisher=Morgan Kaufmann Publishers |___location=San Mateo, California |isbn=9781483214443 |url=https://www.sciencedirect.com/book/9780934613637/readings-in-distributed-artificial-intelligence}}</ref> In [[multi-agent system]]s the main focus is how agents coordinate their knowledge and activities. For distributed problem solving the major focus is how the problem is decomposed and the solutions are synthesized.
==Goals==
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Areas where DAI have been applied are:
* [[E-commerce|Electronic commerce]], e.g. for [[Trading strategy|trading strategies]] the DAI system learns financial trading rules from subsamples of very large samples of financial data
* [[Telecommunications network|Networks]], e.g. in [[
* [[Scheduling (production processes)|Routing]], e.g. model vehicle flow in transport networks
* [[Scheduling (production processes)|Scheduling]], e.g. [[flow shop scheduling]] where the [[resource management]] entity ensures local optimization and cooperation for global and local consistency
* [[Search engine|Search engines]], e.g. in LLM [[Federated search|federated search]] like Ithy where document retrieval and analysis are distributed to DAI agents before aggregation <ref>{{cite web|title=Ithy: A distributed AI search engine|url=https://ithy.com}}</ref>
* Multi-Agent systems, e.g. [[artificial life]], the study of [[Artificial life|simulated life]]
* Electric power systems, e.g. Condition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System<ref>{{Cite journal|
DAI integration in tools has included:
* ECStar is a distributed rule-based learning system.<ref>{{cite web |title=Anyscale Learning For All {{!}} alfagroup |url=http://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/index.php?n=Site.BP#ECStar-fig |website=alfagroup.csail.mit.edu}}</ref>
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* {{cite book | last1=Shoham | first1=Yoav | last2=Leyton-Brown | first2=Kevin | title=Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations | publisher=[[Cambridge University Press]] | isbn=978-0-521-89943-7 | url=http://www.masfoundations.org/download.html | year=2009 | ___location=New York}}
* Sun, Ron, (2005). ''Cognition and Multi-Agent Interaction''. New York: Cambridge University Press. {{ISBN|978-0-521-83964-8}}
* {{cite journal |last1=Trentesaux |first1=Damien |last2= Philippe |first2=Pesin |last3=Tahon |first3=Christian |title= Distributed artificial intelligence for FMS scheduling, control and design support |journal=Journal of Intelligent Manufacturing |date=2000 |volume=11 |issue=6 |pages=573–589 |doi=10.1023/A:1026556507109|s2cid=36570655 }}
* {{cite book |
|publisher=Morgan & Claypool Publishers |isbn=978-1-59829-526-9 |date=2007 |___location=San Rafael, CA}}
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