Distributed artificial intelligence: Difference between revisions

Revision as of 00:45, 16 August 2016

Revision as of 09:35, 5 November 2016

Line 1:

{{Multiple issues|

{{cleanup|date=October 2011}}

{{more footnotes|date=February 2011}}

}}

'''Distributed Artificial Intelligence (DAI)''' is a subfield of [[artificial intelligence]] research dedicated to the development of distributed solutions for complex problems regarded as requiring intelligence. DAI is closely related to and a predecessor of the field of [[Multi-agent system|Multi-Agent Systems]].

==Definition==

Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision making problems. It is [[Embarrassingly_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_agentIntelligent 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.

==Goals==

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

* A [[Distributed_computingDistributed 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]]

Line 20 ⟶ 23:

==History==

In the 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interaction of intelligent agents[2]. 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 [1]. In [[Multi-agent_system|Multi-agent systemssystem]]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.

==Examples==

Line 27 ⟶ 30:

==Approaches==

Two types of DAI has emerged:

* In [[Multi-agent_system|Multi-agent systemssystem]]s agents coordinate their knowledge and activities and reason about the processes of coordination. Agents are physical or virtual entities that can act, perceive its environment and communicate with other agents. The agent is autonomous and has skills to achieve goals. The agents change the state of their environment by their actions. There are a number of different coordination techniques[3].

* In distributed problem solving the work is divided among nodes and the knowledge is shared. The main concerns are task decomposition and synthesis of the knowledge and solutions.

Line 35 ⟶ 38:

==Applications==

Areas where DAI have been applied are:

* Electronic commerce, e.g. for [[Trading_strategyTrading strategy|trading strategies]] the DAI system learns financial trading rules from subsamples of very large samples of financial data

* Networks, e.g. in [[Telecommunication|telecommunicationstelecommunication]]s the DAI system controls the cooperative resources in a WLAN network http://dair.uncc.edu/projects/past-projects/wlan-resource

* [[Scheduling_Scheduling (production_processesproduction processes)|Routing]], e.g. model vehicle flow in transport networks

* [[Scheduling_Scheduling (production_processesproduction processes)|Scheduling]], e.g. [[flow shop scheduling]] where the resource management entity ensures local optimization and cooperation for global and local consistency

* Multi-Agent systems, e.g. [[Artificial_lifeArtificial life|Artificial Life]], the study of simulated life

==Tools==

Line 88 ⟶ 91:

*[4] Damien Trentesaux, Philippe Pesin, and Christian Tahon. Distributed artificial intelligence for fms scheduling, control

and design support. Journal of Intelligent Manufacturing, 11(6):573-589, 2000.

== Further reading ==

* Hewitt, Carl; and Jeff Inman (November/December 1991). "DAI Betwixt and Between: From 'Intelligent Agents' to Open Systems Science" ''IEEE Transactions on Systems, Man, and Cybernetics''. Volume: 21 Issue: 6, pps. 1409–1419. ISSN 0018-9472