distributed artificial intelligence distributed artificial intelligence dai also called decentralized artificial intelligence is a subfield of artificial intelligence research dedicated to the development of distributed solutions for problems dai is closely related to and a predecessor of the field of multiagent systems definition distributed artificial intelligence dai is an approach to solving complex learning planning and decision making problems it is 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 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 subsamples 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 intelligence especially if they require large data by distributing the problem to autonomous processing nodes agents to reach the objective dai requires a 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 multiagent 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 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 multiagent based simulation mabs a branch of dai that builds the foundation for simulations that need to analyze not only phenomena at macro level but also at micro level as it is in many social simulation scenarios history in the 1975 distributed artificial intelligence emerged as a subfield of artificial intelligence that dealt with interaction of intelligent agents in multiagent systems 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 multiagent systems and distributed problem solving are the two main dai approaches there are numerous applications and tools approaches two types of dai has emerged in multiagent systems 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 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 dai can apply a bottomup approach to ai similar to the subsumption architecture as well as the traditional topdown approach of ai in addition dai can also be a vehicle for emergence applications areas where dai have been applied are electronic commerce eg for trading strategies the dai system learns financial trading rules from subsamples of very large samples of financial data networks eg in telecommunications the dai system controls the cooperative resources in a wlan network httpdairuncceduprojectspastprojectswlanresource routing eg model vehicle flow in transport networks scheduling eg flow shop scheduling where the resource management entity ensures local optimization and cooperation for global and local consistency multiagent systems eg artificial life the study of simulated life electric power systems eg condition monitoring multiagent system commas applied to transformer condition monitoring and intelliteam ii automatic restoration system tools ecstar a distributed rulebased learning system agents and multiagent systems notion of agents agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving notion of multiagentsmultiagent system is defined as a network of agents which are loosely coupled working as a single entity like society for problem solving that an individual agent cannot solve software agents the key concept used in dps and mabs is the abstraction called software agents an agent is a virtual or physical autonomous entity that has an understanding of its environment and acts upon it an agent is usually able to communicate with other agents in the same system to achieve a common goal that one agent alone could not achieve this communication system uses an agent communication language a first classification that is useful is to divide agents into reactive agent a reactive agent is not much more than an automaton that receives input processes it and produces an output deliberative agent a deliberative agent in contrast should have an internal view of its environment and is able to follow its own plans hybrid agent a hybrid agent is a mixture of reactive and deliberative that follows its own plans but also sometimes directly reacts to external events without deliberation wellrecognized agent architectures that describe how an agent is internally structured are asmo emergence of distributed modules bdi believe desire intention a general architecture that describes how plans are made interrap a threelayer architecture with a reactive a deliberative and a social layer pecs physics emotion cognition social describes how those four parts influences the agents behavior soar a rulebased approach challenges the challenges in distributed ai are 1how to carry out communication and interaction of agents and which communication language or protocols should be used 2how to ensure the coherency of agents 3how to synthesise the results among intelligent agents group by formulation description decomposition and allocation collective intelligence simulated reality swarm intelligence a bond and l gasser readings in distributed artificial intelligence morgan kaufmann san mateo ca 1988 brahim chaibdraa bernard moulin ren mandiau and p millot trends in distributed artificial intelligence artificial intelligence review 613566 1992 nick r jennings coordination techniques for distributed artificial intelligence foundations of distributed artificial intelligence pages 187210 1996 damien trentesaux philippe pesin and christian tahon distributed artificial intelligence for fms scheduling control and design support journal of intelligent manufacturing 116573589 2000 catterson v m davidson e m mcarthur s d j practical applications of multiagent systems in electric power systems european transactions on electrical power 222 235252 2012 hewitt carl and jeff inman novemberdecember 1991 dai betwixt and between from intelligent agents to open systems science ieee transactions on systems man and cybernetics volume 21 issue 6 pps 14091419 issn 00189472 shoham yoav leytonbrown kevin 2009 multiagent systems algorithmic gametheoretic and logical foundations new york cambridge university press isbn9780521899437 sun ron 2005 cognition and multiagent interaction new york cambridge university press isbn9780521839648 vlassis nikos 2007 a concise introduction to multiagent systems and distributed artificial intelligence san rafael ca morgan claypool publishers isbn9781598295269 grace david zhang honggang august 2012 cognitive communications distributed artificial intelligencedai regulatory policy and economics implementation john wiley sons press isbn9781119951506 