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Distributed Artificial Intelligence in Space
Distributed Artificial Intelligence in Space
Published: 2017/04/26
Channel: Software Engineering Institute | Carnegie Mellon University
Distributed artificial intelligence
Distributed artificial intelligence
Published: 2016/01/22
Channel: WikiAudio
The Maze Runners - Distributed Artificial Intelligence [Android]
The Maze Runners - Distributed Artificial Intelligence [Android]
Published: 2015/02/25
Channel: Menelaos Kotsollaris
Distributed Artificial Intelligence, prey-predator
Distributed Artificial Intelligence, prey-predator
Published: 2015/12/06
Channel: Mohamed Taher Ben Torkia
Distributed Deep Learning for AI developers
Distributed Deep Learning for AI developers
Published: 2017/08/08
Channel: This Week On developerWorks
Distributed Artificial Intelligence
Distributed Artificial Intelligence
Published: 2017/11/12
Channel: Zahra M.M.A. Sadiq
Artificial Intelligence will replace your soul
Artificial Intelligence will replace your soul
Published: 2017/08/07
Channel: RichieFromBoston
Foundations of Distributed Artificial Intelligence Sixth Generation Computer Technologies
Foundations of Distributed Artificial Intelligence Sixth Generation Computer Technologies
Published: 2016/11/20
Channel: carolyn
Distributed Training in the Cloud
Distributed Training in the Cloud
Published: 2017/10/27
Channel: Google Cloud
Foundations of Distributed Artificial Intelligence Sixth Generation Computer Technologies
Foundations of Distributed Artificial Intelligence Sixth Generation Computer Technologies
Published: 2016/08/21
Channel: keene
The Role of Multi-Agent Learning in Artificial Intelligence Research at DeepMind
The Role of Multi-Agent Learning in Artificial Intelligence Research at DeepMind
Published: 2017/03/10
Channel: The Alan Turing Institute
CELL ICO - Distributed Artificial Intelligence | Meine Meinung!
CELL ICO - Distributed Artificial Intelligence | Meine Meinung!
Published: 2018/01/15
Channel: Tomoon
Distributed Intelligence Demos: Setup and Environment Overview
Distributed Intelligence Demos: Setup and Environment Overview
Published: 2016/07/22
Channel: ItronSmartMedia
Artificial Intelligence is going to replace programmers
Artificial Intelligence is going to replace programmers
Published: 2017/04/25
Channel: Ivan on Tech
Why CELL will change the Cryptocurrency World (A.I. Revolution)
Why CELL will change the Cryptocurrency World (A.I. Revolution)
Published: 2018/01/16
Channel: CELL Whale
Artificial Intelligence And Robotics Documentary 2017
Artificial Intelligence And Robotics Documentary 2017
Published: 2017/08/09
Channel: world documentary
Blockchain Consensus Algorithms and Artificial Intelligence
Blockchain Consensus Algorithms and Artificial Intelligence
Published: 2017/10/10
Channel: Siraj Raval
Bottos-New Chinese Coin | NEO Co-Founder on The Team | Artificial Intelligence
Bottos-New Chinese Coin | NEO Co-Founder on The Team | Artificial Intelligence
Published: 2017/12/10
Channel: Chico Crypto Consultants
Shoes.com & Sentient — World’s First AI Powered Shopping Experience
Shoes.com & Sentient — World’s First AI Powered Shopping Experience
Published: 2015/11/17
Channel: Sentient Technologies
Normalize Distribution - Artificial Intelligence for Robotics
Normalize Distribution - Artificial Intelligence for Robotics
Published: 2012/02/19
Channel: Udacity
Distributed Artificial Intelligence Architecture and Modelling First Australian Workshop on DAI Canb
Distributed Artificial Intelligence Architecture and Modelling First Australian Workshop on DAI Canb
Published: 2017/01/20
Channel: rochelle
Ex Machina | Examining Our Fear of Artificial Intelligence
Ex Machina | Examining Our Fear of Artificial Intelligence
Published: 2015/04/10
Channel: Creators
Distributed Artificial Intelligence Architecture and Modelling First Australian Workshop on DAI Canb
Distributed Artificial Intelligence Architecture and Modelling First Australian Workshop on DAI Canb
Published: 2016/12/18
Channel: liesa
How Will Artificial Intelligence Affect Your Life | Jeff Dean | TEDxLA
How Will Artificial Intelligence Affect Your Life | Jeff Dean | TEDxLA
Published: 2017/01/18
Channel: TEDx Talks
What Cisco Machine Learning & Artificial Intelligence Can do for the Network with JP Vasseur
What Cisco Machine Learning & Artificial Intelligence Can do for the Network with JP Vasseur
Published: 2017/07/05
Channel: Tech Field Day
Trent McConaghy - Blockchains for Artificial Intelligence
Trent McConaghy - Blockchains for Artificial Intelligence
Published: 2017/07/26
Channel: PyData
High Performance Hardware for Distributed Deep Learning
High Performance Hardware for Distributed Deep Learning
Published: 2017/04/15
Channel: RichReport
Artificial Intelligence: FICO predictions for 2018
Artificial Intelligence: FICO predictions for 2018
Published: 2017/12/25
Channel: Aban Tech
Free Download Distributed Artificial Intelligence Volume II Research Notes in Artificial Intelligenc
Free Download Distributed Artificial Intelligence Volume II Research Notes in Artificial Intelligenc
Published: 2017/03/11
Channel: M. Gotzon
AI: What
AI: What's Working, What's Not
Published: 2017/12/08
Channel: a16z
Download Distributed Artificial Intelligence Volume IPdf
Download Distributed Artificial Intelligence Volume IPdf
Published: 2017/03/03
Channel: L. domitica
Sentient: Artificial Intelligence at Scale
Sentient: Artificial Intelligence at Scale
Published: 2014/12/09
Channel: Sentient Technologies
Normalize Distribution Solution - Artificial Intelligence for Robotics
Normalize Distribution Solution - Artificial Intelligence for Robotics
Published: 2012/02/19
Channel: Udacity
Distributed Deep Learning
Distributed Deep Learning
Published: 2017/02/20
Channel: Amazon Web Services
Distence - Distributed Intelligence for Industrial Applications
Distence - Distributed Intelligence for Industrial Applications
Published: 2016/08/07
Channel: Distence
Free Download Distributed Artificial Intelligence Volume I
Free Download Distributed Artificial Intelligence Volume I
Published: 2017/03/11
Channel: M. Gotzon
Download Distributed Artificial Intelligence Volume II Research Notes in Artificial Intelligenc Volu
Download Distributed Artificial Intelligence Volume II Research Notes in Artificial Intelligenc Volu
Published: 2017/03/03
Channel: L. domitica
Expert system in Artificial intelligence in hindi
Expert system in Artificial intelligence in hindi
Published: 2017/04/30
Channel: Last moment tuitions
Accelerate Artificial Intelligence with ARM Processors
Accelerate Artificial Intelligence with ARM Processors
Published: 2017/06/08
Channel: Arm
AWS re:Invent 2017: GPS: Artificial Intelligence, Machine Learning, Deep Learning: C (GPSBUS201)
AWS re:Invent 2017: GPS: Artificial Intelligence, Machine Learning, Deep Learning: C (GPSBUS201)
Published: 2017/11/29
Channel: Amazon Web Services
PipelineAI:  High Performance Distributed TensorFlow AI + GPU + Model Optimizing Predictions
PipelineAI: High Performance Distributed TensorFlow AI + GPU + Model Optimizing Predictions
Published: 2017/08/15
Channel: PipelineAI
Distributed Intelligence in IoT Deployments
Distributed Intelligence in IoT Deployments
Published: 2015/04/17
Channel: Oracle Internet of Things
"The Biological Path Towards Strong AI" by Matt Taylor
"The Biological Path Towards Strong AI" by Matt Taylor
Published: 2017/09/30
Channel: Strange Loop
[Webinar] How to Drive Up Conversions Across Your Entire Funnel Using AI
[Webinar] How to Drive Up Conversions Across Your Entire Funnel Using AI
Published: 2017/12/18
Channel: Sentient Technologies
Agent Coordination – Part 2
Agent Coordination – Part 2
Published: 2017/01/23
Channel: European Data Science Academy
Friuno-Smart Automation System with Artificial Intelligence
Friuno-Smart Automation System with Artificial Intelligence
Published: 2017/11/25
Channel: Godwin Rose Samuel W
Agent Coordination – Part 3
Agent Coordination – Part 3
Published: 2017/01/23
Channel: European Data Science Academy
Singularity is Near...      2016 Documentary
Singularity is Near... 2016 Documentary
Published: 2016/05/25
Channel: Enigmas Of The Universe
Matrix ICO: Ethereum Killer via Artificial Intelligence (AI)?
Matrix ICO: Ethereum Killer via Artificial Intelligence (AI)?
Published: 2017/12/23
Channel: Decentralized TV
Ray Kurzweil Remembers Marvin Minsky
Ray Kurzweil Remembers Marvin Minsky
Published: 2016/04/04
Channel: Sentient Technologies
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WIKIPEDIA ARTICLE

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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 Systems.

Definition[edit]

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 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[edit]

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 require:

  • 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 multi-agent systems. Mainstreams 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.
  • Multi-Agent 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[edit]

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 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[edit]

Multi-agent systems and distributed problem solving are the two main DAI approaches. There are numerous applications and tools.

Approaches[edit]

Two types of DAI has emerged:

  • In Multi-agent 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[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.

DAI can apply a bottom-up approach to AI, similar to the subsumption architecture as well as the traditional top-down approach of AI. In addition, DAI can also be a vehicle for emergence.

Applications[edit]

Areas where DAI have been applied are:

  • Electronic commerce, e.g. for trading strategies the DAI system learns financial trading rules from subsamples of very large samples of financial data
  • Networks, e.g. in telecommunications the DAI system controls the cooperative resources in a WLAN network http://dair.uncc.edu/projects/past-projects/wlan-resource
  • Routing, e.g. model vehicle flow in transport networks
  • 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 Life, the study of simulated life
  • Electric power systems, e.g. COndition Monitoring Multi-Agent System (COMMAS) applied to transformer condition monitoring, and IntelliTEAM II Automatic Restoration System [1]

Tools[edit]

  • ECStar, a distributed rule-based learning system

Agents and Multi-agent systems[edit]

Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving.

Notion of Multi-Agents:Multi-Agent 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[edit]

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.

Well-recognized 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 three-layer 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 rule-based approach)

Challenges[edit]

The challenges in Distributed AI are:

1.How to carry out communication and interaction of agents and which communication language or protocols should be used.

2.How to ensure the coherency of agents.

3.How to synthesise the results among 'intelligent agents' group by formulation, description, decomposition and allocation.

See also[edit]

References[edit]

  1. ^ Catterson, Victoria M.; Davidson, Euan M.; McArthur, Stephen D. J. (2012-03-01). "Practical applications of multi-agent systems in electric power systems". European Transactions on Electrical Power. 22 (2): 235–252. doi:10.1002/etep.619. ISSN 1546-3109. 
  • [1] A. Bond and L. Gasser. Readings in Distributed Artificial Intelligence. Morgan Kaufmann, San Mateo, CA, 1988.
  • [2] Brahim Chaib-Draa, Bernard Moulin, René Mandiau, and P Millot. Trends in distributed artificial intelligence.

Artificial Intelligence Review, 6(1):35-66, 1992.

  • [3] Nick R Jennings. Coordination techniques for distributed artificial intelligence. Foundations of distributed artificial

intelligence, pages 187-210, 1996.

  • [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.

  • [5] Catterson, V. M., Davidson, E. M., & McArthur, S. D. J. Practical applications of multi-agent systems in electric power systems. European Transactions on Electrical Power, 22(2), 235–252. 2012

Further reading[edit]

  • 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
  • Shoham, Yoav; Leyton-Brown, Kevin (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. New York: Cambridge University Press. ISBN 978-0-521-89943-7. 
  • Sun, Ron, (2005). Cognition and Multi-Agent Interaction. New York: Cambridge University Press. ISBN 978-0-521-83964-8
  • Vlassis, Nikos (2007). A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. San Rafael, CA: Morgan & Claypool Publishers. ISBN 978-1-59829-526-9. 
  • Grace, David; Zhang, Honggang (August 2012). Cognitive Communications: Distributed Artificial Intelligence(DAI), Regulatory Policy and Economics, Implementation. John Wiley & Sons Press. ISBN 978-1-119-95150-6

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