Distributed artificial intelligence

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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.


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.

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 communicate 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:

  • Soar (a rule-based approach)
  • 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).

Basic Disadvantages[edit]

The Basic Problems of 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.

Real time Applications[edit]


  1. Problem Solving
  2. Multi-Agent Simulation
  3. Construction of Synthetic Worlds
  4. Collective Robotics
  5. Robotic clusters
  6. Kenetic Program Design

See also[edit]


  1. ^ Ferber, Jacques. Multi-Agent System: An Introduction to Distributed Artificial Intelligence. 

Further reading[edit]