Distributed artificial intelligence (DAI) is a subfield of artificial intelligence

Artificial intelligence

Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

 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

Multi-agent system

A multi-agent system is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems that are difficult or impossible for an individual agent or a monolithic system to solve...

.

Objective

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
  Multiprocessor
  Computer system having two or more processing units each sharing main memory and peripherals, in order to simultaneously process programs.Sometimes the term Multiprocessor is confused with the term Multiprocessing....
  
   systems and clusters of computers can be used to speed up calculation.
• Distributed problem solving (DPS): the concept of agent
  Intelligent agent
  In artificial intelligence, an intelligent agent is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals . Intelligent agents may also learn or use knowledge to achieve their goals...
  
  , autonomous entities that can communicate with each other, was developed to serve as an abstraction
  Abstraction
  Abstraction is a process by which higher concepts are derived from the usage and classification of literal concepts, first principles, or other methods....
  
   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
  Macroscopic
  The macroscopic scale is the length scale on which objects or processes are of a size which is measurable and observable by the naked eye.When applied to phenomena and abstract objects, the macroscopic scale describes existence in the world as we perceive it, often in contrast to experiences or...
  
   level but also at micro
  Micro
  Micro is a prefix in the metric system denoting a factor of 10-6 . Confirmed in 1960, the prefix comes from the Greek , meaning "small".The symbol for the prefix is the Greek letter μ...
  
   level, as it is in many social simulation
  Social simulation
  Social simulation is a research field that applies computational methods to study issues in the social sciences. The issues explored include problems in sociology, political science, economics, anthropology, geography, archaeology and linguistics ....
  
   scenarios.

Agents and Multi-agent systems

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

The key concept used in DPS and MABS is the abstraction called software agent

Software agent

In computer science, a software agent is a piece of software that acts for a user or other program in a relationship of agency, which derives from the Latin agere : an agreement to act on one's behalf...

s. 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
  Deliberative agent
  Deliberative agent is a sort of software agent used mainly in multi-agent system simulations. According to Wooldridge's definition, a deliberative agent is "one that possesses an explicitly represented, symbolic model of the world, and in which decisions are made via symbolic reasoning".Compared...
  
   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
  Soar (cognitive architecture)
  Soar is a symbolic cognitive architecture, created by John Laird, Allen Newell, and Paul Rosenbloom at Carnegie Mellon University, now maintained by John Laird's research group at the University of Michigan. It is both a view of what cognition is and an implementation of that view through a...
  
   (a rule-based approach)
• BDI
  BDI software agent
  The Belief-Desire-Intention software model is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming...
  
   (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

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

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

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
• Sun, Ron, (2005). Cognition and Multi-Agent Interaction. New York: Cambridge University Press. ISBN 978-0521839648