Distributed Artificial Intelligence

Distributed Artificial Intelligence (DAI) is a subfield of AI research dedicated to the development of solutions for complex problems, that are not easily solveable with classic algorithmical programs.

There are three main streams in DAI research

Parallel problem solving: mainly deals with how classic AI 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 agents, 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.

The key concept used in DPS and MABS is the abstraction called agent. An agent is a virtual (or physical) autonomous entity that has an understanding of its environment an 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.

A first classification that is useful is to divide agents into:

reactive agent - A reactive agent is not much more than an automata that receives input, processes it and produces and output.
deliberative agent - A deliberative agent in contrast should have an own internal view of its environment and is able to follow its own plans.
hybrid agent - A hybrid agent at last is a mixture of reactive and deliberative, that follows its own plans, but also sometimes directly reacts to external events without deliberation.

Well recognied agent architectures that describe how an agent is internally structured are:

Soar (an rule based approach)
BDI (Believe Desire Intention, a general architecture the 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 behaviour).

Another important thing about agents is the ability to communicate. Important Agent Communication Languages a(ACL) are FIPA ACL (Foundation for Intelligent Physical Agents, a standardization consortium) and KQML (Knowledge Query and Manipulation Language), that both rely on speech act theory developed by Searle 1960 and enhanced by Winograd and Flores in the 70s. Both languages are very similar and describe a set of performatives and their meaning (e.g. ask-one). the content of the performative is not standardized, but varies from system to system. To make agents understand each other they have to not only speak the same language, but also have a common ontology. An ontology describes what kind of things an agent can deal with and how they are related to each other. It's part of the agents knowledge base.

Important researchers in the area of agents are:

Jacques Ferber
Michael Wooldridge and Nick Jennings
Rao and Georgeff
Rosaria Conte
...

Many papers about agents and MAS can be found at CiteSeer (http://citeseer.nj.nec.com/cs )