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.
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.
- 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).
Important researchers in the area of agents are:
- Jacques Ferber
- Michael Wooldridge and Nick Jennings
- Rao and Georgeff
- Rosaria Conte
- ...
See also Collective intelligence