One field of artificial intelligence is distributed artificial intelligence (DAI). It is used for learning , planning and decision making. It can use a wide range of computational resources in different areas. This means that it can easily analyze large amounts of data and solve problems quickly.
The main difference between Artificial Intelligence (AI) and Distributed Artificial Intelligence (DAI).
Distributed Artificial Intelligence (DAI) also called Decentralized Artificial Intelligence is a subfield of Artificial intelligence (AI) research devoted to the development of distributed solutions for problems.
DAI is closely related to and a predecessor of the field of Multi-Agent Systems.
The objectives of DAI are to solve the reasoning, planning, learning and perception problems of AI, especially if they require large data, by distributing the problem to autonomous processing nodes (agents).
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.
First, I think that DAI isn't an AI field but its extension.
The DAI's systems are complex and characterized by the distribution and decentralization of data and processing (Action/Perception or Communication).
The main difference between AI and DAI is that in DAI we expect simultaneous actions of several autonomous components (Robots, for instance) which implies: concurrency and parallelism.
A serious problem related to the previous considerations is the treatment of the action:
in AI it was validated directly by an immediate modification of the states' variables of the system.
in DAI this immediate correspondence and modification of the states' variables is to be avoided because it affects the inconsistency (coherence) of the system and the autonomy of its components.
See for more detail my paper (or the references of the Influence/Reaction Principle in the paper):
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