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One of the many approaches to artificial intelligence is distributed artificial intelligence (DAI). It is used to for learning by means of complex learning methods, large-scale planning and decision making. It can use a wide range of computational resources in different areas. This means that it can easily process and analyze large amounts of data and resolve problems quickly.
There are many agents or autonomous learning nodes in such a system. These nodes are highly distributed and are independent of each other. Due to this, machine learning systems using distributed artificial intelligence are quite adaptable and reliable. This means that DAI systems do not have to be completely redeployed after any change to the data files given as input for the problem.
Distributed artificial intelligence uses a parallel system for computing. Many “nodes” or learning agents, independent of each other, are located at geographically diverse places. Parallel processing allows the system to use all computational resources to their fullest extent. Due to its immense processing power, huge data sets can be analyzed quickly, with each part being analyzed by a separate node. If a change is to be made in the data which is given to the system, the corresponding node is redeployed, and not the whole system.
The integration of the solutions is done by an effective communication system between the agents or nodes. This ensures that the processing is elastic. Unlike centralized AI system, the data in DAI systems do not have to be given to a single location. The dataset may be updated over time. The nodes can interact with each other regarding the solution dynamically and have skills necessary to achieve the solution. Thus, DAI is considered one of the best approaches to machine learning and artificial intelligence.