Distributed artificial intelligence

Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, planning, and decision-making problems.

DAI systems consist of autonomous learning processing nodes (agents), that are distributed, often at a very large scale.

Furthermore, DAI systems are built to be adaptive to changes in the problem definition or underlying data sets due to the scale and difficulty in redeployment.

The objectives of Distributed Artificial Intelligence are to solve the reasoning, planning, learning and perception problems of artificial intelligence, especially if they require large data, by distributing the problem to autonomous processing nodes (agents).

To reach the objective, DAI requires: There are many reasons for wanting to distribute intelligence or cope with multi-agent systems.

The challenges in Distributed AI are: Areas where DAI have been applied are: DAI integration in tools has included: Notion of Agents: Agents can be described as distinct entities with standard boundaries and interfaces designed for problem solving.