Distributed Artificial Intelligence (DAI)
Distributed Artificial Intelligence (DAI) is a branch of artificial intelligence that concentrates on the design, theory, and application of AI systems composed of multiple interacting intelligent agents. These agents, which can be both software and hardware entities, are designed to solve problems collaboratively or compete in a shared environment.
DAI systems are characterized by their decentralized nature, absence of a central controlling authority, and the capability of agents to operate autonomously, communicate with each other, and coordinate their actions to achieve a common goal or a set of individual goals.
This approach allows DAI systems to tackle complex, large-scale problems by dividing them into smaller, more manageable tasks distributed among multiple agents. DAI encompasses a range of concepts from multi-agent systems, parallel processing, and collaborative AI, focusing on how distributed collections of agents can be orchestrated to exhibit intelligent behavior.
One example of DAI in action is in the field of smart grids, where multiple agents representing different components of the power system (such as consumers, producers, and storage systems) work together to optimize energy distribution and consumption in real-time. Each agent autonomously manages its objectives, such as minimizing costs or maximizing energy efficiency, while coordinating with other agents to maintain the stability and reliability of the power supply.
Another example is in the field of autonomous vehicles, where distributed AI allows a fleet of vehicles to communicate and coordinate with each other to improve traffic flow, reduce congestion, and increase safety. Each vehicle acts as an intelligent agent, making decisions based on its own sensors as well as information shared by other vehicles and infrastructure, demonstrating how DAI can be applied to develop complex, adaptive systems capable of solving real-world problems.