Distributed artificial intelligence

Distributed Artificial Intelligence (DAI) is a subfield of Artificial Intelligence that deals with the development and management of multi-agent systems. These systems consist of multiple interacting intelligent agents that can work independently or in collaboration to solve complex tasks.

Overview

Distributed Artificial Intelligence (DAI) is an approach to solving complex learning, reasoning, and decision-making problems. It is distinguished by its focus on the collective behavior of systems composed of multiple autonomous entities, known as agents. These agents, which can be either physical (robots) or virtual (software programs), are capable of exhibiting intelligent behavior in their own right, but can achieve greater problem-solving power when acting in concert.

History

The concept of DAI was first introduced in the late 1970s and early 1980s as researchers began to realize the limitations of individual AI systems in solving complex, real-world problems. The idea was to create systems that could harness the power of multiple AI agents, each with their own areas of expertise, to achieve a common goal.

Multi-Agent Systems

A key concept in DAI is the multi-agent system (MAS). In a MAS, multiple agents interact with each other to achieve individual or collective goals. These systems can be used to model and solve complex problems that are beyond the capabilities of individual agents.

Techniques

DAI employs a variety of techniques to enable agent collaboration, including agent communication languages, negotiation, cooperative distributed problem solving, and swarm intelligence.

Applications

DAI has been applied in a wide range of fields, including Computer Science, Robotics, Economics, and Social Sciences. It has been used to model economic markets, manage traffic flow, coordinate search and rescue missions, and much more.

See Also

• Artificial Intelligence
• Multi-agent system
• Swarm Intelligence
• Agent (computing)

References

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