Multiagent systems are a pivotal area of study in artificial intelligence (AI) and robotics, focusing on systems where several agents interact with each other and the environment. These agents can be software entities (like bots in a digital setting) or physical robots acting in the real world. The complexity of multiagent systems arises from the interactions between agents, which can be cooperative, competitive, or a mix of both. Such systems are studied to understand how individual agent behaviors lead to emergent group behavior, optimize collective problem-solving, and negotiate solutions in scenarios where agents have differing objectives. Applications of multiagent systems range from automated trading systems in financial markets, to coordination of autonomous vehicles, and even to modeling social and economic systems.
Historical Overview: The concept of multiagent systems gained prominence in the 1970s and 1980s with the rise of distributed computing and the realization that many complex systems could be modeled and managed more effectively as a collection of interacting agents.
Key Contributors: While no single individual can be credited with the inception of multiagent systems, this field has been significantly advanced by researchers in AI and computer science, including Michael Wooldridge and Katia Sycara, who have contributed to the theoretical foundations and practical applications of multiagent systems.