Autonomous agents are integral to both theoretical and practical AI, embodying systems or software entities that act independently to perform tasks, make decisions, or achieve goals without constant human guidance. These agents are designed to perceive their environment through sensors, interpret the sensory information, and act upon the environment using actuators to achieve specific objectives. The significance of autonomous agents lies in their ability to adapt to changes, learn from interactions, and make autonomous decisions, which are crucial for applications ranging from autonomous vehicles and industrial robotics to intelligent software agents in complex simulations and virtual environments. Their development involves a multidisciplinary approach, incorporating insights from computer science, robotics, cognitive science, and artificial intelligence, focusing on aspects like decision-making, learning, adaptation, and interaction with humans and other agents.
Historical overview: The concept of autonomous agents gained prominence in the late 20th century, with the term and its foundational ideas becoming more widespread in academic and research circles in the 1980s and 1990s as robotics, AI, and computer science advanced.
Key contributors: While many researchers and developers have contributed to the field of autonomous agents, Rodney Brooks and his work on behavior-based robotics in the late 1980s and early 1990s at MIT are often cited. Brooks' approach to building robots capable of navigating real-world environments autonomously without complex models or heavy computation laid the groundwork for many modern autonomous systems.