Reactive multi-agent systems (RMAS) are collections of autonomous agents that respond to changes in their environment in real-time. These agents operate independently but are designed to act based on specific stimuli or events without requiring extensive planning or deliberation. The focus is on quick reactions and adaptability, which makes RMAS useful in dynamic environments where conditions may shift frequently and unpredictably.
A defining feature of RMAS is their ability to process sensory information and make decisions based on that information. For example, in a robotic swarm tasked with search and rescue operations, each robot collects data about its surroundings and reacts to obstacles by altering its path. They may also coordinate among themselves by sharing information about discovered paths or hazards. This kind of system contrasts with more deliberative multi-agent systems, where agents plan out their actions in advance, possibly leading to delays when quick responses are crucial.
Real-world applications of RMAS are vast and diverse. One common example is traffic management systems, where individual vehicles or traffic signals adjust their operations based on current traffic conditions. Another instance is in automated warehouses, where robotic agents dynamically navigate to fulfill orders while avoiding collisions and optimizing routes in real-time. These systems illustrate how RMAS can enhance efficiency and safety by enabling agents to react promptly to their operational context, creating a more intelligent and responsive environment.