AI agents handle dynamic environments by employing a combination of sensing, decision-making, and learning techniques. First, they gather information about their surroundings through sensors or data inputs. This allows them to perceive changes in the environment, such as moving objects, changing conditions, or user interactions. For instance, a robotic vacuum cleaner uses sensors to detect obstacles and adjust its path accordingly, ensuring efficient cleaning while adapting to room layouts or furniture positions.
Once the AI agent has gathered data, it processes this information to make decisions. This involves developing models or algorithms that can evaluate different scenarios and predict outcomes based on the current state of the environment. For example, in a self-driving car, the AI continuously assesses traffic conditions, road signs, and pedestrian movements to decide when to accelerate, brake, or change lanes. The ability to make quick, informed decisions is crucial when the environment constantly changes.
In addition to immediate sensing and decision-making, AI agents can improve their performance over time through learning. They can be designed to use reinforcement learning techniques, where they receive feedback based on their actions. An example is an AI agent in a game that learns strategies by playing multiple rounds, adjusting its approach based on previous successes or failures. This learning capability allows AI agents to adapt not only in real-time but also over longer periods, becoming more effective in handling complex and unpredictable environments.