AI agents use reasoning to achieve goals by processing information, drawing logical conclusions, and making decisions based on their environment and objectives. Reasoning allows these agents to analyze different situations, predict outcomes, and select the most effective actions to meet their goals. This typically involves the use of algorithms that integrate both deductive and inductive reasoning, enabling the agent to work with known data and generalize from experiences to inform future actions.
For instance, consider an AI agent designed for autonomous driving. This agent must constantly assess its surroundings, including the positions of other vehicles, pedestrians, and traffic signals. Through reasoning, the agent can infer what these detected objects are likely to do next—such as predicting a pedestrian might cross the street—based on historical data and current behavior patterns. By weighing the safety of various maneuvers, the agent can decide the best course of action, such as slowing down or yielding. Here, reasoning plays a crucial role in ensuring that the agent responds appropriately to dynamic scenarios.
Another example is in customer support chatbots. These agents can use reasoning to understand user inquiries and provide relevant responses. For instance, if a customer asks about a refund policy, the chatbot first identifies the context (refund requests), connects that with the stored information about policies, and then crafts a logical response based on the user's situation. This involves parsing the user's input, recognizing key terms, and applying logical rules to ascertain the best response. Through reasoning, the bot can also handle follow-up questions or clarify ambiguities, ensuring a more effective interaction that ultimately serves its goal of aiding the customer.