Скачать или смотреть The Five types of AI Agents

There are five main types of AI agents, each designed with increasing levels of complexity and adaptability. Simple Reflex Agents operate on basic if-then rules and respond to immediate inputs, as demonstrated by a rule-based BankBot for common user queries. Model-Based Reflex Agents maintain an internal state and use it for decision-making, like a smart thermostat adjusting to both temperature and time of day. Goal-Based Agents go beyond reactivity by planning steps to achieve a specific user goal, such as an online assistant guiding shoppers through purchasing or returns. Utility-Based Agents evaluate multiple possible actions and select the one with the highest utility score, making them useful in marketing strategy selection. Learning Agents represent the most advanced form, improving over time using feedback and reinforcement learning concepts like Q-learning. Each type of agent is illustrated with a working Python example, ranging from hard-coded logic to dynamically learned behaviors. These agents can interact in real-time with users, respond to changing inputs, and even optimize future decisions. The examples showcase how agents handle user goals, adapt to feedback, and make increasingly intelligent choices. Collectively, they demonstrate the progression from simple rule-following systems to adaptive AI capable of learning and reasoning.