👉 Planner computing is an approach to artificial intelligence that focuses on creating systems capable of reasoning about their own computational processes and goals, enabling them to plan actions to achieve desired outcomes. Unlike traditional AI systems that often rely on predefined rules or data-driven learning, planner computing involves the system generating plans autonomously to solve complex problems. This is achieved by modeling the problem environment, identifying goals, and systematically exploring potential sequences of actions to reach those goals efficiently. Planners use techniques from symbolic AI, such as search algorithms and logical reasoning, to navigate through possible solutions, often incorporating constraints and optimizing for criteria like time, cost, or resource usage. This capability is particularly valuable in dynamic and uncertain environments where adaptability and strategic foresight are crucial, such as in robotics, autonomous vehicles, and complex decision-making systems.
