metagoal

A metagoal is a concept used in artificial intelligence and other fields to describe a goal that governs or prioritizes other goals. Instead of a direct objective, a metagoal focuses on the process of achieving goals, or on how to select, order, or adapt sub-goals. For example, a robot might have a metagoal of "efficiency" which would influence how it chooses to complete its primary tasks. This metagoal would direct the robot to select the shortest path, use the least amount of energy, or complete tasks in the quickest order, even if those choices are not explicitly part of the original task's objective.

The concept of metagoals is particularly relevant in areas such as planning, decision-making, and reinforcement learning. In these contexts, a system might not just aim to achieve a specific outcome, but also to learn how to achieve outcomes more effectively over time. A metagoal could be to "minimize learning time," "maximize long-term reward," or "maintain stability." By setting these higher-level objectives, a system can develop more sophisticated strategies and adapt to changing environments or priorities.

Metagoals can also be seen as a form of meta-cognition, or "thinking about thinking." They represent a level of abstraction above the immediate tasks, allowing for a more strategic and overarching approach to problem-solving. This hierarchical structure of goals, with metagoals overseeing sub-goals, helps in managing complexity and achieving more robust and intelligent behavior.