Empowerment (artificial intelligence)

In the context of artificial intelligence (AI) and robotics, empowerment refers to the degree to which an agent (e.g., a robot or AI system) believes its actions can influence its future sensory state. It's a quantitative measure of an agent's ability to control its environment and achieve desired outcomes. High empowerment suggests the agent has significant control and can reliably predict and shape its future experiences. Low empowerment, conversely, indicates a limited ability to affect the environment.

Empowerment is typically calculated based on information-theoretic principles, often involving the quantification of mutual information between an agent's actions and its future observations. The higher the mutual information, the greater the agent's empowerment. Different formulations exist, depending on the specific definition of actions and observations, and the time horizon considered. Some approaches focus on immediate effects, while others consider long-term consequences.

The concept of empowerment is closely related to concepts such as intrinsic motivation, curiosity-driven learning, and active inference. An agent driven by empowerment seeks to maximize its ability to influence its environment, leading it to explore and learn about the world in a self-directed manner. This can be a powerful mechanism for developing robust and adaptable AI systems, as it encourages agents to discover useful skills and strategies without explicit external rewards.

Empowerment can be used as a reward signal in reinforcement learning, guiding the agent to learn policies that increase its control over its surroundings. It can also be used as a criterion for selecting actions, even in the absence of pre-defined goals. This allows agents to engage in exploratory behavior and discover new possibilities. Furthermore, empowerment has implications for understanding the emergence of agency and autonomy in artificial systems. By maximizing their empowerment, agents become more capable of acting independently and achieving their own objectives.