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The study examines the agency concept, defined as the ability of a system to direct the results towards an objective, and argues that determining whether a system exhibits the agency depends inherently on the reference framework used for the evaluation. When analyzing the essential properties of the agency, the study maintains that any evaluation of the agency should consider the perspective from which it is measured, which implies that the agency is not an absolute attribute, but varies with different reference cadres. This perspective has significant implications for fields such as reinforcement learning, where the understanding and definition of the agency is crucial.
Google Deepmind researchers and Alberta University examine the agency concept, the ability of a system to direct the results towards a goal, through the reinforcement learning lens. They argue that evaluating the agency depends inherently as Marco, which means that it must be evaluated in relation to a specific frame of reference. The analysis of the essential properties of the agency proposed in previous studies shows that each property depends on the table. They conclude that any fundamental study of the agency must take into account this dependency of the framework and discuss the implications for reinforcement learning.
The study postulates that determining whether an agency exhibits inherently depends on the chosen frame of reference. The authors argue that the four essential properties of the agency (individuality, source of action, regulations and adaptation) depend on arbitrary commitments that define this frame of reference. For example, establishing the limit of a system (individuality) or identifying significant behavior led by objectives (regulations) requires subjective decisions, concluding that the agency cannot be universally measured without considering these contextual paintings.
This perspective has significant implications for RL. In RL, agents are designed to make decisions to achieve specific objectives. Recognizing that the agency depends on the framework suggests that evaluating the behavior and effectiveness of an RL agent can vary according to the chosen frame of reference. Consequently, the development of a fundamental science of the agency requires recognizing and incorporating the dependence of the framework in the analysis and design of RL systems.
The agency, the ability of a system to direct the results towards a goal, is a central theme in fields such as biology, philosophy, cognitive science and artificial intelligence. Determining if a system exhibits agency is a challenge, since it often depends on the perspective from which it is evaluated. This perspective, or frame of reference, influences how we interpret the actions and objectives of a system. For example, a thermostat can be seen as directed by the objective, with the objective of maintaining an established temperature, but this interpretation depends on how we define the direction of objectives. Similarly, a rock rolling through a hill could be considered that it has the objective of reaching the bottom, but this is also an interpretation dependent on the perspective.
The agency is closely linked to intelligence, but the relationship between the two is not yet understood. Explore this relationship through the lens of the dependency of the frame offers a new border to understand the central concepts in reinforcement learning. A next natural step is to develop a precise definition of frames of reference of agents and formal evidence that supports the statement that the agency depends on the framework. Choosing an appropriate frame of reference is crucial, and it is not clear what principles of frame selection are defensible and what implications these principles have for the study of agents. Investigating, formalizing and comparing different principles to select Reference Marcos is an important address for future research.
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Sana Hassan, a consulting intern in Marktechpost and double grade student in Iit Madras, passionate to apply technology and ai to address real world challenges. With great interest in solving practical problems, it provides a new perspective to the intersection of ai and real -life solutions.
