arXiv:2605.27929v1 Announce Type: cross Abstract: Active sensing links behavior and learning through an action-perception loop: actions determine the observations used to update internal predictive models of perception, which subsequently guide the next actions. Predictive-coding frameworks provide a natural way to model this process, since internal representations are continuously updated to predict future observations. Here, we ask how exploratory and exploitative behavioral strategies shape these internal predictive representations. We build an online learning agent in a tree-like maze with
This research emerges as AI development increasingly focuses on autonomous and adaptive systems, seeking to better understand the fundamental mechanisms of intelligence for more robust and generalizable AI.
Understanding how exploratory behavior shapes predictive representations offers a pathway to designing more efficient and human-like AI agents capable of learning in complex, dynamic environments.
This research provides a theoretical framework and computational model for linking active sensing and predictive coding, enhancing our understanding of how AI agents can organically learn from environmental interactions.
- · AI researchers
- · Robotics developers
- · Autonomous system designers
- · AI models reliant solely on static datasets
- · Systems with limited active learning capabilities
Improved theoretical models for active learning in AI agents.
Development of more adaptive and efficient AI algorithms for navigation and decision-making in unknown environments.
Acceleration in the creation of general-purpose AI agents that can rapidly learn and adapt in real-world scenarios without extensive pre-training.
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Read at arXiv cs.LG