Stay Within Bounds: Motivation as Regulatory Control in Autonomous Agents
TL;DR for operators An autonomous agent often has to do more than maximize task reward. It may need to keep battery charge, temperature, latency, physical safety, communication quality, or other critical variables within acceptable ranges while deciding what to sense, remember, explore, and do next. Galus and Starzyk formalize a cognitive architecture around that premise. In their Motivated Emotional Mind model, programs are selected by minimizing expected discounted future violations of tolerated internal ranges rather than by maximizing an externally supplied reward.1 Regulatory pressure also feeds into learning and exploration. Memory representations compete using sensory similarity, internal context, and learned valence, then reconstruct sensory content that is fused with incoming evidence. If no known program is predicted to be adequate, the system enters a procedural gap and generates candidates instead of continuing to choose among an insufficient repertoire. ...