You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition

Abstract:Human activity recognition (HAR) is usually framed as gradient-based training of neural networks. Agentic Heuristic Learning (AHL) Studio explores a complementary view inspired by human cognitive learning: people learn activities by remembering examples, forming rules, and repairing mistakes, not by backpropagating. This proposed tool implements AHL for HAR: a learning-time agent reasons over sensor protocols, proposes executable heuristic policies, records repair traces, and exports an LLM-free policy for edge deployment. We focus on the HAR benchmark family and provide an end-to-end workflow from dataset observation to edge-oriented export. On eleven HAR datasets evaluated so far, AHL policies reach strong executable-policy performance while remaining inspectable, editable, and replayable \footnote{this https URL}.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.16065 [cs.LG]

(or arXiv:2609.16065v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.16065 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3798063.3837307 Focus to learn more DOI(s) linking to related resources

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