You Don't Need To Train: Agentic Heuristic Learning Studio for Executable Human Activity Recognition
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Computer Science > Machine Learning
Title: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}.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.16065 [cs.LG] |
| (or arXiv:2609.16065v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.16065
arXiv-issued DOI via DataCite
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| Related DOI: | https://doi.org/10.1145/3798063.3837307
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