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

2026-09-13

AI Summary

该研究提出一种基于代理启发式学习的方法解决人体活动识别问题，无需梯度训练神经网络，通过记忆示例、形成规则和修正错误来实现高效且可解释的策略。

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{https://github.com/zhaxidele/ahl-ts-studio}.

Problem

• Human Activity Recognition
• Agentic Heuristic Learning
• Cognitive Learning

Innovation

• Agentic Heuristic Learning
• Human Activity Recognition
• Edge Deployment
• Executable Policy