Multi-Resident Data Association in Smart Homes via MDP Modeling and Behavior Cloning

The paper introduces a new framework for multi-resident data association in smart homes, framing the problem as a Markov Decision Process and solving it with Behavior Cloning. Two policy architectures are proposed: a lightweight Transformer treating association as multi-label classification over event histories, and a fine-tuned Large Language Model that generates conditional text to leverage semantic knowledge. Experiments on three datasets show the approach outperforms existing baselines, especially for concurrent multi-resident events, and the LLM policy hints at cross-environment transfer potential.

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• llms
• reinforcement-learning
• fine-tuning
• benchmarks
• safety