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Mining user behavior patterns based on reinforcement learning algorithm to optimize service robot interaction strategy
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DOI：10.1051/meca/2026002.png)
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Abstract
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In order to resolve the challenges of reduced adaptability and generalization stemming from complicated user behavior and sparse feedback in service robot interactions, this paper presents a reinforcement learning-based approach to user behavior pattern mining and policy optimization. This approach integrates Bayesian belief updates and automata learning with counterexamples to unify intent modeling and policy iteration: dynamic intent reasoning promotes multi-scale exploration under sparse rewards; and counterexample reasoning reconstructs the rewards function to bolster policy generalization. Experiments revealed that the method led to mean latency standard deviation of 0.031, a task completion rate was 78.14%, and behavior recognition accuracy 0.9, which can contribute to more capable policies and improve user satisfaction, while providing a reference for the design of complex interaction strategies.
Keywords:
Service robot interaction
reinforcement learning
user behavior pattern modeling
dynamic intent recognition
strategy adaptive optimization
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1.2
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23
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0
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