A Semantic-Prior-Guided AI Framework for Collaborative Environment Understanding and Robust Agent Decision Making

Abstract

This study addresses key challenges in agent decision making within complex environments, including missing semantic structures, the disconnection between perception and reasoning, and limited behavioral consistency. It proposes a collaborative learning framework that integrates semantic priors for autonomous behavior modeling and environment understanding. The method builds a unified semantic enhanced state representation by encoding scene semantics, object relations, and task logic into learnable prior structures, which provide high level semantic constraints for policy generation. A multimodal environment understanding module then combines visual, contextual, and dynamic signals to produce structured abstractions of key objects, spatial layouts, and semantic conditions. On this basis, a structured dynamic model is constructed to capture the evolution of semantic states under actions, forming a unified reasoning pipeline that links perception, cognition, and behavior. The framework further employs a policy generation module guided by semantic consistency, enabling the agent to produce coordinated, robust, and interpretable actions driven jointly by semantic priors and environment understanding. A comprehensive experimental system is developed, including comparison experiments, hyperparameter sensitivity experiments, environment perturbation experiments, and semantic prior ablation experiments, to evaluate the role of semantic priors in improving task success rate, path efficiency, semantic abstraction, and behavioral diversity. The results show that collaborative modeling of semantics and behavior enhances decision stability, structured reasoning, and cross scene adaptability in complex environments, providing a scalable methodological foundation for building autonomous agents with coherent cognitive structures.