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This paper introduces a human-simulation-based framework that enables LLM-driven AI systems to autonomously formulate questions and define tasks by reasoning about internal states, environmental observations, and interactions with other agents. By treating question formation as a distinct decision process, the framework integrates internal, environment-aware, and inter-agent-aware prompting scopes to broaden cognitive coverage. Experimental results in a multi-agent simulation demonstrate that environment-aware and inter-agent-aware prompting significantly reduce "no-eat" events, indicating improved adaptability and decision quality.
LLMs can proactively identify and address problems in dynamic environments by autonomously formulating questions, leading to more effective decision-making.
Large language model (LLM)-driven AI systems are increasingly important for autonomous decision-making in dynamic and open environments. However, most existing systems rely on predefined tasks and fixed prompts, limiting their ability to autonomously identify what problems should be solved when environmental conditions change. In this paper, we propose a human-simulation-based framework that enables AI systems to autonomously form questions and set tasks by reasoning over their internal states, environmental observations, and interactions with other AI systems. The proposed method treats question formation as a first-class decision process preceding task selection and execution, and integrates internal-driven, environment-aware, and inter-agent-aware prompting scopes to progressively expand cognitive coverage. In addition, the framework supports learning the question-formation process from experience, allowing the system to improve its adaptability and decision quality over time. xperimental results in a multi-agent simulation environment show that environment-aware prompting significantly reduces no-eat events compared with the internal-driven baseline, and inter-agent-aware prompting further reduces cumulative no-eat events by more than 60% over a 20-day simulation, with statistically significant improvements (p<0.05).