将大型语言模型与心理上有基础的因果推理模型增强为不确定性下的规划
Semanti Basu1, Moon Hwan Kim1, Semir Tatlidil2
1Computer Science, Brown University, Providence, RI, United States.
Frontiers in artificial intelligence
|February 16, 2026
概括
将人类因果模型与大型语言模型 (LLM) 整合起来,可以显著改善AI在不确定性下进行规划. 这种混合方法提高了对象组装和故障排除等复杂任务的决策能力.
科学领域:
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 大型语言模型 (LLM) 在模式识别方面表现出色,但在不确定性下难以做出决策.
- 人类的推理利用明确的因果模型来更好地解释,在不确定的情况下产生假设和推断.
研究的目的:
- 研究人类因果模型与LLMs的战略整合.
- 增强对象组装和故障排除任务的规划结果,以部分可观测的马尔科夫决策过程 (POMDPs) 为模型.
主要方法:
- 开发了一个交互式LLM代理来规划POMDP框架内的特定任务的行动.
- 引入了一个混合推理框架,将LLM信心分数与人类因果模型洞察力结合起来,用于最终的行动选择.
主要成果:
- 在三个最先进的法学士课程中,在任务规划奖励方面取得了显著的改善.
- 通过详细的模拟,展示了通过人类因果模型增强基线LLM规划者的有效性.
结论:
- 整合人类因果模型的混合推理框架为改善在不确定的规划场景中LLM绩效提供了有希望的方向.
- 拟议的方法提高了AI代理人的决策稳定性和任务成功率.
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