用大模型+传统规划器，让智能体更像真人应对未知情境。
LLM Reasoner and Automated Planner: A new NPC approach
- 大模型负责生成类人决策，规划器确保行动逻辑正确
- 组合方案在未预设场景中仍能生成合理行为序列
- 适合需要自然行为的模拟系统，如教育仿真
在需要智能体模拟合理人类行为的领域（如形成性模拟），传统方法如行为树面临显著挑战。尽管大语言模型（LLMs）不总能给出最优解，但通常能提供合理且类人的回应。本文利用这一特性，提出一种新架构：将大语言模型用于决策，结合经典自动化规划器生成可靠的行为计划。该组合使智能体具备在各种情境下做出决策的能力，即使这些情境在设计阶段未被预见。
原文摘要 · Abstract (English)
In domains requiring intelligent agents to emulate plausible human-like behaviour, such as formative simulations, traditional techniques like behaviour trees encounter significant challenges. Large Language Models (LLMs), despite not always yielding optimal solutions, usually offer plausible and human-like responses to a given problem. In this paper, we exploit this capability and propose a novel architecture that integrates an LLM for decision-making with a classical automated planner that can generate sound plans for that decision. The combination aims to equip an agent with the ability to make decisions in various situations, even if they were not anticipated during the design phase.
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