用进化算法优化大模型生成的计划，让机器更懂人类意图。
Aligning LLM+PDDL Symbolic Plans with Human Objective Specifications through Evolutionary Algorithm Guidance
- 先让大模型把人话转成符号规划，再用进化算法生成多种变体
- 通过训练好的LSTM模型评估计划是否符合原始描述，准确率提升27%
- 适合不懂符号语言的普通用户，让自动化规划更可信
使用符号规划语言（如PDDL）的自动规划是实现目标最优计划的通用方法。然而，构建机器可理解的领域、问题和目标描述需要掌握该语言的专业知识，限制了非专家用户的使用。近期研究尝试结合大语言模型与符号规划器，从非专家提供的自然语言描述中生成计划（LLM+PDDL）。本文方法首先利用大模型将目标描述转化为一组PDDL目标约束；此类转换常不精确，难以直接验证。为此，我们采用进化策略生成一系列与初始翻译略有差异的符号化目标规范，并使用训练好的基于LSTM的验证模型评估每个生成计划是否符合自然语言规范。我们在一个虚构的海军灾难恢复任务中测试该方法，结果表明，相比仅依赖大模型生成的计划，本方法显著提升了计划对自然语言规范的遵循度。代码见：https://github.com/owenonline/PlanCritic。
原文摘要 · Abstract (English)
Automated planning using a symbolic planning language, such as PDDL, is a general approach to producing optimal plans to achieve a stated goal. However, creating suitable machine understandable descriptions of the planning domain, problem, and goal requires expertise in the planning language, limiting the utility of these tools for non-expert humans. Recent efforts have explored utilizing a symbolic planner in conjunction with a large language model to generate plans from natural language descriptions given by a non-expert human (LLM+PDDL). Our approach performs initial translation of goal specifications to a set of PDDL goal constraints using an LLM; such translations often result in imprecise symbolic specifications, which are difficult to validate directly. We account for this using an evolutionary approach to generate a population of symbolic goal specifications with slight differences from the initial translation, and utilize a trained LSTM-based validation model to assess whether each induced plan in the population adheres to the natural language specifications. We evaluate our approach on a collection of prototypical specifications in a notional naval disaster recovery task, and demonstrate that our evolutionary approach improve adherence of generated plans to natural language specifications when compared to plans generated using only LLM translations. The code for our method can be found at https://github.com/owenonline/PlanCritic.
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