Autonomous agents rely on automated planning algorithms to achieve their objectives. Simulation-based planning offers a significant advantage over declarative models in modelling complex environments. However, relying solely on a planner that produces a single plan may not be practical, as the generated plans may not always satisfy the agent's preferences. To address this limitation, we introduce $\texttt{FBI}_\texttt{LTL}$, a diverse planner explicitly designed for simulation-based planning problems. $\texttt{FBI}_\texttt{LTL}$ utilises Linear Temporal Logic (LTL) to define semantic diversity criteria, enabling agents to specify what constitutes meaningfully different plans. By integrating these LTL-based diversity models directly into the search process, $\texttt{FBI}_\texttt{LTL}$ ensures the generation of semantically diverse plans, addressing a critical limitation of existing diverse planning approaches that may produce syntactically different but semantically identical solutions. Extensive evaluations on various benchmarks consistently demonstrate that $\texttt{FBI}_\texttt{LTL}$ generates more diverse plans compared to a baseline approach. This work establishes the feasibility of semantically-guided diverse planning in simulation-based environments, paving the way for innovative approaches in realistic, non-symbolic domains where traditional model-based approaches fail.

翻译：自主智能体依赖自动化规划算法来实现其目标。基于模拟器的规划在复杂环境建模方面相比声明式模型具有显著优势。然而，仅依赖生成单一规划方案的规划器可能并不实用，因为生成的规划方案未必总能满足智能体的偏好。为应对这一局限，我们提出了$\texttt{FBI}_\texttt{LTL}$——一个专为基于模拟器的规划问题设计的显式多样化规划器。$\texttt{FBI}_\texttt{LTL}$利用线性时序逻辑（LTL）定义语义多样性标准，使智能体能够明确界定何种规划方案构成有意义的差异化。通过将这些基于LTL的多样性模型直接集成到搜索过程中，$\texttt{FBI}_\texttt{LTL}$确保生成语义多样化的规划方案，从而解决了现有多样化规划方法可能产生语法不同但语义相同解的关键缺陷。在多类基准测试上的广泛评估一致表明，相比基线方法，$\texttt{FBI}_\texttt{LTL}$能生成更具多样性的规划方案。本研究确立了在基于模拟器的环境中实现语义引导的多样化规划的可行性，为传统基于模型的方法失效的现实非符号领域开辟了创新方法的发展路径。