Diverse Planning with Simulators via Linear Temporal Logic

• URL: http://arxiv.org/abs/2510.17418v1
• Date: Mon, 20 Oct 2025 10:59:09 GMT
• Title: Diverse Planning with Simulators via Linear Temporal Logic
• Authors: Mustafa F. Abdelwahed, Alice Toniolo, Joan Espasa, Ian P. Gent,
• Abstract summary: $textttFBI_textttLTL$ is a diverse planner explicitly designed for simulation-based planning problems.<n>By integrating these-based diversity models directly into the search process, $textttFBI_textttLTL$ ensures the generation of semantically diverse plans.
• Score: 1.684937603700545
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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