{"ID":2852711,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17418","arxiv_id":"2510.17418","title":"Diverse Planning with Simulators via Linear Temporal Logic","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.","short_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 a...","url_abs":"https://arxiv.org/abs/2510.17418","url_pdf":"https://arxiv.org/pdf/2510.17418v1","authors":"[\"Mustafa F. Abdelwahed\",\"Alice Toniolo\",\"Joan Espasa\",\"Ian P. Gent\"]","published":"2025-10-20T10:59:09Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.MA\"]","methods":"[]","has_code":false}