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.

Prior approaches to generating diverse plans in domain-independent  planning seek out variations on plan structure such as actions or  causal links used, or states entered.  Measuring such syntactic  differences between plans can be misleading because syntactically  different plans can be semantically identical.  We develop a  landmark-based plan distance measure that captures semantic  differences between plans. The landmark-based distance measure focuses on the disjunctive landmarks  satisfied by each plan.  We develop a  simple algorithm for finding diverse plans that is based upon the LAMA planner.  We illustrate that, in comparison with plan distance  measures,  landmark-based plan distance is  not as susceptible to including irrelevant or redundant actions in  plans to increase plan distance. Through extensive empirical evaluation, we find that  high landmark distance between plans implies high action set  distance, but not vice versa.  Landmark-based plan distance overcomes some of the weaknesses of syntactic plan distance measures and can be used to find plan sets that are both landmark diverse and action set diverse.