Diverse Planning with Simulators via Linear Temporal Logic

TL;DR

This paper introduces FBI_LTL, a novel diverse planning algorithm using Linear Temporal Logic to generate semantically varied plans in simulation-based environments, improving over existing methods.

Contribution

We propose FBI_LTL, a planning method that integrates LTL-based semantic diversity criteria into the search process for more meaningful plan variation.

Findings

FBI_LTL produces more diverse plans than baseline methods.

Semantic diversity is effectively captured using LTL criteria.

The approach is validated on various benchmarks.

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.