Diverse Planning with Simulators via Linear Temporal Logic
Mustafa F. Abdelwahed, Alice Toniolo, Joan Espasa, Ian P. Gent

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 , a diverse planner explicitly designed for simulation-based planning problems. 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, ensures the generation of semantically diverse plans, addressing a critical limitation of existing…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Logic, Reasoning, and Knowledge · Robotic Path Planning Algorithms
