# LTLf Best-Effort Synthesis in Nondeterministic Planning Domains

arXiv: https://arxiv.org/abs/2308.15188 · HTML: https://mlsanity.com/paper/2308.15188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective](https://mlsanity.com/paper/2003.00330/) (2020)
- [Symbolic LTLf Synthesis](https://mlsanity.com/paper/1705.08426/) (2017)
- [Hybrid Compositional Reasoning for Reactive Synthesis from Finite-Horizon Specifications](https://mlsanity.com/paper/1911.08145/) (2019)
- [A Symbolic Approach to Safety LTL Synthesis](https://mlsanity.com/paper/1709.07495/) (2017)
- [Planning and Synthesis Under Assumptions](https://mlsanity.com/paper/1807.06777/) (2018)
- [Multi-tier Automated Planning for Adaptive Behavior (Extended Version)](https://mlsanity.com/paper/2002.12445/) (2020)
- [Neural Entity Summarization with Joint Encoding and Weak Supervision](https://mlsanity.com/paper/2005.00152/) (2020)

## Built on by (most cited first, 2 of 2)

- [LTLf+ and PPLTL+: Extending LTLf and PPLTL to Infinite Traces](https://mlsanity.com/paper/2411.09366/) (2024)
- [FONDANT: Strong and Best-Effort Planning via Antichains](https://mlsanity.com/paper/2609.35160/) (2026)

## Abstract (arXiv)

We study best-effort strategies (aka plans) in fully observable nondeterministic domains (FOND) for goals expressed in Linear Temporal Logic on Finite Traces (LTLf). The notion of best-effort strategy has been introduced to also deal with the scenario when no agent strategy exists that fulfills the goal against every possible nondeterministic environment reaction. Such strategies fulfill the goal if possible, and do their best to do so otherwise. We present a game-theoretic technique for synthesizing best-effort strategies that exploit the specificity of nondeterministic planning domains. We formally show its correctness and demonstrate its effectiveness experimentally, exhibiting a much greater scalability with respect to a direct best-effort synthesis approach based on re-expressing the planning domain as generic environment specifications.