# Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives

arXiv: https://arxiv.org/abs/2304.11641 · HTML: https://mlsanity.com/paper/2304.11641/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 48 references that are in the map.
- First posted on arXiv 23 Apr 2023.

## Builds on (most cited first, 10 of 10)

- [Specifying User Preferences using Weighted Signal Temporal Logic](https://mlsanity.com/paper/2010.00752/) (2020)
- [Perception-Based Temporal Logic Planning in Uncertain Semantic Maps](https://mlsanity.com/paper/2012.10490/) (2020)
- [Reactive Task and Motion Planning under Temporal Logic Specifications](https://mlsanity.com/paper/2103.14464/) (2021)
- [Minimum-violation LTL Planning with Conflicting Specifications](https://mlsanity.com/paper/1303.3679/) (2013)
- [What to Do When You Can't Do It All: Temporal Logic Planning with Soft Temporal Logic Constraints](https://mlsanity.com/paper/2008.01926/) (2020)
- [Minimum-Violation Planning for Autonomous Systems: Theoretical and Practical Considerations](https://mlsanity.com/paper/2009.11954/) (2020)
- [Learning and Planning for Temporally Extended Tasks in Unknown Environments](https://mlsanity.com/paper/2104.10636/) (2021)
- [Hyperproperties for Robotics: Planning via HyperLTL](https://mlsanity.com/paper/1911.11870/) (2019)
- [Receding Horizon Control Based Online Motion Planning with Partially Infeasible LTL Specifications](https://mlsanity.com/paper/2007.12123/) (2020)
- [BP-RRT: Barrier Pair Synthesis for Temporal Logic Motion Planning](https://mlsanity.com/paper/2009.02432/) (2020)

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

- [Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies](https://mlsanity.com/paper/2403.18212/) (2024)
- [Automata Learning of Preferences over Temporal Logic Formulas from Pairwise Comparisons](https://mlsanity.com/paper/2505.18030/) (2025)

## Abstract (arXiv)

This paper studies temporal planning in probabilistic environments, modeled as labeled Markov decision processes (MDPs), with user preferences over multiple temporal goals. Existing works reflect such preferences as a prioritized list of goals. This paper introduces a new specification language, termed prioritized qualitative choice linear temporal logic on finite traces, which augments linear temporal logic on finite traces with prioritized conjunction and ordered disjunction from prioritized qualitative choice logic. This language allows for succinctly specifying temporal objectives with corresponding preferences accomplishing each temporal task. The finite traces that describe the system's behaviors are ranked based on their dissatisfaction scores with respect to the formula. We propose a systematic translation from the new language to a weighted deterministic finite automaton. Utilizing this computational model, we formulate and solve a problem of computing an optimal policy that minimizes the expected score of dissatisfaction given user preferences. We demonstrate the efficacy and applicability of the logic and the algorithm on several case studies with detailed analyses for each.