# Probabilistic Planning with Preferences over Temporal Goals

arXiv: https://arxiv.org/abs/2103.14489 · HTML: https://mlsanity.com/paper/2103.14489/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Safe Reinforcement Learning via Shielding](https://mlsanity.com/paper/1708.08611/) (2017)
- [Reinforcement Learning for Temporal Logic Control Synthesis with Probabilistic Satisfaction Guarantees](https://mlsanity.com/paper/1909.05304/) (2019)
- [Correct-by-synthesis reinforcement learning with temporal logic constraints](https://mlsanity.com/paper/1503.01793/) (2015)

## Built on by (most cited first, 4 of 4)

- [Probabilistic Planning with Partially Ordered Preferences over Temporal Goals](https://mlsanity.com/paper/2209.12267/) (2022)
- [Opportunistic Qualitative Planning in Stochastic Systems with Incomplete Preferences over Reachability Objectives](https://mlsanity.com/paper/2210.01878/) (2022)
- [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)

We present a formal language for specifying qualitative preferences over temporal goals and a preference-based planning method in stochastic systems. Using automata-theoretic modeling, the proposed specification allows us to express preferences over different sets of outcomes, where each outcome describes a set of temporal sequences of subgoals. We define the value of preference satisfaction given a stochastic process over possible outcomes and develop an algorithm for time-constrained probabilistic planning in labeled Markov decision processes where an agent aims to maximally satisfy its preference formula within a pre-defined finite time duration. We present experimental results using a stochastic gridworld example and discuss possible extensions of the proposed preference model.