# FAPE: a Constraint-based Planner for Generative and Hierarchical Temporal Planning

arXiv: https://arxiv.org/abs/2010.13121 · HTML: https://mlsanity.com/paper/2010.13121/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Oct 2020.

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

- [Improving Plan Execution Flexibility using Block-Substitution](https://mlsanity.com/paper/2406.03091/) (2024)
- [On Guiding Search in HTN Temporal Planning with non Temporal Heuristics](https://mlsanity.com/paper/2306.07638/) (2023)
- [Temporal Planning via Interval Logic Satisfiability for Autonomous Systems](https://mlsanity.com/paper/2406.09661/) (2024)
- [Execution Flexibility in Automated Planning: A Comparative Evaluation of Deordering and Reordering Strategies](https://mlsanity.com/paper/2609.16822/) (2026)

## Abstract (arXiv)

Temporal planning offers numerous advantages when based on an expressive representation. Timelines have been known to provide the required expressiveness but at the cost of search efficiency. We propose here a temporal planner, called FAPE, which supports many of the expressive temporal features of the ANML modeling language without loosing efficiency. FAPE's representation coherently integrates flexible timelines with hierarchical refinement methods that can provide efficient control knowledge. A novel reachability analysis technique is proposed and used to develop causal networks to constrain the search space. It is employed for the design of informed heuristics, inference methods and efficient search strategies. Experimental results on common benchmarks in the field permit to assess the components and search strategies of FAPE, and to compare it to IPC planners. The results show the proposed approach to be competitive with less expressive planners and often superior when hierarchical control knowledge is provided. FAPE, a freely available system, provides other features, not covered here, such as the integration of planning with acting, and the handling of sensing actions in partially observable environments.