# Temporal Planning with Intermediate Conditions and Effects

arXiv: https://arxiv.org/abs/1909.11581 · HTML: https://mlsanity.com/paper/1909.11581/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2019.

## Built on by (most cited first, 7 of 7)

- [Using large language models for embodied planning introduces systematic safety risks](https://mlsanity.com/paper/2604.18463/) (2026)
- [A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements](https://mlsanity.com/paper/2408.05795/) (2024)
- [Mind the Prompt: Self-adaptive Generation of Task Plan Explanations via LLMs](https://mlsanity.com/paper/2604.21092/) (2026)
- [An Efficient Incremental Simple Temporal Network Data Structure for Temporal Planning](https://mlsanity.com/paper/2212.07226/) (2022)
- [Platform-Aware Mission Planning](https://mlsanity.com/paper/2501.09632/) (2025)
- [Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning](https://mlsanity.com/paper/2505.13372/) (2025)
- [Compiling Temporal Numeric Planning into Discrete PDDL+: Extended Version](https://mlsanity.com/paper/2603.12188/) (2026)

## Abstract (arXiv)

Automated temporal planning is the technology of choice when controlling systems that can execute more actions in parallel and when temporal constraints, such as deadlines, are needed in the model. One limitation of several action-based planning systems is that actions are modeled as intervals having conditions and effects only at the extremes and as invariants, but no conditions nor effects can be specified at arbitrary points or sub-intervals. In this paper, we address this limitation by providing an effective heuristic-search technique for temporal planning, allowing the definition of actions with conditions and effects at any arbitrary time within the action duration. We experimentally demonstrate that our approach is far better than standard encodings in PDDL 2.1 and is competitive with other approaches that can (directly or indirectly) represent intermediate action conditions or effects.