PRP Rebooted: Advancing the State of the Art in FOND Planning
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Abstract (arXiv)
Fully Observable Non-Deterministic (FOND) planning is a variant of classical symbolic planning in which actions are nondeterministic, with an action's outcome known only upon execution. It is a popular planning paradigm with applications ranging from robot planning to dialogue-agent design and reactive synthesis. Over the last 20 years, a number of approaches to FOND planning have emerged. In this work, we establish a new state of the art, following in the footsteps of some of the most powerful FOND planners to date. Our planner, PR2, decisively outperforms the four leading FOND planners, at times by a large margin, in 17 of 18 domains that represent a comprehensive benchmark suite. Ablation studies demonstrate the impact of various techniques we introduce, with the largest improvement coming from our novel FOND-aware heuristic.
Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 3 of its 28 references that are in the map. First posted on arXiv 18 Dec 2023.
Builds on
3 of 28 references.
References · 3
Built on by
Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2024Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domainscited by 1
- 2024Planning with Uncertainty: Symmetries, Policy Inference, and Solution Compressioncited by 0
- 2025BPMN to PDDL: Translating Business Workflows for AI Planningcited by 0
- 2026Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planningcited by 0
- 2026Learning Bilevel Policies over Symbolic World Models for Long-Horizon Planning15 May 2026
- 2025BPMN to PDDL: Translating Business Workflows for AI Planning22 Nov 2025
- 2024Learning Generalized Policies for Fully Observable Non-Deterministic Planning Domains3 Apr 2024
- 2024Planning with Uncertainty: Symmetries, Policy Inference, and Solution Compression28 Mar 2024