Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Solving QNP and FOND+ with Generating, Testing and Forbidding
Authors: Zheyuan Shi, Hao Dong, Yongmei Liu
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented three solvers in C++ using the above three rearrangement strategies: GTF-BFF (FOND+ solver), GTF3FF (FOND+ solver) and GTF-3FN (QNP solver).1 Note that in this section, when we mention FOND+ domains or problems, we specifically refer to those that cannot be represented as QNPs. We evaluate the performance of our three solvers on QNPs in comparison with DSET [Zeng et al., 2022], FOND-ASP [Rodriguez et al., 2022], and three solvers using qnp2fond translator [Bonet and Geffner, 2020], each paired with different underlying FOND solvers for SC planning: PRP [Muise et al., 2012], (FOND-)SAT [Geffner and Geffner, 2018], and PR2 [Muise et al., 2024]. For FOND+ planning, we compare GTF-BFF and GTF-3FF against FOND-ASP. All experiments were run on an Ubuntu 20.04 Linux machine with an Intel Core i9-10980XE CPU (3.00 GHz). Each instance was allocated a maximum of 8 GB of memory and a runtime limit of 30 minutes. ... The information about the size of these domains and overall solving results are shown in Tables 1 (QNP) and 2 (FOND+). ... Figure 3 illustrates the overall coverage performance (the ratio of all instances solved) of various solvers over time for QNP/FOND+. |
| Researcher Affiliation | Academia | Zheyuan Shi, Hao Dong, Yongmei Liu Dept. of Computer Science, Sun Yat-sen University, Guangzhou 510006, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: SIEVE* ... Algorithm 2: FOND+ Solver. Entry Program ... Algorithm 3: FOND+ Solver. GTF |
| Open Source Code | Yes | 1Codes and data https://github.com/sysulic/GTF4FONDX. |
| Open Datasets | Yes | The QNP instances with small numbers of actions, features, and reachable states are categorized as Tiny-domains, including: blocks clear, blocks on, gripper, delivery, delivery2, q1, q2 (unsolvable), q3, gripper2 and rewards from Bonet and Geffner [2020]; Gripper1u (unsolvable) and Nest3u (unsolvable) from Zeng et al. [2022]; and 9 instances in qnp1 from Rodriguez et al. [2022]. Other existing QNP domains (each including one or more instances) include: Nests and Nests u (all instances are unsolvable) from Zeng et al. [2022]; qnp2 from Rodriguez et al. [2022]; Gripper Abs, Ferry Abs, Logistics Abs, Zenotravel Abs, Nomystery Abs, and Floortile Abs from Dong et al. [2025]. Existing FOND+ domains are all from Rodriguez et al. [2022]: qnp2-f11, qnp2-f01 (unsolvable), football and football u (unsolvable). |
| Dataset Splits | No | The paper refers to |
| Hardware Specification | Yes | All experiments were run on an Ubuntu 20.04 Linux machine with an Intel Core i9-10980XE CPU (3.00 GHz). Each instance was allocated a maximum of 8 GB of memory and a runtime limit of 30 minutes. |
| Software Dependencies | No | The paper states, "We implemented three solvers in C++" and mentions the operating system "Ubuntu 20.04 Linux." However, it does not provide specific version numbers for the C++ compiler, associated libraries, or any other critical software dependencies. |
| Experiment Setup | Yes | Each instance was allocated a maximum of 8 GB of memory and a runtime limit of 30 minutes. |