Improvements to the Generate-and-Complete Approach to Conformant Planning
Authors: Liangda Fang, Min Zhan, Jin Tong, Xiujie Huang, Ziliang Chen, Quanlong Guan
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that the improved GC planner has significant improvements over the original GC approach in many instances with a large number of initial states. Our approach also outperforms all of state-of-the-art planners, solving 989 instances in comparison to 784, which is the most solved by DNF. We evaluate our improved GC approach on benchmarks that includes a total of 50 domains, with 1,179 instances from previous literature and the IPC competitions. |
| Researcher Affiliation | Academia | 1Jinan University, Guangzhou, 510632, China 2Pengcheng Laboratory, Shenzhen, 518055, China 3Pazhou Laboratory, Guangzhou, 510330, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: GC(P) ... Algorithm 2: Complete(P, S, α) ... Algorithm 3: Extend(P, s, s , α) ... Algorithm 4: CG-Complete(P, s , α) ... Algorithm 5: i GC(P) |
| Open Source Code | No | The paper states: "We implemented the improved GC approach, namely i GC, on top of the source code of the GC[LAMA] planner." This indicates that the authors built their work upon existing open-source code (GC[LAMA]), but it does not explicitly state that their specific implementation (i GC) is open-source or provide a link to its repository. No other statements or links for code release are provided. |
| Open Datasets | Yes | We collect 50 domains with a total of 1179 instances, including 7 domains from IPC-5 [Bonet and Givan, 2006]: adder-ipc5, coins, comm, uts-k, uts-l, uts-r, and sortnet; 6 domains from IPC-6 [Bryce and Buffet, 2008]: uts-cycle, raos-key, forest, dispose, adder-ipc6, and block; 7 domains from [Hoffmann and Brafman, 2006]: logistics, bomb, ring, grid, omlette, cleaner, and safe; 7 domains from [Palacios and Geffner, 2009]: cube-center, sqr-center, corners-sqr, cornerscube, look-and-grab, 1-dispose, and push-to; 10 domains from [To et al., 2015]: new-dispose, new-push, new-ring, new-uts-cycle, new-uts-k, or-1-dispose, or-coins, or-dispose, or-new-push, and or-push-to; 2 domains from [Grastien and Scala, 2020]: grid-empty and grid-wall; 5 domains from [Nguyen et al., 2012]: 1-dispose-disappear, hall-a, hall-r, marker-enc1, and look-and-grab-disappear; 2 domains from [Eiter et al., 2003]: bt and btc; 3 domains from others: retrieve, reward and to-trash; and 1 domain we propose: or1-dispose-disappear, a variant of or-1-dispose, which action pickup randomly drops the item at any location if it is held. |
| Dataset Splits | No | The paper discusses solving instances of planning problems from various domains and benchmarks. It does not provide specific details on training/test/validation splits for these instances, as the nature of the research involves solving planning problems rather than training machine learning models with explicit data splits. |
| Hardware Specification | Yes | The experiments were run on Ubuntu 24.04, with an Intel 8086K 4.0 GHz CPU and 64GB RAM. |
| Software Dependencies | No | The paper mentions using "LAMA [Richter and Westphal, 2010] as the underlying classical planner" and "Z3 [de Moura and Bjørner, 2008] as the underlying SAT solver of the verification procedure." However, it does not specify the exact version numbers of LAMA or Z3 that were used in their experiments, which is required for reproducibility. |
| Experiment Setup | Yes | The memory limit is 16GB, and the time limit is 3600s. |