Learning General Planning Policies from Small Examples Without Supervision
Authors: Guillem Francès, Blai Bonet, Hector Geffner11801-11808
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented the proposed approach in a C++/Python system called D2L and evaluated it on several problems. Results Table 1 provides an overview of the execution of D2L over all generalized domains. |
| Researcher Affiliation | Academia | Universitat Pompeu Fabra, Barcelona, Spain |
| Pseudocode | No | The paper describes its methods in prose and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code and benchmarks are available online2 and archived in Zenodo (Franc es, Bonet, and Geffner 2021b). 2https://github.com/rleap-project/d2l. |
| Open Datasets | Yes | The domains include all problems with simple goals from (Bonet, Franc es, and Geffner 2019), e.g. clearing a block or stacking two blocks in Blocksworld, plus standard PDDL domains such as Gripper, Spanner, Miconic, Visitall and Blocksworld. We have used the benchmark distribution in https://github. com/aibasel/downward-benchmarks. |
| Dataset Splits | No | The paper mentions 'training instances' and 'test instances' but does not explicitly describe a 'validation' dataset split or process. |
| Hardware Specification | Yes | All experiments were run on an Intel i7-8700 CPU@3.2GHz with a 16 GB memory limit. |
| Software Dependencies | No | The paper mentions 'Open-WBO Weighted Max SAT solver', 'C++/Python system', and 'modified version of the Pyperplan planner' but does not specify their version numbers. |
| Experiment Setup | Yes | In all the experiments, we use δ = 2 and k F = 8, except in Delivery, where k F = 9 is required to find a policy. |