Numeric Planning via Abstraction and Policy Guided Search
Authors: León Illanes, Sheila A. McIlraith
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate competitive performance when compared to other well-known algorithms for numeric planning, and a significant performance improvement in certain domains. Experimental results in five standard benchmark domains from the International Planning Competition [Long and Fox, 2003] are displayed in Tables 1 and 2. |
| Researcher Affiliation | Academia | Le on Illanes and Sheila A. Mc Ilraith Department of Computer Science University of Toronto, Toronto, Canada {lillanes,sheila}@cs.toronto.edu |
| Pseudocode | Yes | Algorithm 1: Best-first search for numeric planning, Algorithm 2: Overview of the ARGUS algorithm, Algorithm 3: Policy Guided Expansion Procedure, Algorithm 4: Abstraction of Numeric Planning Problem into Classical Planning Problem |
| Open Source Code | No | The paper mentions implementing ARGUS as an extension to the Fast Downward planning system but does not provide an explicit statement or link to the open-source code for their specific implementation. |
| Open Datasets | Yes | Experimental results in five standard benchmark domains from the International Planning Competition [Long and Fox, 2003] are displayed in Tables 1 and 2. |
| Dataset Splits | No | The paper utilizes standard benchmark domains but does not explicitly specify the training, validation, or test splits (e.g., percentages or sample counts) used for the experiments, nor does it refer to specific predefined splits within those benchmarks. |
| Hardware Specification | No | The paper mentions a memory limit ('at most 4GB of memory') for experiments but does not provide specific details about the hardware used, such as CPU or GPU models, or other computer specifications. |
| Software Dependencies | Yes | We implemented ARGUS as an extension to the Fast Downward planning system [Helmert, 2006]. The tests used to determine the possible effects of operators are evaluated using the Z3 Theorem Prover [De Moura and Bjørner, 2008]. |
| Experiment Setup | Yes | The abstract search is performed as a greedy best-first search using the FF heuristic [Hoffmann and Nebel, 2001]... The concrete search algorithm is a weighted A (with w 5) modified with the ARGUS expansion procedure. For our experiments, both planners were configured as satisficing planners, ignoring all the optimization aspects of the benchmark domains. ...limited to a 30 minute runtime using at most 4GB of memory. These limits are applied to all preprocessing stages and search. |