Additive Merge-and-Shrink Heuristics for Diverse Action Costs
Authors: Gaojian Fan, Martin Müller, Robert Holte
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments demonstrate that in these domains, an additive set of M&S heuristics using the new cost partitioning method produces much more informative and effective heuristics than creating a single M&S heuristic which directly encodes diverse costs. |
| Researcher Affiliation | Academia | Gaojian Fan, Martin M uller and Robert Holte University of Alberta, Canada {gaojian, mmueller, rholte}@ualberta.ca |
| Pseudocode | No | The paper describes procedures and mappings in text and tables (e.g., Table 2 showing cost mapping) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository. |
| Open Datasets | No | The paper refers to 'nonunit cost IPC domains' and 'GRIPPER' and provides a general competition website ('www.icaps-conference.org/index.php/Main/Competitions') but does not provide a specific link, DOI, repository name, or formal citation with authors/year for dataset access. |
| Dataset Splits | No | The paper compares performance on unit-cost and non-unit-cost versions of IPC domain instances, but it does not describe specific training, validation, or test dataset splits (e.g., percentages or counts) for a model. |
| Hardware Specification | No | The paper mentions time and memory limits for experiments but does not provide specific details about the hardware (e.g., CPU, GPU models, memory amounts) used. |
| Software Dependencies | No | The paper references 'Fast-Downward' (implicitly through its documentation URL) and general algorithms like 'Dijkstra s algorithm' and 'A*', but it does not list specific software dependencies with version numbers (e.g., Python, libraries, or solvers with versions). |
| Experiment Setup | Yes | The M&S construction of DCP-MS uses the same configuration as single M&S (the default recommended configuration). The recommended configuration: DFP merging, bisimulation shrinking, label reduction before shrinking, maximum of 50,000 states per abstraction (from www.fast-downward.org/Doc/Heuristic#Merge-and-shrink_heuristic). Each run has a 30 minute time limit and a 2 GB memory limit. |