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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Heuristics for Numeric Planning via Subgoaling
Authors: Enrico Scala, Patrik Haslum, Sylvie Thiébaux
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show the effectiveness of its inadmissible and admissible version on satisficing and optimal numeric planning, respectively. |
| Researcher Affiliation | Collaboration | 1Research School of Computer Science, The Australian National University 2NICTA Canberra, ACT, Australia firstnameEMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Benchmarks include IPC domains (http://www.icaps-conference.org/), numeric reformulation of the COUNTERS and GARDENING domains by Franc es and Geffner (2015), our motivating example (SAILING) and a new domain called FARMLAND. |
| Dataset Splits | No | The paper discusses evaluating heuristics on various planning domains and instances (e.g., SAILING instances scaled by number of boats and people, GARDENING with up to 3 plants), but it does not specify explicit training, validation, and test dataset splits with percentages or sample counts for reproducibility in the context of model training. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using concepts similar to Metric-FF and PDDL 2.1 but does not list specific software dependencies with version numbers for its implementation or experimental environment. |
| Experiment Setup | Yes | We evaluate three planners: two satisficing planners, using the inadmissible additive heuristic ˆhaddhbd+ and its extension with redundant constraints ˆhraddhbd+, and one optimal planner using the admissible heuristic with redundant constraints ˆhmaxhbd+. The latter is used within A?, while all other heuristics are used in a Greedy Best First Search (GBFS). For comparison, we also run GBFS with the interval-based relaxation (IBR) heuristic, obtained from Metric-FF (Hoffmann 2003) by disabling Enforced Hill Climbing. The optimal planner is compared with blind search. The timeout for all planners was 1, 800 seconds. |