Delete- and Ordering-Relaxation Heuristics for HTN Planning
Authors: Daniel Höller, Pascal Bercher, Gregor Behnke
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that our heuristics are competitive with state-of-the-art heuristics in terms of coverage, but much more informed. [...] 6 Evaluation We integrated our heuristic into the PANDA framework [Bercher et al., 2014] and combined it with the progression algorithm by H oller et al. [2020]. [...] The coverage of all systems is given in Figure 3 (left). |
| Researcher Affiliation | Academia | 1Saarland University, Saarland Informatics Campus 2The Australian National University, College of Engineering and Computer Science 3University of Freiburg 4Ulm University, Institute of Artificial Intelligence |
| Pseudocode | No | The paper describes its Integer Programming model and various constraints, but it does not contain a dedicated pseudocode block or algorithm section. |
| Open Source Code | No | Source code is available at panda.hierarchical-task.net (This link refers to the general PANDA framework into which the authors integrated their work, not explicitly the source code for the novel heuristics presented in this paper.) |
| Open Datasets | No | We used the same problem set used in related work [H oller et al., 2018; Behnke et al., 2018; Behnke et al., 2019] including 144 instances. (While it references a problem set used in prior work, it does not provide a direct link, DOI, or repository for accessing this dataset in the context of this paper.) |
| Dataset Splits | No | The paper mentions using a 'problem set' of 144 instances but does not provide specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or test sets. |
| Hardware Specification | Yes | We used a server with Xeon E5-2660 CPUs (2.60 GHz), 4 GB RAM and 10 minutes time limit. |
| Software Dependencies | Yes | Our IP model was solved using the CPLEX solver (version 12.8, restricted to 1 CPU core). |
| Experiment Setup | No | The paper describes various heuristic configurations (e.g., hdor, hdor lp) and search strategies (e.g., Greedy Best First, A*, GA*), but it does not provide specific hyperparameters like learning rates, batch sizes, or optimizer settings for the experimental setup. |