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].
Cost-optimal Planning, Delete Relaxation, Approximability, and Heuristics
Authors: Christer Bäckström, Peter Jonsson, Sebastian Ordyniak
JAIR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The aim of this article is to analyse the approximability of cost-optimal monotone planning, and thus the performance of relevant heuristic functions. Our findings imply that it may be beneficial to study these kind of problems within the framework of parameterised complexity and we initiate work in this direction. |
| Researcher Affiliation | Academia | Christer Bäckström EMAIL Peter Jonsson EMAIL Department of Computer Science Linköping University SE-581 83 Linköping, Sweden Sebastian Ordyniak EMAIL School of Computing University of Leeds LS2 9JT, Leeds, United Kingdom |
| Pseudocode | Yes | 1 function Greedy Plan( V, A, I, G ) 2 if G is satisfied by I then return 3 s := I; ω := 4 while there is some a A such that pre(a) is satisfied by s and s = s eff(a) do 5 s := s eff(a) 6 Add a to the end of ω 7 Remove a from A 8 if G is satisfied by s then return ω 9 else reject Figure 3: Greedy algorithm for solving (+,+) instances |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions existing tools like 'Fast Downward planner' but not code released by the authors for this specific work. |
| Open Datasets | No | The paper is theoretical and focuses on analyzing the approximability and complexity of planning problems. It does not describe or use any specific datasets for empirical experiments. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset splits are discussed. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental results that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation of its methods, thus no specific software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and complexity analysis. It does not contain an experimental setup with hyperparameters or training configurations. |