Hierarchical Task Network Planning with Task Insertion and State Constraints
Authors: Zhanhao Xiao, Andreas Herzig, Laurent Perrussel, Hai Wan, Xiaoheng Su
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that just as for TIHTN planning, all solutions of the TIHTNS planning problem can be obtained by acyclic decomposition and task insertion, entailing that its plan-existence problem is decidable without any restriction on decomposition methods. We also prove that the extension by state constraints does not increase the complexity of the plan-existence problem, which stays 2-NEXPTIME-complete, based on an acyclic progression operator. In addition, we show that TIHTNS planning covers not only the original TIHTN planning but also hierarchy-relaxed hierarchical goal network planning. |
| Researcher Affiliation | Academia | Zhanhao Xiao1,2, Andreas Herzig1,3, Laurent Perrussel1, Hai Wan4,5, and Xiaoheng Su4 1IRIT, University of Toulouse, Toulouse, France 2AIRG, Western Sydney University, Penrith, Australia 3IRIT, CNRS, Toulouse, France 4School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China 5Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. The 'Acyclic progression' section describes the operator textually without a formal algorithm block. |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The paper is theoretical and does not describe experiments with datasets, therefore it does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with datasets, therefore it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology). |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, therefore it does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments or software implementation details that would require specific versioned software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, therefore it does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings). |