Trading Plan Cost for Timeliness in Situated Temporal Planning
Authors: Shahaf Shperberg, Andrew Coles, Erez Karpas, Eyal Shimony, Wheeler Ruml
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Empirical Results We tested the new DAG method on DACE2 problems whose performance profiles, cost distributions, and deadline distributions had a variety of forms. ... Table 1: Solution cost and metareasoning runtime (ms) of the algorithms on different types of benchmark problems. |
| Researcher Affiliation | Academia | Shahaf S. Shperberg1 , Andrew Coles2 , Erez Karpas 3 , Eyal Shimony1 and Wheeler Ruml4 1Ben-Gurion University, Israel 2King s College London, UK 3Technion, Israel 4University of New Hampshire, USA |
| Pseudocode | No | The paper describes the "Delay-Aware Greedy (DAG)" scheme in prose but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the source code of the methodology described. |
| Open Datasets | Yes | and Planner (P), which are distributions collected from search trees of the OPTIC planner when run on problems from the Robocup Logistics League [Niemueller et al., 2015] domain. |
| Dataset Splits | No | The paper states "we ran the algorithms on each setting for 500 attempts" but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper 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 does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper discusses some aspects of the greedy schemes (e.g., γ values for DAG) and baseline algorithms, but it does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings typically found in an "Experimental Setup" section. |