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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Using Machine Learning for Decreasing State Uncertainty in Planning
Authors: Senka Krivic, Michael Cashmore, Daniele Magazzeni, Sandor Szedmak, Justus Piater
JAIR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process. (...) In Section 5 we present the experimental results. |
| Researcher Affiliation | Academia | Senka Krivic EMAIL King s College London (...) Michael Cashmore EMAIL University of Strathclyde (...) Daniele Magazzeni EMAIL King s College London (...) Sandor Szedmak EMAIL Aalto University (...) Justus Piater EMAIL University of Innsbruck (...) |
| Pseudocode | No | The paper describes procedures in numbered steps within paragraphs (e.g., Section 3.1, Section 4.1) but does not present any structured pseudocode or algorithm blocks with typical code-like formatting (e.g., indentation, keywords like 'if', 'for', 'return'). |
| Open Source Code | Yes | The system can be easily used with other domains as well (Krivic, Cashmore, Magazzeni, Ridder, Szedmak, & Piater, 2018). Krivic, S., Cashmore, M., Magazzeni, D., Ridder, B., Szedmak, S., & Piater, J. (2018). State Predictions System together with Domains and Test Scripts. https://github.com/Senka2112/State Predictions. |
| Open Datasets | Yes | The domains TPP, blocksworld, openstacks, and satellite were taken from the International planning Competition (Pommerening, Torralba, & Balyo, 2018). |
| Dataset Splits | Yes | The percentages of knowledge used as known data of the state in tests are 0.5%, 1%, 2%, 3%, 5%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%. (...) To examine the reproducibility of prediction problems, we randomly generated 10 states for each combination of the percentage of knowledge and problem size. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU models, GPU models, memory, etc.) used for running the experiments. |
| Software Dependencies | No | We integrated the prediction process into the planning and execution framework ROSPlan (Cashmore et al., 2015) in the Robot Operating System (ROS) (Quigley et al., 2009). The contingent Closed-Loop Greedy Planner (CLG) (Albore & Geffner, 2009) and POPF (Coles et al., 2010) are used to solve the resulting planning problems. (...) The resulting plans were validated against the ground truth using VAL (Howey, Long, & Fox, 2004). (No specific version numbers are provided for any of these software components.) |
| Experiment Setup | Yes | The confidence value for completing the graph with predictions was set to ct = 0 in all experiments. (...) A time limit of 1800 [s] was given to planning (including any prediction). (...) The value of ϵ = 0.05 is set for ϵ-greedy exploration for all domains. |