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..
Computing Contingent Plans Using Online Replanning
Authors: Radimir Komarnitsky, Guy Shani
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present a set of experiments, showing our approach to scale better than state of the art offline planners. We experiment with well known contingent benchmarks. Table 1 compares CPOR, CLG, and PO-PRP. |
| Researcher Affiliation | Academia | Radimir Komarnitsky and Guy Shani Information Systems Engineering Ben Gurion University, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 describes CPOR. Algorithm 2 identifies whether a plan for a given node n already exists. |
| Open Source Code | No | The paper states 'CPOR is implemented in C#', but does not provide any explicit statements about making the source code available or a link to a repository. |
| Open Datasets | Yes | We experiment with well known contingent benchmarks. For simple benchmarks that allow for structured solutions, our planner scales well beyond the reach of state-of-the-art planners. Table 1 compares CPOR, CLG, and PO-PRP (best reported variant). Doors, CTP, and Wumpus are simple. We illustrate this using a 4 4 Wumpus domain (Albore, Palacios, and Geffner 2009). |
| Dataset Splits | No | The paper uses well-known benchmarks but does not specify the exact percentages or methodology for splitting datasets into training, validation, or testing sets. |
| Hardware Specification | Yes | CPOR is implemented in C#, and the experiments were executed on a Windows 7 machine with 16GB of RAM, and an i7 Intel CPU. |
| Software Dependencies | No | The paper states 'CPOR is implemented in C#', which is a programming language, but does not list any specific software libraries, solvers, or packages with their version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values, training configurations, or system-level settings for the models or algorithms used. |