Computing Contingent Plans Using Online Replanning
Authors: Radimir Komarnitsky, Guy Shani
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 {radimir,shanigu}@bgu.ac.il |
| 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. |