State Projection via AI Planning
Authors: Shirin Sohrabi, Anton Riabov, Octavian Udrea
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions.All our experiments were run on a 2.5 GHz Intel Core i7 processor with 16 GB RAM. We ran the Planning Projector on a speciļ¬c use case (details of the use case omitted). Table 1 shows a summary of our comparison on the energy domain. |
| Researcher Affiliation | Industry | Shirin Sohrabi, Anton V. Riabov, and Octavian Udrea IBM T.J. Watson Research Center 1101 Kitchawan Rd, Yorktown Heights, NY 10598, USA {ssohrab, riabov, udrea}@us.ibm.com |
| Pseudocode | No | The paper describes the system architecture and components but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions collecting data from RSS publications and Twitter, but it does not provide concrete access information (e.g., specific links, DOIs, or citations to publicly available versions) for the dataset used in their experiments. It refers to a specific, potentially confidential, use case. |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits (e.g., percentages or sample counts) for their experiments. |
| Hardware Specification | Yes | All our experiments were run on a 2.5 GHz Intel Core i7 processor with 16 GB RAM. |
| Software Dependencies | No | The paper mentions various tools and planners (e.g., Free Mind, PDDL, LM-Cut, TK planner, LPG-d) but does not specify their version numbers or other software dependencies with versions used for reproduction. |
| Experiment Setup | No | The paper describes the overall system and its components but does not provide specific experimental setup details such as hyperparameters, optimization settings, or training configurations. |