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 [1].
Goal Operations for Cognitive Systems
Authors: Michael Cox, Dustin Dannenhauer, Sravya Kondrakunta
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Computational Experiments We evaluated MIDCA’s goal transformation in a modified blocks world domain. ... Empirical Results We collected data from 260 instances of MIDCA varying resources and number of goals. Figure 3 shows the results of MIDCA using a goal transformation strategy; whereas Figure 4 shows the results with fixed, static goals (i.e., no goal change). |
| Researcher Affiliation | Academia | Michael T. Cox, Dustin Dannenhauer, Sravya Kondrakunta, Wright State Research Institute Beavercreek, OH 45431 EMAIL, Lehigh University Bethlehem, PA 18015 EMAIL, Wright State University Dayton, OH 45435 EMAIL |
| Pseudocode | Yes | Table 1. Beta and Choose. Although ' is an ordered set, ' is a sequence where is treated like the set operator and like set difference. Reverse maintains the order of ' (choose inverts it). |
| Open Source Code | No | No concrete access to source code for the methodology was provided. |
| Open Datasets | No | The paper mentions 'a modified blocks world domain' but does not provide concrete access information for a publicly available or open dataset. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was provided. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) were provided. |
| Experiment Setup | No | The paper describes the experimental domain and varying parameters ('number of resources' and 'number of goals') but does not provide specific experimental setup details such as hyperparameters, optimizer settings, or training configurations. |