Model AI Assignments 2016
Authors: Todd Neller, Laura Brown, James Marshall, Lisa Torrey, Nate Derbinsky, Andrew Ward, Thomas Allen, Judy Goldsmith, Nahom Muluneh
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2016 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. |
| Researcher Affiliation | Academia | Todd W. Neller Gettysburg College tneller@gettysburg.edu Laura E. Brown Michigan Technological University lebrown@mtu.edu James B. Marshall Sarah Lawrence College jmarshall@sarahlawrence.edu Lisa Torrey St. Lawrence University ltorrey@stlawu.edu Nate Derbinsky Wentworth Institute of Technology derbinskyn@wit.edu Andrew A. Ward andrew.ward.cs@gmail.com Thomas E. Allen, Judy Goldsmith, and Nahom Muluneh University of Kentucky {teal223,goldsmit}@cs.uky.edu |
| Pseudocode | No | The paper describes assignments that involve algorithms, but it does not contain structured pseudocode or algorithm blocks within its content. |
| Open Source Code | Yes | Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu. An easy-to-use graphical simulator written in Python is included |
| Open Datasets | Yes | Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu. Included in our offering are: ... sample datasets that do or do not conform to the assumptions of k-Means Clustering |
| Dataset Splits | No | The paper describes educational assignments and mentions sample datasets but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning for a research experiment. |
| Hardware Specification | No | The paper describes educational assignments and resources but does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Python, Matlab/Octave, Weka, and Prolog, but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate experiments. |
| Experiment Setup | No | The paper describes educational assignments and activities but does not contain specific experimental setup details (concrete hyperparameter values, training configurations, or system-level settings) for a research experiment in the main text. |