Teaching Automated Strategic Reasoning Using Capstone Tournaments

Authors: Oscar Veliz, Marcus Gutierrez, Christopher Kiekintveld

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We describe a tournament-based pedagogy that we have used in two different courses with two different games based on current research topics in artificial intelligence to engage students in designing agents that use strategic reasoning. Many students find this structure very engaging, and we find that students develop a deeper understanding of the abstract strategic reasoning concepts introduced in the courses.
Researcher Affiliation Academia Oscar Veliz, Marcus Gutierrez, and Christopher Kiekintveld University of Texas at El Paso El Paso, TX 79968 osveliz@miners.utep.edu mgutierrez22@miners.utep.edu cdkiekintveld@utep.edu
Pseudocode No The paper does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper mentions incorporating "open source code from Poker App by Dan Puperi (Puperi 2014)" and reusing
Open Datasets No The paper describes custom-designed games used for student projects and a pedagogical approach; it does not utilize or provide access to a publicly available dataset in the conventional sense of machine learning research.
Dataset Splits No The paper describes a pedagogical approach and custom game scenarios; it does not involve machine learning experiments that typically require training, validation, or test dataset splits for model evaluation.
Hardware Specification No The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the tournaments or student agents.
Software Dependencies No The paper mentions using "open source code from Poker App by Dan Puperi (Puperi 2014)", but it does not provide specific version numbers for this or any other software dependencies used in their system.
Experiment Setup No The paper describes the general structure of the tournaments and games but does not provide specific experimental setup details such as hyperparameters or training configurations typically found in model-based experiments.