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 eight AI assignments from the 2026 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at \url{http://modelai.gettysburg.edu}.

In this paper, we solve and visualize optimal play for All Yellow Zombie Dice, a simplification of the Zombie Dice jeopardy dice game by Steve Jackson [1] where we assume that all dice have the same outcome distribution. We present a spectrum of All Yellow Zombie Dice human-playable strategies that trade off greater play complexity for better performance, and collectively clarify key considerations for excellent play.

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 thirteen AI assignments from the 2025 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu

The Model AI Assignments session seeks to gather and dis- seminate the best assignment designs of the Artificial In- telligence (AI) Education community. Recognizing that as- signments form the core of student learning experience, we here present abstracts of five AI assignments from the 2024 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment spec- ifications and supporting resources may be found at http://modelai.gettysburg.edu.

In this paper, we solve and visualize optimal play for the Great Rolled Ones jeopardy dice game by Mitschke and Scheunemann [4, p. 4–5]. We share the second player advantage and compute that the first player should start with 3 compensation points (komi) for greatest fairness. We present a spectrum of human-playable strategies that trade off greater play complexity for better performance, and collectively clarify key considerations for excellent play.

In this paper, we demonstrate a novel technique for dynamically generating an emotionally-directed video game soundtrack. We begin with a human Conductor observing gameplay and directing associated emotions that would enhance the observed gameplay experience. We apply supervised learning to data sampled from synchronized input gameplay features and Conductor output emotional direction features in order to fit a mathematical model to the Conductor's emotional direction. Then, during gameplay, the emotional direction model maps gameplay state input to emotional direction output, which is then input to a music generation module that dynamically generates emotionally-relevant music during gameplay. Our empirical study suggests that random forests serve well for modeling the Conductor for our two experimental game genres.

Kalah (a.k.a. Mancala) is a two-player game of perfect information that has been a popular game for over half a century despite a strong first player advantage. In this paper, we present initial game states that are fair, as well as optimal play insights from analysis of optimal and suboptimal states.

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 2023 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu .

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 2022 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

In this paper, we introduce a technique for AI generation of the Bullets puzzle, a paper-and-pencil variant of Minesweeper. Whereas traditional Minesweeper can be lost due to the need to guess mine or non-mine positions, our puzzle is fully deducible from a minimal clue set. Puzzle generation is based on analysis and optimization of solutions from a human-like reasoning engine that classifies types of deductions. Additionally, we provide insights to subjective puzzle quality, minimal clue sampling trade-offs, and optimal bullet density.

In this column, we describe the Model AI Assignment "FairKalah: Fair Mancala Competition". After introducing the rules of Mancala (a.k.a. Kalah), we discuss the primary difficulty that its unfairness causes for AI competition assessment, and present a solution along with a description of a set of resources to aid in assignment adoption.

This paper describes a deterministic approach to building a fixed-strategy gin rummy player. In the paper, we develop and evaluate both heuristic and neural network models for informing draw, discard, and knock decisions in the game. In this empirical study, we test performance of the models through competitive game play, show which best inform strategy, and demonstrate statistical significance of the improvement over a simple strategy. Through this empirical study, we indicate features that we expect to be helpful in future improvements to Gin Rummy play.

We have continued adjusting to a "new normal" in the Covid era. In addition to the significant socio-economic challenges of the pandemic, for us as a scientific organization, we continue to grapple with a world with few, if any, in-person conferences for a second year in a row, and continued virtual interactions for the community. We are, however, proud of what we have been able to accomplish in the past year. As part of transparent communication with our membership, we share here the annual report that we provide to ACM each summer. You may notice a slight change in format this year, to focus on areas that ACM is particularly interested in hearing from us about. Also note that we include the report without modifications, so the information is a few months old!

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 ex- perience, we here present abstracts of three AI assignments from the 2012 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

We have continued adjusting to a "new normal" in the Covid era. In addition to the significant socio-economic challenges of the pandemic, for us as a scientific organization, we continue to grapple with a world with few, if any, in-person conferences for a second year in a row, and continued virtual interactions for the community. We are, however, proud of what we have been able to accomplish in the past year. As part of transparent communication with our membership, we share here the annual report that we provide to ACM each summer. You may notice a slight change in format this year, to focus on areas that ACM is particularly interested in hearing from us about. Also note that we include the report without modifications, so the information is a few months old!

In this article, we describe various approaches to opponent hand estimation in the card game Gin Rummy. We use an application of Bayes’ rule, as well as both simple and convolutional neural networks, to recognize patterns in simulated game play and predict the opponent’s hand. We also present a new minimal-sized construction for using arrays to pre-populate hand representation images. Finally, we define various metrics for evaluating estimations, and evaluate the strengths of our different estimations at different stages of the game.

We develop a data-driven approach for hand strength evaluation in the game of Gin Rummy. Employing Convolutional Neural Networks, Monte Carlo simulation, and Bayesian reasoning, we compute both offensive and defensive scores of a game state. After only one training cycle, the model was able to make sophisticated and human-like decisions with a 55.4% +/- 0.8% win rate (90% confidence level) against a Simple player.

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 2021 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu.

We perform an empirical study of Gin Rummy knocking strategies, drawing insight from a population of AI players that vary in both discarding and knocking strategies. For our best performing player, simple linear regression yielded a knocking strategy that both affirmed the features expert players give attention to in making knock decisions, and yet called into question the way such features are conventionally used.

It has been a first year full of unexpected challenges for the new officers of SIGAI! Along with the election of a new leadership team, we began the year with many changes, including integrating a new leadership team, changes in several of the appointed officers, and of course the global pandemic which has radically altered many of our activities! Of particular note, we are excited to welcome on board Louise Dennis as the new conference coordination officer, Anuj Karpatne as a new co-editor for AI Matters, and Alan Tsang as the new information officer, taking over from Michael Rovatsos, Amy McGovern, and Hang Ma respectively. We are very grateful to Michael, Amy, and Hang for years of excellent service to SIGAI! While we were working on several new initiatives, like everyone else, the Covid-19 pandemic changed the nature of what we were able to do and what we had to focus on. Nevertheless, we managed to get several of these initiatives off the ground, and were lucky in that SIGAI was well-positioned both financially and in terms of our rapport with many communities.