Barrett

Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.

Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.

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Jaipur Simulation and AI

Description
This paper details the process for designing both a simulation of the board game Jaipur, and an artificial intelligence (AI) agent that can play the game against a human player. When designing an AI for a card game, there are two major problems that can arise. The first is the…

This paper details the process for designing both a simulation of the board game Jaipur, and an artificial intelligence (AI) agent that can play the game against a human player. When designing an AI for a card game, there are two major problems that can arise. The first is the difficulty of using a search space to analyze every possible set of future moves. Due to the randomized nature of the deck of cards, the search space rapidly leads to an exponentially growing set of potential game states to analyze when one tries to look more than one turn ahead. The second aspect that poses difficulty is the element of uncertainty that exists from opponent feedback. Certain moves are weak to specific opponent reactions, and these are difficult to predict due to hidden information. To circumvent these problems, the AI uses a greedy approach to decision making, attempting to maximize the value of its plays immediately, and not play for future turns. The agent utilizes conditional statements to evaluate the game state and choose a game action that it deems optimal, a heuristic to place an expected value (EV) of the goods it can choose from, and selects the best one based on this evaluation. Initial implementation of the simulation was done using C++ through a terminal application, and then was translated to a graphical interface using Unity and C#.
ContributorsOrr, James Christopher (Author) / Kobayashi, Yoshihiro (Thesis director) / Selgrad, Justin (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05

Isle of Thrones

Description

In Isle of Thrones, you must build up your kingdom to avoid being dominated by your opponent. Players start with a castle as their initial piece of land and draw cards from two decks, Event and Effect, which can influence the game. Each turn involves drawing an Event Card, playing…

In Isle of Thrones, you must build up your kingdom to avoid being dominated by your opponent. Players start with a castle as their initial piece of land and draw cards from two decks, Event and Effect, which can influence the game. Each turn involves drawing an Event Card, playing Effect Cards, taking actions such as placing land tiles strategically on the board to expand territory, and potentially drawing more Effect Cards. Players earn points by placing land tiles with different named pieces of the same point value next to each other, and the game ends when the board is filled or when a player can no longer place more pieces. Land pieces can be destroyed and returned to the Land Pile, and the player with the most points at the end of the game wins.
ContributorsMcDaniel, Katherine (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / Barrett, The Honors College (Contributor) / School of Social and Behavioral Sciences (Contributor)
Created2023-05

McDanielKatherine_Spring_2023.pdf

Description
In Isle of Thrones, you must build up your kingdom to avoid being dominated by your opponent. Players start with a castle as their initial piece of land and draw cards from two decks, Event and Effect, which can influence the game. Each turn involves drawing an Event Card, playing…

In Isle of Thrones, you must build up your kingdom to avoid being dominated by your opponent. Players start with a castle as their initial piece of land and draw cards from two decks, Event and Effect, which can influence the game. Each turn involves drawing an Event Card, playing Effect Cards, taking actions such as placing land tiles strategically on the board to expand territory, and potentially drawing more Effect Cards. Players earn points by placing land tiles with different named pieces of the same point value next to each other, and the game ends when the board is filled or when a player can no longer place more pieces. Land pieces can be destroyed and returned to the Land Pile, and the player with the most points at the end of the game wins.
ContributorsMcDaniel, Katherine (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / Barrett, The Honors College (Contributor) / School of Social and Behavioral Sciences (Contributor)
Created2023-05

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ContributorsMcDaniel, Katherine (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / Barrett, The Honors College (Contributor) / School of Social and Behavioral Sciences (Contributor)
Created2023-05

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ContributorsMcDaniel, Katherine (Author) / Loebenberg, Abby (Thesis director) / Mack, Robert (Committee member) / Barrett, The Honors College (Contributor) / School of Social and Behavioral Sciences (Contributor)
Created2023-05
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Artificial Intelligence with Graph Neural Networks Applied to a Risk-like Board Game

Description
This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes.…

This project aspires to develop an AI capable of playing on a variety of maps in a Risk-like board game. While AI has been successfully applied to many other board games, such as Chess and Go, most research is confined to a single board and is inflexible to topological changes. Further, almost all of these games are played on a rectangular grid. Contrarily, this project develops an AI player, referred to as GG-net, to play the online strategy game Warzone, which is based on the classic board game Risk. Warzone is played on a wide variety of irregularly shaped maps. Prior research has struggled to create an effective AI for Risk-like games due to the immense branching factor. The most successful attempts tended to rely on manually restricting the set of actions the AI considered while also engineering useful features for the AI to consider. GG-net uses no human knowledge, but rather a genetic algorithm combined with a graph neural network. Together, these methods allow GG-net to perform competitively across a multitude of maps. GG-net outperformed the built-in rule-based AI by 413 Elo (representing an 80.7% chance of winning) and an approach based on AlphaZero using graph neural networks by 304 Elo (representing a 74.2% chance of winning). This same advantage holds across both seen and unseen maps. GG-net appears to be a strong opponent on both small and medium maps, however, on large maps with hundreds of territories, inefficiencies in GG-net become more significant and GG-net struggles against the rule-based approach. Overall, GG-net was able to successfully learn the game and generalize across maps of a similar size, albeit further work is required for GG-net to become more successful on large maps.
ContributorsBauer, Andrew (Author) / Yang, Yezhou (Thesis director) / Harrison, Blake (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2022-05