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for maximum returns; however, hostile ants outside mean that exploration is dangerous and
wasteful. Since the loss of a young ant is a greater cost than the loss of an older ant, it is more
efficient for older ants to forage.
The player must also manage space in the nest. Because each room in the nest has a
maximum capacity beyond which new food, fungus, and larvae will not be produced in the room,
the player must dig a nest of appropriate size and organization to house his colony. This process
is important to the player’s further understanding of the decentralized nature of ant architecture;
since the player cannot designate where new rooms will be built, he must express his architectural
instructions as situational urges, in keeping with the process by which real ant nests are shaped.
Because the food in the game environment is limited, in order to achieve a population of
50 ants, the player must learn to grow and tend a fungus garden. This is a complex activity which
includes several smaller tasks: gathering leaves, digging new rooms in the nest, removing leaf
waste from the nest, distributing the fungus throughout the nest, and gathering the food from the
fungus throughout the nest. Completing these subtasks requires that the player demonstrate an
understanding of the decentralized logic that drives an ant colony.
The AI System in Detail
Each ant has a caste (forager, midden worker, nurse, or queen), and each caste has a
behavioral model that is represented by triggers and urges. A trigger is a boolean statement about
the ant’s situation which is updated each frame. Triggers include “inside nest,” “outside,”
“carrying food,” “smells leaf” and so on. The configuration of these triggers each frame
represents the ant’s entire “understanding” of its environment.
Urges represent the ant’s desire to perform a certain action. Each urge is tied to one or
more triggers, and each urge has a strength which is determined by the player. When all of the
triggers tied to an urge are true, that urge occurs at full strength. When only some of the triggers
21
Object Description
| Title | Leafcutters: life simulation gameplay designed to evoke engagement with real-world subject matter |
| Author | Graner, William B. |
| Author email | granerw@gmail.com; bill@bgraner.com |
| Degree | Master of Fine Arts |
| Document type | Thesis |
| Degree program | Interactive Media |
| School | School of Cinematic Arts |
| Date defended/completed | 2011-05-05 |
| Date submitted | 2011 |
| Restricted until | Unrestricted |
| Date published | 2011-05-05 |
| Advisor (committee chair) | Gibson, Jeremy |
| Advisor (committee member) |
Fullerton, Tracy Anderson, Steven F. |
| Abstract | Leafcutters is a life simulation game about leafcutting ants which is designed to evoke engagement with real world subject matter. In this game, players shape the behaviors of a colony of ants in order to establish complex behaviors such as foraging and fungus farming. The game system in Leafcutters is adapted from existing biological research on ants, with an emphasis on the accurate adaptation of a natural system into a game system. This project draws on previous works in artificial life, life simulation games, swarm games, virtual pets, and virtual ants. Leafcutters is a work of expressive AI, an evocative knowledge object, and an educational game. |
| Keyword | simulation game; evocative knowledge object; educational game; video game |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3919 |
| Contributing entity | University of Southern California |
| Rights | Graner, William B. |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Graner-4578 |
| Archival file | uscthesesreloadpub_Volume40/etd-Graner-4578.pdf |
Description
| Title | Page 26 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | for maximum returns; however, hostile ants outside mean that exploration is dangerous and wasteful. Since the loss of a young ant is a greater cost than the loss of an older ant, it is more efficient for older ants to forage. The player must also manage space in the nest. Because each room in the nest has a maximum capacity beyond which new food, fungus, and larvae will not be produced in the room, the player must dig a nest of appropriate size and organization to house his colony. This process is important to the player’s further understanding of the decentralized nature of ant architecture; since the player cannot designate where new rooms will be built, he must express his architectural instructions as situational urges, in keeping with the process by which real ant nests are shaped. Because the food in the game environment is limited, in order to achieve a population of 50 ants, the player must learn to grow and tend a fungus garden. This is a complex activity which includes several smaller tasks: gathering leaves, digging new rooms in the nest, removing leaf waste from the nest, distributing the fungus throughout the nest, and gathering the food from the fungus throughout the nest. Completing these subtasks requires that the player demonstrate an understanding of the decentralized logic that drives an ant colony. The AI System in Detail Each ant has a caste (forager, midden worker, nurse, or queen), and each caste has a behavioral model that is represented by triggers and urges. A trigger is a boolean statement about the ant’s situation which is updated each frame. Triggers include “inside nest,” “outside,” “carrying food,” “smells leaf” and so on. The configuration of these triggers each frame represents the ant’s entire “understanding” of its environment. Urges represent the ant’s desire to perform a certain action. Each urge is tied to one or more triggers, and each urge has a strength which is determined by the player. When all of the triggers tied to an urge are true, that urge occurs at full strength. When only some of the triggers 21 |
