TY - GEN
T1 - Modular Procedural Generation for Voxel Maps
AU - Pyarelal, Adarsh
AU - Banerjee, Aditya
AU - Barnard, Kobus
N1 - Funding Information:
Research was sponsored by the Army Research Office and was accomplished under Grant Number W911NF-20-1-0002. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation herein.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Task environments developed in Minecraft are becoming increasingly popular for artificial intelligence (AI) research. However, most of these are currently constructed manually, thus failing to take advantage of procedural content generation (PCG), a capability unique to virtual task environments. In this paper, we present mcg, an open-source library to facilitate implementing PCG algorithms for voxel-based environments such as Minecraft. The library is designed with human-machine teaming research in mind, and thus takes a ‘top-down’ approach to generation, simultaneously generating low and high level machine-readable representations that are suitable for empirical research. These can be consumed by downstream AI applications that consider human spatial cognition. The benefits of this approach include rapid, scalable, and efficient development of virtual environments, the ability to control the statistics of the environment at a semantic level, and the ability to generate novel environments in response to player actions in real time.
AB - Task environments developed in Minecraft are becoming increasingly popular for artificial intelligence (AI) research. However, most of these are currently constructed manually, thus failing to take advantage of procedural content generation (PCG), a capability unique to virtual task environments. In this paper, we present mcg, an open-source library to facilitate implementing PCG algorithms for voxel-based environments such as Minecraft. The library is designed with human-machine teaming research in mind, and thus takes a ‘top-down’ approach to generation, simultaneously generating low and high level machine-readable representations that are suitable for empirical research. These can be consumed by downstream AI applications that consider human spatial cognition. The benefits of this approach include rapid, scalable, and efficient development of virtual environments, the ability to control the statistics of the environment at a semantic level, and the ability to generate novel environments in response to player actions in real time.
KW - Artificial social intelligence
KW - Procedural content generation
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U2 - 10.1007/978-3-031-21671-8_6
DO - 10.1007/978-3-031-21671-8_6
M3 - Conference contribution
AN - SCOPUS:85148022383
SN - 9783031216701
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 85
EP - 101
BT - Computational Theory of Mind for Human-Machine Teams - 1st International Symposium, ToM for Teams 2021, Revised Selected Papers
A2 - Gurney, Nikolos
A2 - Sukthankar, Gita
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st International Symposium, ToM for Teams 2021
Y2 - 4 November 2021 through 6 November 2021
ER -