“AI Sprawl” is
a machine learning experiment based on behavioral conditioning. Reward and
punishment are utilized to encourage success of designed aggregative behaviors.
Collision, avoiding collision, applied or unapplied gravity, only interlocking,
as well as designed rotations and interchanges of stacking housing units and
urban artifacts, are studied as variables of a training process in pursue of
unexpected, yet controlled, intelligent aggregations.
Made in Unity using machine learning workflows.
Institution: ACADIA 2021 Realingments
Instructors: Chien-hua Huang & Zach Beale
Research: Machine Learning, AI, Unity.
1. Vetical stacking over topography intent results.
2. Spread stacking over ground and topography intent results.
3. Applied and unapplied collision and gravity stacking experiments.