  We apply the knowledge of urban settings established with the study of Land
Use and Transport Interaction (LUTI) models to develop reward functions for an
agent-based system capable of planning realistic artificial cities. The system
aims to replicate in the micro scale the main components of real settlements,
such as zoning and accessibility in a road network. Moreover, we propose a
novel representation for the agent's environment that efficiently combines the
road graph with a discrete model for the land. Our system starts from an empty
map consisting only of the road network graph, and the agent incrementally
expands it by building new sites while distinguishing land uses between
residential, commercial, industrial, and recreational.
