Programming Methodology for Biologically-Inspired Self-
Assembling Systems  Radhika Nagpal, Attila Kondacs, and
Catherine Chang  We present a programming methodology for self-
assembling complex structures from vast numbers of locally-
interacting identically-programmed agents, using techniques
inspired by developmental biology. We demonstrate this approach
through two examples: shape formation on a reconfigurable sheet,
and self-assembling two dimensional structures through
replication. In each case, the desired global shape is specified
using an abstract geometry-based language, and the agent program
is directly compiled from the global specification. The
resulting self-assembly processes are versatile, analyzable, and
reliable in the face of random agent distributions, varying
numbers of agents and random death. This approach takes
advantage of traditional computer science techniques for
managing complexity, while relying on biological models for
achieving robustness at the local level.