The objective of this project is to understand the roles of general morphogenetic principles such as heterogeneity of components, differentiation/re-differentiation of components, and local information exchange among components in the self-organization of biological collectives, and to utilize them to improve the performance of collective artificial systems. The research is conducted in the following four steps: (1) Study the effects of morphogenetic principles on self-organizing patterns of heterogeneous collectives using mathematical/computational models. (2) Apply the results of theoretical investigation to construct models of actual self-organizing patterns found in real-world heterogeneous biological collectives. (3) Introduce the morphogenetic principles to existing collective artificial systems and develop mechanisms for programming their structures and behaviors. (4) Measure the overall performance of the proposed morphogenetic collective systems in decentralized optimization and exploration tasks. This research contributes to biology and ecology by providing new theoretical insight into how heterogeneous collectives may self-organize without centralized control, and also to computational science and engineering by bringing an unexplored combination of morphogenetic principles into bio-inspired computational systems. It is expected to produce societal impacts by providing a novel framework for the design of growing, self-organizing, self-repairing, and evolving artifacts. This project involves the following educational activities: (1) Interdisciplinary graduate certificate program in complex systems science. (2) New course on computational approaches to the origin and evolution of life. (3) Regional high school research program. (4) Public exhibition of morphogenetic collective systems at New York Hall of Science. Efforts will be made to attract female and ethnic minority students who are currently underrepresented in STEM.
