Researchers at the University of Surrey have recently developed self-organizing algorithms inspired by biological morphogenesis that can generate formations for multi-robot teams, adapting to the environment they are moving in. Their recent study, featured in IEEE Transactions on Cognitive … evelopmental Systems, was partly funded by the European Commission's FP7 program. "This research can be traced back to my previous work on morphogenetic robotics that applies genetic and cellular principles underlying biological morphogenesis to the self-organization of collective systems, such as robot swarms," Professor Yaochu Jin, a Surrey University Distinguished Chair and principal investigator on the study, told TechXplore. "Our main idea was to build a metaphor between cells in multi-cellular organisms and robots, including modules for reconfigurable modular robots." The main advantage of using morphological principles observed in nature to generate collective robot behavior is that these principles allow robots to self-organize themselves in a way that is'guided', 'predictable' or'controllable'.

Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology to engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. Finally, we discuss the usefulness of self-organization within ALife studies, point to perspectives for future research, and list open questions.

A totally new computational grammatical structure has been developed which encompasses the general class of self-organizing systems. It is based on a universal rewrite system and the principle of nilpotency, where a system and its environment have a space-time variation defined by the phase, which preserves the dual mirror-image relationship between the two. As briefly summarized in the paper, there is already substantial and hard evidence in favour of the application of this universal rewrite approach to quantum physics. Further applications in diverse fields suggest that, while the relationship between a self-organized system and its environment must be fully understood in quantum physical computational terms rather than digital ones, a new discrete approach to this quantum mechanical understanding can be described, which extends beyond the purely quantum range. It offers a new calculational means, within existing digital computational technology, to approach and validate the workings of self-organized systems, and may well encompass related but different computational methods used by other workers.