Seeking Open-Ended Evolution in Swarm Chemistry II: Analyzing Long-Term Dynamics via Automated Object Harvesting

We studied the long-term dynamics of evolutionary Swarm Chemistry by extending the simulation length ten-fold compared to earlier work and by developing and using a new automated object harvesting method. Both macroscopic dynamics and microscopic object features were characterized and tracked using several measures. Results showed that the evolutionary dynamics tended to settle down into a stable state after the initial transient period, and that the extent of environmental perturbations also affected the evolutionary trends substantially. In the meantime, the automated harvesting method successfully produced a huge collection of spontaneously evolved objects, revealing the system's autonomous creativity at an unprecedented scale.Comment: 8 pages, 9 figures, to be published in the ALIFE 2018 proceeding

Complexity, Development, and Evolution in Morphogenetic Collective Systems

Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system's structure and behavior, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors, and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.Comment: 13 pages, 8 figures, 1 table; submitted to "Evolution, Development, and Complexity: Multiscale Models in Complex Adaptive Systems" (Springer Proceedings in Complexity Series

Exploring Quantum Control Landscape Structure

A common goal of quantum control is to maximize a physical observable through the application of a tailored field. The observable value as a function of the field constitutes a quantum control landscape. Previous works have shown, under specified conditions, that the quantum control landscape should be free of suboptimal critical points. This favorable landscape topology is one factor contributing to the efficiency of climbing the landscape. An additional, complementary factor is the landscape \textit{structure}, which constitutes all non-topological features. If the landscape's structure is too complex, then climbs may be forced to take inefficient convoluted routes to finding optimal controls. This paper provides a foundation for understanding control landscape structure by examining the linearity of gradient-based optimization trajectories through the space of control fields. For this assessment, a metric $R\geq 1$ is defined as the ratio of the path length of the optimization trajectory to the Euclidean distance between the initial control field and the resultant optimal control field that takes an observable from the bottom to the top of the landscape. Computational analyses for simple model quantum systems are performed to ascertain the relative abundance of nearly straight control trajectories encountered when optimizing a state-to-state transition probability. The collected results indicate that quantum control landscapes have very simple structural features. The favorable topology and the complementary simple structure of the control landscape provide a basis for understanding the generally observed ease of optimizing a state-to-state transition probability.Comment: 27 pages, 7 figure

Swarm Mechanics and Swarm Chemistry: A Transdisciplinary Approach for Robot Swarms

This paper for the first time attempts to bridge the knowledge between chemistry, fluid mechanics, and robot swarms. By forming these connections, we attempt to leverage established methodologies and tools from these these domains to uncover how we can better comprehend swarms. The focus of this paper is in presenting a new framework and sharing the reasons we find it promising and exciting. While the exact methods are still under development, we believe simply laying out a potential path towards solutions that have evaded our traditional methods using a novel method is worth considering. Our results are characterized through both simulations and real experiments on ground robots.Comment: 7 pages, 11 figures, submitted to ICRA 2024 conferenc