Self-Organized Fission Control for Flocking System

This paper studies the self-organized fission control problem for flocking system. Motivated by the fission behavior of biological flocks, information coupling degree (ICD) is firstly designed to represent the interaction intensity between individuals. Then, from the information transfer perspective, a “maximum-ICD” based pairwise interaction rule is proposed to realize the directional information propagation within the flock. Together with the “separation/alignment/cohesion” rules, a self-organized fission control algorithm is established that achieves the spontaneous splitting of flocking system under conflict external stimuli. Finally, numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm

Lenia and Expanded Universe

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semi-automatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of "virtual eukaryotes" that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.Comment: 8 pages, 5 figures, 1 table; submitted to ALIFE 2020 conferenc

Movement Decisions and Foraging Behaviour in Shoals of Fish The influence of internal and external stimuli

This thesis explores the mechanisms and functions of decision-making in groups, specifically in the context of social foraging in fish shoals. While many animal groups may seem homogeneous to the naked-eye, closer inspection reveals considerable heterogeneity, as they are composed of individuals with different phenotypes and different motivations living in stochastic, complex environments. The question then, is how do individual behavioural decisions change under varying internal and external conditions and what effect does this have on group level decision-making? How do animals address conflicts of interest and competition effects whilst ensuring benefits of group living are maintained? The approach taken in this thesis has been to address these questions from many angles, using a range of freshwater and marine species and employing an array of novel experimental set-ups. Of particular importance has been the utilization of automated, multi agent tracking software, which has allowed for the description of the movement and interaction of individually identified fish at a much finer scale than in the past. This project has direct significance to our understanding of the individual and group dynamics of social species, which is a central theme in behavioural ecology, and will inform researchers in a variety of fields from theoretical biology to sociological studies of human grouping patterns. The inclusion of internal nutritional state and external environmental factors into studies of group movement and decision-making in a foraging context is a practical way of linking the mechanistic forces behind individual behaviour to functional group-level responses. This will help expand our understanding of the evolutionary causes of group living and its ecological consequences, influencing conservation management plans and strategies to improve fisheries and aquacultural practices

Herding predators using swarm intelligence

Swarm intelligence, a nature-inspired concept that includes multiplicity, stochasticity, randomness, and messiness is emergent in most real-life problem-solving. The concept of swarming can be integrated with herding predators in an ecological system. This paper presents the development of stabilizing velocity-based controllers for a Lagrangian swarm of n∈N individuals, which are supposed to capture a moving target (intruder). The controllers are developed from a Lyapunov function, total potentials, designed via Lyapunov-based control scheme (LbCS) falling under the classical approach of artificial potential fields method. The interplay of the three central pillars of LbCS, which are safety, shortness, and smoothest course for motion planning, results in cost and time effectiveness and efficiency of velocity controllers. Computer simulations illustrate the effectiveness of control laws

What Makes Complex Systems Complex?

This paper explores some of the factors that make complex systems complex. We first examine the history of complex systems. It was Aristotle’s insight that how elements are joined together helps determine the properties of the resulting whole. We find (a) that scientific reductionism does not provide a sufficient explanation; (b) that to understand complex systems, one must identify and trace energy flows; and (c) that disproportionate causality, including global tipping points, are all around us. Disproportionate causality results from the wide availability of energy stores. We discuss three categories of emergent phenomena—static, dynamic, and adaptive—and recommend retiring the term emergent, except perhaps as a synonym for creative. Finally, we find that virtually all communication is stigmergic