A Survey of Cellular Automata: Types, Dynamics, Non-uniformity and Applications

Cellular automata (CAs) are dynamical systems which exhibit complex global behavior from simple local interaction and computation. Since the inception of cellular automaton (CA) by von Neumann in 1950s, it has attracted the attention of several researchers over various backgrounds and fields for modelling different physical, natural as well as real-life phenomena. Classically, CAs are uniform. However, non-uniformity has also been introduced in update pattern, lattice structure, neighborhood dependency and local rule. In this survey, we tour to the various types of CAs introduced till date, the different characterization tools, the global behaviors of CAs, like universality, reversibility, dynamics etc. Special attention is given to non-uniformity in CAs and especially to non-uniform elementary CAs, which have been very useful in solving several real-life problems.Comment: 43 pages; Under review in Natural Computin

Novel design methods of central nervous system of C. elegans and olfactory bulb model of mammal based on sequential logic and numerical integration

This study proposes a novel design method of a neuromorphic electronic circuit: design of a neuromorphic circuit based on appropriately selected hybrid dynamics of synchronous sequential logic, asynchronous sequential logic, and numerical integration. Based on the proposed design method, a novel central nervous system model of C. elegans, and an olfactory bulb model are presented. It is then shown that the presented models can realize typical responses of a conventional central nervous system model of C. elegans, and the observation of chaos in the olfactory bulb. Furthermore, the presented models are implemented by a field programmable gate array and the presented model of C.elegans is used to control a prototype robot of C. elegans body. Then, experiments validate that the presented central nervous system model enables the body robot to reproduce typical chemotaxis behaviors of the conventional C. elegans model. In addition, comparisons show that the presented model consumes fewer circuit elements and lower power compared to various central nervous system models of C. elegans based on synchronous sequential logic, asynchronous sequential logic, and numerical integration

ERGODIC CELLULAR AUTOMATON NEURON MODEL FOR A VIRTUAL CLINICAL TRIAL OF NEURAL PROSTHESIS

A novel cellular automaton neuron model and its cellular differentiation method are presented. It is shown that the differentiation method enables the neuron model to reproduce typical nonlinear responses of a given neuron model. Then a virtual clinical trial of neural prosthesis is executed, i.e., a target neuron model in a network composed of biologically plausible differential equation neuron models is replaced with the presented neuron model that is differentiated to reproduce the target neuron model. The presented neuron model is implemented in a field programmable gate array and the virtual clinical trial is validated by experiments. The results show the presented neuron model is much more hardware-efficient compared to a simplified differential equation neuron model

Development of theories on asynchronous discrete-state system and their applications to designs of small and low-power neural prosthesis devices

研究成果の概要 (和文) : 本研究では，非同期離散状態システムを用いた生物模倣ハードウェアの設計とその神経補綴装置への応用のための基礎固めに取り組んだ．具体的には，以下の２つのテーマに取り組んだ．（１）非同期離散状態神経細胞モデルを題材にして，非同期離散状態システムの新しい理論解析手法を整備した．（２）非同期離散状態神経振動子ネットワークを題材にして，非同期離散状態システムを用いた生物システムモデルの系統的な設計手法を整備した．（３）非同期離散状態神経細胞モデルを題材にして，小型で低消費電力な非同期離散状態生物模倣ハードウェアの系統的設計手法を整備した．研究成果の概要 (英文) : In this study, we developed a design method of biomimetic hardware whose dynamics is described by an asynchronous discrete state map. For example, we obtained the following results, (1) We developed an asynchronous discrete state neuron model and related theoretical analysis method. (2) We developed an asynchronous discrete state central pattern generator model and related systematic design method. (3) We developed an asynchronous discrete state neuron model and related efficient implementation method

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells