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

A Hormone Inspired System for On-line Adaptation in Swarm Robotic Systems

Individual robots, while providing the opportunity to develop a bespoke and specialised system, suffer in terms of performance when it comes to executing a large number of concurrent tasks. In some cases it is possible to drastically increase the speed of task execution by adding more agents to a system, however this comes at a cost. By mass producing relatively simple robots, costs can be kept low while still gaining the benefit of large scale multi-tasking. This approach sits at the core of swarm robotics. Robot swarms excel in tasks that rely heavily on their ability to multi-task, rather than applications that require bespoke actuation. Swarm suited tasks include: exploration, transportation or operation in dangerous environments. Swarms are particularly suited to hazardous environments due to the inherent expendability that comes with having multiple, decentralised agents. However, due to the variance in the environments a swarm may explore and their need to remain decentralised, a level of adaptability is required of them that can't be provided before a task begins. Methods of novel hormone-inspired robotic control are proposed in this thesis, offering solutions to these problems. These hormone inspired systems, or virtual hormones, provide an on-line method for adaptation that operates while a task is executed. These virtual hormones respond to environmental interactions. Then, through a mixture of decay and stimulant, provide values that grant contextually relevant information to individual robots. These values can then be used in decision making regarding parameters and behavioural changes. The hormone inspired systems presented in this thesis are found to be effective in mid-task adaptation, allowing robots to improve their effectiveness with minimal user interaction. It is also found that it is possible to deploy amalgamations of multiple hormone systems, controlling robots at multiple levels, enabling swarms to achieve strong, energy-efficient, performance

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

An Amalgamation of Hormone Inspired Arbitration Systems For Application In Robot Swarms

Previous work has shown that virtual hormone systems can be engineered to arbitrateswarms of robots between sets of behaviours. These virtual hormones act similarly to theirnatural counterparts, providing a method of online, reactive adaptation. It is yet to be shownhow virtual hormone systems could be used when a robotic swarm has a large variety of task typesto execute. This paper details work that demonstrates the viability of a collection of virtual hormonesthat can be used to regulate and adapt a swarm over time, in response to different environmentsand tasks. Specifically, the paper examines a new method of hormone speed control for energyefficiency and combines it with two existing systems controlling environmental preference as wellas a selection of behaviours that produce an effective foraging swarm. Experiments confirm theeffectiveness of the combined system, showing that a swarm of robots equipped with multiple virtualhormones can forage efficiently to a specified item demand within an allotted period of time

Applications and design of cooperative multi-agent ARN-based systems.

The Artificial Reaction Network (ARN) is an Artificial Chemistry inspired by Cell Signalling Networks (CSNs). Its purpose is to represent chemical circuitry and to explore the computational properties responsible for generating emergent high-level behaviour. In previous work, the ARN was applied to the simulation of the chemotaxis pathway of E. coli and to the control of quadrupedal robotic gaits. In this paper, the design and application of ARN-based cell-like agents termed Cytobots are explored. Such agents provide a facility to explore the dynamics and emergent properties of multicellular systems. The Cytobot ARN is constructed by combining functional motifs found in real biochemical networks. By instantiating this ARN, multiple Cytobots are created, each of which is capable of recognizing environmental patterns, stigmergic communication with others and controlling its own trajectory. Applications in biological simulation and robotics are investigated by first applying the agents to model the life-cycle phases of the cellular slime mould D. discoideum and then to simulate an oil-spill clean-up operation. The results demonstrate that an ARN based approach provides a powerful tool for modelling multi-agent biological systems and also has application in swarm robotics

Interaction dynamics and autonomy in cognitive systems

The concept of autonomy is of crucial importance for understanding life and cognition. Whereas cellular and organismic autonomy is based in the self-production of the material infrastructure sustaining the existence of living beings as such, we are interested in how biological autonomy can be expanded into forms of autonomous agency, where autonomy as a form of organization is extended into the behaviour of an agent in interaction with its environment (and not its material self-production). In this thesis, we focus on the development of operational models of sensorimotor agency, exploring the construction of a domain of interactions creating a dynamical interface between agent and environment. We present two main contributions to the study of autonomous agency: First, we contribute to the development of a modelling route for testing, comparing and validating hypotheses about neurocognitive autonomy. Through the design and analysis of specific neurodynamical models embedded in robotic agents, we explore how an agent is constituted in a sensorimotor space as an autonomous entity able to adaptively sustain its own organization. Using two simulation models and different dynamical analysis and measurement of complex patterns in their behaviour, we are able to tackle some theoretical obstacles preventing the understanding of sensorimotor autonomy, and to generate new predictions about the nature of autonomous agency in the neurocognitive domain. Second, we explore the extension of sensorimotor forms of autonomy into the social realm. We analyse two cases from an experimental perspective: the constitution of a collective subject in a sensorimotor social interactive task, and the emergence of an autonomous social identity in a large-scale technologically-mediated social system. Through the analysis of coordination mechanisms and emergent complex patterns, we are able to gather experimental evidence indicating that in some cases social autonomy might emerge based on mechanisms of coordinated sensorimotor activity and interaction, constituting forms of collective autonomous agency

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Art as we don't know it

2018 marked the 10th anniversary of the Bioart Society and created the impetus for the publication of Art as We Don’t Know It. For this publication, the Bioart Society joined forces with the School of Arts, Design and Architecture of the Aalto University. The close history and ongoing collaborative relationship between the Bioart Society and Biofilia – Base for Biological Arts in the Aalto University lead to this mutual effort to celebrate together a diverse and nurturing environment to foster artistic practices on the intersection of art, science and society. Rather than stage a retrospective, we decided to invite writings that look forward and invite speculations about the potential directions of bioarts. The contributions range from peer-reviewed articles to personal accounts and inter-views, interspersed with artistic contributions and Bioart Society projects. The selection offers a purview of the rich variety, both in content and form, of the work currently being made within the field of bioart. The works and articles clearly trouble the porous and provisional definitions of what might be understood as bioart, and indeed definitions of bioart have been usefully and generativity critiqued since the inception of the term. Whilst far from being definitive, we consider the contributions of the book to be tantalising and valuable indicators of trends, visions and impulses. We also invite into the reading of this publication a consideration of potential obsolescences knowing that some of today’s writing will become archaic over time as technologies driven by contemporary excitement and hype are discarded. In so doing we also acknowledge and ponder upon our situatedness and the partialness of our purview in how we begin and find points of departure from which to anticipate the unanticipated. Whilst declining the view of retrospection this book does present art and research that has grown and flourished within the wider network of both the Bioart Society and Biofilia during the previous decade. The book is structured into four thematic sections Life As We Don’t Know It, Convergences, Learnings/Unlearnings, Redraw and Refigure and rounded off with a glossary

Self-sufficiency of an autonomous self-reconfigurable modular robotic organism

In recent years, getting inspiration from simple but complex biological organisms, several advances have been seen in autonomous systems to mimic different behaviors that emerge from the interactions of a large group of simple individuals with each other and with the environment. Among several open issues a significantly important issue, not addressed so far, is the self-sufficiency, or in other words, the energetic autonomy of a modular robotic organism. This feature plays a pivotal role in maintaining a robotic organism\u27s autonomy for a longer period of time. To address the challenges of self-sufficiency, a novel dynamic power management system (PMS) with fault tolerant energy sharing is proposed, realized in the form of hardware and software, and tested. The innate fault tolerant feature of the proposed PMS ensures power sharing in an organism despite docked faulty robotic modules. Due to the unavailability of sufficient number of real robotic modules a simulation framework called Replicator Power Flow Simulator is devised for the implementation of application software layer power management components. The simulation framework was especially devised because at the time of writing this work no simulation tool was available that could be used to perform power sharing and fault tolerance experiments at an organism level. The simulation experiments showed that the proposed application software layer dynamic power sharing policies in combination with the distributed fault tolerance feature in addition to self-sufficiency are expected to enhance the robustness and stability of a real modular robotic organism under varying conditions.Inspiriert von einfachen aber komplexen biologischen Organismen wurden in den letzten Jahren verschiedenste autonome Systeme entwickelt, welche die Verhaltensweisen einer großen Gruppe einfacher Individuen nachahmen. Das zentrale und bis heute ungelöste Problem dieser Organismen ist deren autonome Energieversorgung. Zur Sicherstellung der Energieversorgung eines aus mehreren Robotern zusammengesetzten Organismus wurde in dieser Arbeit ein neuartiges Power-Management-System (PMS) konzipiert, aufgebaut und an einzelnen Robotermodulen und einem Roboterorganismus getestet. Die Hardware eines bestehenden Roboters wurde um ein neues Konzept erweitert, das auch bei fehlerhaften Robotermodulen einen Energieaustausch sicherstellt und so zu einer erhöhten Robustheit des PMS führen soll. Das entwickelte PMS wurde in modulare Roboter integriert und beispielhaft anhand eines Roboterorganismus getestet. In Ermangelung einer ausreichenden Anzahl von Robotermodulen wurde eine Simulationsumgebung entwickelt und die Software des PMS im Simulationsprogramm, anstatt im Roboter, implementiert. Dieses Simulationswerkzeug ist momentan das Einzige, das unter Berücksichtigung des Bewegungsmodells des Organismus den Energietransport im Roboterorganismus visuell darstellt und das Verhalten in verschiedenen Fehlerfällen simulieren kann. Die Simulationen und Messungen zeigen, dass das entwickelte PMS geeignet ist, die Energieversorgung von Roboterorganismen auch in Fehlerfällen sicherzustellen und so die Stabilität und Robustheit zu erhöhen

HCC Architecture - Hormonal Communications and Control Architecture

This thesis aims to provide a novel framework for a multiagent system implementation. The major feature of the proposed architecture is the introduction of the biological concept of hormones. The hormones are passed via the communication network to convey limited global system state knowledge. The agents\u27 response to a hormone is interpreted depending on its own local agent state. The primary focus of this thesis is the development of the particulars of the architecture. Prior work of multiagent systems research is reviewed and studied for contributions. Biological studies of hormones are employed to draw out interaction rules and analyze control mechanisms in a biological organism. The hormonal communication and control architecture is constructed, with major components detailed by flowcharts. The proposal is tested with two simulations: A minesweeping problem that has been modeled by other models, and an application of the architecture to a hypothetical ant colony. Research on biological ants is presented to suggest the behavior and goals of a model configured to employ the HCC architecture. The model is fleshed out, and the decisions made by considerations to the architecture are explained. The implementation of the simulation programming with the SWARM programming libraries for the Objective-C language is discussed. The data from experimental runs are analyzed with attention to global action