Adaptive management of an active services network

The benefits of active services and networks cannot be realised unless the associated increase in system complexity can be efficiently managed. An adaptive management solution is required. Simulation results show that a distributed genetic algorithm, inspired by observations of bacterial communities, can offer many key management functions. The algorithm is fast and efficient, even when the demand for network services is rapidly varying

Bacterial Stigmergy: An Organising Principle of Multicellular Collective Behaviours of Bacteria

The self-organisation of collective behaviours often manifests as dramatic patterns of emergent large-scale order. This is true for relatively "simple" entities such as microbial communities and robot "swarms, " through to more complex self-organised systems such as those displayed by social insects, migrating herds, and many human activities. The principle of stigmergy describes those self-organised phenomena that emerge as a consequence of indirect communication between individuals of the group through the generation of persistent cues in the environment. Interestingly, despite numerous examples of multicellular behaviours of bacteria, the principle of stigmergy has yet to become an accepted theoretical framework that describes how bacterial collectives self-organise. Here we review some examples of multicellular bacterial behaviours in the context of stigmergy with the aim of bringing this powerful and elegant self-organisation principle to the attention of the microbial research community

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

An Ansatz for undecidable computation in RNA-world automata

In this Ansatz we consider theoretical constructions of RNA polymers into automata, a form of computational structure. The basis for transitions in our automata are plausible RNA-world enzymes that may perform ligation or cleavage. Limited to these operations, we construct RNA automata of increasing complexity; from the Finite Automaton (RNA-FA) to the Turing Machine equivalent 2-stack PDA (RNA-2PDA) and the universal RNA-UPDA. For each automaton we show how the enzymatic reactions match the logical operations of the RNA automaton, and describe how biological exploration of the corresponding evolutionary space is facilitated by the efficient arrangement of RNA polymers into a computational structure. A critical theme of the Ansatz is the self-reference in RNA automata configurations which exploits the program-data duality but results in undecidable computation. We describe how undecidable computation is exemplified in the self-referential Liar paradox that places a boundary on a logical system, and by construction, any RNA automata. We argue that an expansion of the evolutionary space for RNA-2PDA automata can be interpreted as a hierarchical resolution of the undecidable computation by a meta-system (akin to Turing's oracle), in a continual process analogous to Turing's ordinal logics and Post's extensible recursively generated logics. On this basis, we put forward the hypothesis that the resolution of undecidable configurations in RNA-world automata represents a mechanism for novelty generation in the evolutionary space, and propose avenues for future investigation of biological automata

Architecture, Space and Information in Constructions Built by Humans and Social Insects: a Conceptual Review

The similarities between the structures built by social insects and by humans have led to a convergence of interests between biologists and architects. This new, de facto interdisciplinary community of scholars needs a common terminology and theoretical framework in which to ground its work. In this conceptually oriented review paper, we review the terms “information”, “space” and “architecture” to provide definitions that span biology and architecture. A framework is proposed on which interdisciplinary exchange may be better served, with the view that this will aid better cross fertilisation between disciplines, working in the areas of collective behaviour and analysis of the structures and edifices constructed by non-humans; and to facilitate how this area of study may better contribute to the field of architecture. We then use these definitions to discuss the informational content of constructions built by organisms and the influence these have on behaviour, and vice versa. We review how spatial constraints inform and influence interaction between an organism and its environment, and examine the reciprocity of space and information on construction and the behaviour of humans and social insects

Part 1: a process view of nature. Multifunctional integration and the role of the construction agent

This is the first of two linked articles which draw s on emerging understanding in the field of biology and seeks to communicate it to those of construction, engineering and design. Its insight is that nature 'works' at the process level, where neither function nor form are distinctions, and materialisation is both the act of negotiating limited resource and encoding matter as 'memory', to sustain and integrate processes through time. It explores how biological agents derive work by creating 'interfaces' between adjacent locations as membranes, through feedback. Through the tension between simultaneous aggregation and disaggregation of matter by agents with opposing objectives, many functions are integrated into an interface as it unfolds. Significantly, biological agents induce flow and counterflow conditions within biological interfaces, by inducing phase transition responses in the matte r or energy passing through them, driving steep gradients from weak potentials (i.e. shorter distances and larger surfaces). As with biological agents, computing, programming and, increasingly digital sensor and effector technologies share the same 'agency' and are thus convergent

Control flow in active inference systems Part II: Tensor networks as general models of control flow

Living systems face both environmental complexity and limited access to free-energy resources. Survival under these conditions requires a control system that can activate, or deploy, available perception and action resources in a context specific way. In Part I, we introduced the free-energy principle (FEP) and the idea of active inference as Bayesian prediction-error minimization, and show how the control problem arises in active inference systems. We then review classical and quantum formulations of the FEP, with the former being the classical limit of the latter. In this accompanying Part II, we show that when systems are described as executing active inference driven by the FEP, their control flow systems can always be represented as tensor networks (TNs). We show how TNs as control systems can be implemented within the general framework of quantum topological neural networks, and discuss the implications of these results for modeling biological systems at multiple scales

Spatio-temporal patterns act as computational mechanisms governing emergent behavior in robotic swarms

Our goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their self-coordinating emergent behavior, has proven ineffective, largely due to the swarm's inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micro-macro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm's emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Cooperation and antagonism in information exchange in a growth scenario with two species

© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This is a pre-copyedited, author-produced PDF of an article accepted for publication in Journal of Theoretical Biology following peer review. The version of record [Andres Burgos & Daniel Polani, 'Cooperation and antagonism in information exchange in a growth scenario with two species' Journal of Theoretical Biology, Vol 399 (117-133), June 2016] is available on line via doi: http://dx.doi.org/10.1016/j.jtbi.2016.04.006We consider a simple information-theoretic model of communication, in which two species of bacteria have the option of exchanging information about their environment, thereby improving their chances of survival. For this purpose, we model a system consisting of two species whose dynamics in the world are modelled by a bet-hedging strategy. It is well known that such models lend themselves to elegant information-theoretical interpretations by relating their respective long-term growth rate to the information the individual species has about its environment. We are specifically interested in modelling how this dynamics are affected when the species interact cooperatively or in an antagonistic way in a scenario with limited resources. For this purpose, we consider the exchange of environmental information between the two species in the framework of a game. Our results show that a transition from a cooperative to an antagonistic behaviour in a species results as a response to a change in the availability of resources. Species cooperate in abundance of resources, while they behave antagonistically in scarcity.Peer reviewedFinal Accepted Versio