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Jon Timmis

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In Silico Investigation into CD8Treg Mediated Recovery in Murine Experimental Encephalomyelitis

by Richard Williams and Jon Timmis

Papers by Jon Timmis

Evolution of Diverse, Manufacturable Robot Body Plans

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics... more Advances in rapid prototyping have opened up new avenues of research within Evolutionary Robotics in which not only controllers but also the body plans (morphologies) of robots can evolve in real-time and real-space. However, this also introduces new challenges, in that robot models that can be instantiated from an encoding in simulation might not be manufacturable in practice (due to constraints associated with the 3D printing and/or automated assembly processes). We introduce a representation for evolving (wheeled) robots with a printed plastic skeleton, and evaluate three variants of a novelty-search algorithm in terms of their ability to produce populations of manufacturable but diverse robots. While the set of manufacturable robots discovered represent only a small fraction of the overall search space of all robots, all methods are shown to be capable of generating a diverse population of manufacturable robots that we conjecture is large enough to seed an evolving robotic ecosystem.

Livestock Disease Management for Trading Across Different Regulatory Regimes

EcoHealth, 2018

The maintenance of livestock health depends on the combined actions of many different actors, bot... more The maintenance of livestock health depends on the combined actions of many different actors, both within and across different regulatory frameworks. Prior work recognised that private risk management choices have the ability to reduce the spread of infection to trading partners. We evaluate the efficiency of farmers' alternative biosecurity choices in terms of their own-benefits from unilateral strategies and quantify the impact they may have in filtering the disease externality of trade. We use bovine viral diarrhoea (BVD) in England and Scotland as a case study, since this provides an example of a situation where contrasting strategies for BVD management occur between selling and purchasing farms. We use an agent-based bioeconomic model to assess the payoff dependence of farmers connected by trade but using different BVD management strategies. We compare three disease management actions: test-cull, test-cull with vaccination and vaccination alone. For a two-farm trading situation, all actions carried out by the selling farm provide substantial benefits to the purchasing farm in terms of disease avoided, with the greatest benefit resulting from test-culling with vaccination on the selling farm. Likewise, unilateral disease strategies by purchasers can be effective in reducing disease risks created through trade. We conclude that regulation needs to balance the trade-off between private gains from those bearing the disease management costs and the positive spillover effects on others.

A Multi-objective Optimization Approach Associated to Climate Change Analysis to Improve Systematic Conservation Planning

Lecture Notes in Computer Science, 2015

Biodiversity conservation has been since long an academic community concern, leading scientists t... more Biodiversity conservation has been since long an academic community concern, leading scientists to propose strategies to effectively meet conservation goals. In particular, Systematic Conservation Planning (SCP) aims to determine the most cost effective way of investing in conservation actions. SCP can be formalized by the Set-Covering Problem, which is NP-hard. SCP is inherently multi-objective, although it has been usually treated with a monobjective and static approach. Here, we propose a multi-objective solution for SCP, increasing its flexibility and complexity, and, at the same time, augmenting the quality of provided information, which reinforces decision-making. We used ensemble forecasting, considering future climate simulations to estimate species occurrence projected to 2080. Our method identifies sites: 1) of high priority for conservation; 2) with significant risk of investment; and, 3) that may become attractive in the future. To the best of our knowledge, this application to a real-world problem in ecology is the first attempt to apply multi-objective optimization to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation.

A study of error diversity in robotic swarms for task partitioning in foraging tasks

Frontiers in Robotics and AI, Jan 4, 2023

Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and w... more Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and will have a similar (if not the same) error model. However, in reality, this is not the case, and this lack of uniformity in the error model, and other operations, can lead to various emergent behaviors. This paper considers the impact of the error model and compares robots in a swarm that operate using the same error model (uniform error) against each robot in the swarm having a different error model (thus introducing error diversity). Experiments are presented in the context of a foraging task. Simulation and physical experimental results show the importance of the error model and diversity in achieving the expected swarm behavior.

Artificial Immune Systems: ICARIS 2010, Proceedings of 9th International Confernence

Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

IEEE Transactions on Cognitive and Developmental Systems, Jun 1, 2023

Most work in evolutionary robotics centres on evolving a controller for a fixed body-plan. Howeve... more Most work in evolutionary robotics centres on evolving a controller for a fixed body-plan. However, previous studies suggest that simultaneously evolving both controller and bodyplan could open up many interesting possibilities. However, the joint optimisation of body-plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have body-plans that are very different from either of their parents, leading to a potential mismatch between the structure of an inherited neural controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit 'types' of robots (where this is defined with respect to the features of the body-plan). By initiating learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.

Autonomous Learning Paradigm for Spiking Neural Networks

Springer eBooks, 2019

Compared to biological systems, existing learning systems lack the ability to learn autonomously,... more Compared to biological systems, existing learning systems lack the ability to learn autonomously, especially in changing and dynamic environments. This paper addresses the issue of autonomous learning by developing a self-learning spiking neural network (SNN) and demonstrating its autonomous learning capability using a simple robot controller application. Our proposed learning rule exploits an inherit property of the existing Spike-Timing-Dependent Plasticity (STDP) rule in that if the instantaneous presynaptic frequency decreases, then for a conventional Hebbian window the STDP rule potentiates. Conversely if the instantaneous frequency increases the STDP rule depresses: the opposite is true for anti-Hebbian window. This paper will also show that obstacle avoidance is achievable using a conventional Hebbian learning window while object tracking can be learned using an anti-Hebbian learning window. Hence the proposed learning paradigm is novel in that it does not require external supervisions for either these tasks. The proposed learning paradigm also uses a previously explored astrocyte neuron interaction where a periodic Slow Inward Current (SIC) from an astrocyte can potentiate a postsynaptic neuron for a period of time: this time window can be used to strengthen/weaken synaptic pathways. An obstacle avoidance task is used for the performance analysis and results show that the SNN based robot controller has autonomous learning capabilities under the dynamic conditions.

Assessing Algorithm Parameter Importance Using Global Sensitivity Analysis

Lecture Notes in Computer Science, 2019

In general, biologically-inspired multi-objective optimization algorithms comprise several parame... more In general, biologically-inspired multi-objective optimization algorithms comprise several parameters which values have to be selected ahead of running the algorithm. In this paper we describe a global sensitivity analysis framework that enables a better understanding of the effects of parameters on algorithm performance. For this work, we tested NSGA-III and MOEA/D on multi-objective optimization testbeds, undertaking our proposed sensitivity analysis techniques on the relevant metrics, namely Generational Distance, Inverted Generational Distance, and Hypervolume. Experimental results show that both algorithms are most sensitive to the cardinality of the population. In all analyses, two clusters of parameter usually appear: (1) the population size (Pop) and (2) the Crossover Distribution Index, Crossover Probability, Mutation Distribution Index and Mutation Probability; where the first cluster, Pop, is the most important (sensitive) parameter with respect to the others. Choosing the correct population size for the tested algorithms has a significant impact on the solution accuracy and algorithm performance. It was already known how important the population of an evolutionary algorithm was, but it was not known its importance compared to the remaining parameters. The distance between the two clusters shows how crucial the size of the population is, compared to the other parameters. Detailed analysis clearly reveals a hierarchy of parameters: on the one hand the size of the population, on the other the remaining parameters that are always grouped together (in a single cluster) without a possible significant distinction. In fact, the other parameters all have the same importance, a secondary relevance for the performance of the algorithms, something which, to date, has not been observed in the evolutionary algorithm literature. The methodology designed in this paper can be adopted to evaluate the importance of the parameters of any algorithm.

Strategies for calibrating models of biology

Briefings in Bioinformatics, Sep 18, 2018

Computational and mathematical modelling has become a valuable tool for investigating biological ... more Computational and mathematical modelling has become a valuable tool for investigating biological systems. Modelling enables prediction of how biological components interact to deliver system-level properties, and extrapolation of biological system performance to contexts and experimental conditions where this is unknown. A model's value hinges on knowing that it faithfully represents the biology under the contexts of use, or clearly ascertaining otherwise and thus motivating further model refinement. These qualities are evaluated through calibration, typically formulated as identifying model parameter values that align model and biological behaviours as measured through a metric applied to both. Calibration is critical to modelling, but is often under-appreciated. A failure to appropriately calibrate risks unrepresentative models that generate erroneous insights. Here we review a suite of strategies to more rigorously challenge a model's representation of a biological system. All are motivated by features of biological systems, and illustrative examples are drawn from the modelling literature. We examine the calibration of a model against distributions of biological behaviours or outcomes, not only average values. We argue for calibration even where model parameter values are experimentally ascertained. We explore how single metrics can be non-distinguishing for complex systems, with multiple component dynamic and interaction configurations giving rise to the same metric output. Under these conditions, calibration is insufficiently constraining and the model non-identifiable: multiple solutions to the calibration problem exist. We draw an analogy to curve fitting and argue that calibrating a biological model against a single experiment or context is akin to curve fitting against a single data point. Though useful for communicating model results, we explore how metrics that quantify heavily emergent properties may not be suitable for use 4 in calibration. Lastly, we consider the role of sensitivity and uncertainty analysis in calibration and the interpretation of model results. Our goal in this manuscript is to encourage deeper a consideration of calibration, and how to increase its capacity to either deliver faithful models or demonstrate them otherwise.

Sample and time efficient policy learning with CMA-ES and Bayesian Optimisation

In evolutionary robot systems where morphologies and controllers of real robots are simultaneousl... more In evolutionary robot systems where morphologies and controllers of real robots are simultaneously evolved, it is clear that there is likely to be requirements to refine the inherited controller of a 'newborn' robot in order to better align it to its newly generated morphology. This can be accomplished via a learning mechanism applied to each individual robot: for practical reasons, such a mechanism should be both sample and time-efficient. In this paper, We investigate two ways to improve the sample and time efficiency of the well-known learner CMA-ES on navigation tasks. The first approach combines CMA-ES with Novelty Search, and includes an adaptive restart mechanism with increasing population size. The second bootstraps CMA-ES using Bayesian Optimisation, known for its sample efficiency. Results using two robots built with the ARE project's modules and four environments show that novelty reduces the number of samples needed to converge, as does the custom restart mechanism; the latter also has better sample and time efficiency than the hybridised Bayesian/Evolutionary method.

Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies

Swarm and Evolutionary Computation

We present a comprehensive global sensitivity analysis of two single-objective and two multi-obje... more We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of their direct effect and interaction effect with other hyperparameters. Using three sensitivity analysis methods, Morris LHS, Morris, and Sobol, to systematically analyze tunable hyperparameters of covariance matrix adaptation evolutionary strategy, differential evolution, non-dominated sorting genetic algorithm III, and multi-objective evolutionary algorithm based on decomposition, the framework reveals the behaviors of hyperparameters to sampling methods and performance metrics. That is, it answers questions like what hyperparameters influence patterns, how they interact, how much they interact, and how much their direct influence is. Consequently, the ranking of hyperparameters suggests their order of tuning, and the pattern of influence reveals the stability of the algorithms.

Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

IEEE Transactions on Cognitive and Developmental Systems, 2022

The joint optimisation of body-plan and control via evolutionary processes can be challenging in ... more The joint optimisation of body-plan and control via evolutionary processes can be challenging in rich morphological spaces in which offspring can have body-plans that are very different from either of their parents. This causes a potential mismatch between the structure of an inherited controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body-plans and a learning algorithm to optimise the parameters of a neural controller. The topology of this controller is created once the body-plan of each offspring body-plan is generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit 'types' of robots (where this is defined with respect the features of the body-plan). By learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialised one, we show that both the speed and magnitude of learning increases over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.

Review Computational Models of the NF-κB Signalling Pathway

In this review article, we discuss the current state of computational modelling of the nuclear fa... more In this review article, we discuss the current state of computational modelling of the nuclear factor-kappa B (NF-κB) signalling pathway. NF-κB is a transcription factor, which is ubiquitous within cells and controls a number of immune responses, including inflammation and apoptosis. The NF-κB signalling pathway is tightly regulated, commencing with activation at the cell membrane, signal transduction through various components within the cytoplasm, translocation of NF-κB into the nucleus and, finally, the transcription of various genes relating to the innate and adaptive immune responses. There have been a number of computational (mathematical) models developed of the signalling pathway over the past decade. This review describes how these approaches have helped advance our understanding of NF-κB control.

Bio-inspired Anomaly Detection for Low-cost Gas Sensors

2018 IEEE 18th International Conference on Nanotechnology (IEEE-NANO), 2018

This paper proposes a novel anomaly detection method for gas sensors using spiking neural network... more This paper proposes a novel anomaly detection method for gas sensors using spiking neural network principles. The synapse models with excitatory/inhibitory responses and a single spiking neuron are employed to develop the bio-inspired anomaly detector for a single gas sensor. The approach can detect anomalies in the data, which is collected by the gas sensor by identifying rapid changes rather than a magnitude threshold. In particular, the false-positive detections due to the drifts of low-cost sensors are minimised using the proposed bio-inspired approach. Using the chemicals of surgical spirits and isobutanol as test substances, experiments were carried out to evaluate the proposed method. Results demonstrate that gas anomalies can be detected when the chemical substances are presented to the sensor. In addition, results show that the approach can detect under the presence of sensor drift. The proposed bio-inspired detector was implemented on FPGA hardware, which demonstrates rela...

The ARE Robot Fabricator: How to (Re)produce Robots that Can Evolve in the Real World

The 2019 Conference on Artificial Life, 2019

The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of... more The long term vision of the Autonomous Robot Evolution (ARE) project is to create an ecosystem of both virtual and physical robots with evolving brains and bodies. One of the major challenges for such a vision is the need to construct many unique individuals without prior knowledge of what designs evolution will produce. To this end, an autonomous robot fabrication system for evolutionary robotics, the Robot Fabricator, is introduced in this paper. Evolutionary algorithms can create robot designs without direct human interaction; the Robot Fabricator will extend this to create physical copies of these designs (phenotypes) without direct human interaction. The Robot Fabricator will receive genomes and produce populations of physical individuals that can then be evaluated, allowing this to form part of the evolutionary loop, so robotic evolution is not confined to simulation and the reality gap is minimised. In order to allow the production of robot bodies with the widest variety of shapes and functional parts, individuals will be produced through 3D printing, with prefabricated actuators and sensors autonomously attached in the positions determined by evolution. This paper presents details of the proposed physical system, including a proof-ofconcept demonstrator, and discusses the importance of considering the physical manufacture for evolutionary robotics.

Towards a platform model of the IL-1 stimulated NF-kB signalling pathway using communicating stream X-machines

The Nuclear Factor-kappa B (NF-κB) signalling pathway is one of the key signalling pathways invol... more The Nuclear Factor-kappa B (NF-κB) signalling pathway is one of the key signalling pathways involved in the control and regulation of the immune system [3]. Activation of the NF-κB transcription factor is a tightly regulated event, with NF-κB normally sequestered in the cytosol of non-stimulated cells. Following activation of a cell membrane receptor and propagation of the signal via intracellular signalling to the IκB Kinase (IKK), phosphorylation-induced degradation of IκB inhibitors occurs to facilitate the release of NF-κB and its translocation to the nucleus. Dysregulation of the pathway is known to be involved in a large number of inflammatory diseases. Although considerable research has been performed since its discovery in 1986, we are still not in a position to control the signalling pathway, and thus limit the effects of NF-κB within promotion of inflammatory diseases. Through adherence to the CoSMoS framework, we are developing a computational model of the IL-1 stimulated...

Hardware Design for Autonomous Robot Evolution

2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020

The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of ph... more The long term goal of the Autonomous Robot Evolution (ARE) project is to create populations of physical robots, in which both the controllers and body plans are evolved. The transition for evolutionary designs from purely simulation environments into the real world creates the possibility for new types of system able to adapt to unknown and changing environments. In this paper, a system for creating robots is introduced in order to allow for their body plans to be designed algorithmically and physically instantiated using the previously introduced Robot Fabricator. This system consists of two types of components. Firstly, skeleton parts are created bespoke for each design by 3D printing, allowing the overall shape of the robot to include almost infinite variety. To allow for the shortcomings of 3D printing, the second type of component are organs which contain components such as motors and sensors, and can be attached to the skeleton to provide particular functions. Specific organ designs are presented, with discussion of the design challenges for evolutionary robotics in hardware. The Robot Fabricator is extended to allow for robots with joints, and some example body plans shown to demonstrate the diversity possible using this system of robot generation.

GABA Regulation of Burst Firing in Hippocampal Astrocyte Neural Circuit: A Biophysical Model

Frontiers in Cellular Neuroscience, 2019

It is now widely accepted that glia cells and gamma-aminobutyric acidergic (GABA) interneurons dy... more It is now widely accepted that glia cells and gamma-aminobutyric acidergic (GABA) interneurons dynamically regulate synaptic transmission and neuronal activity in time and space. This paper presents a biophysical model that captures the interaction between an astrocyte cell, a GABA interneuron and pre/postsynaptic neurons. Specifically, GABA released from a GABA interneuron triggers in astrocytes the release of calcium (Ca 2+ ) from the endoplasmic reticulum via the inositol 1, 4, 5-trisphosphate (IP 3 ) pathway. This results in gliotransmission which elevates the presynaptic transmission probability rate (PR) causing weight potentiation and a gradual increase in postsynaptic neuronal firing, that eventually stabilizes. However, by capturing the complex interactions between IP 3 , generated from both GABA and the 2-arachidonyl glycerol (2-AG) pathway, and PR, this paper shows that this interaction not only gives rise to an initial weight potentiation phase but also this phase is followed by postsynaptic bursting behavior. Moreover, the model will show that there is a presynaptic frequency range over which burst firing can occur. The proposed model offers a novel cellular level mechanism that may underpin both seizure-like activity and neuronal synchrony across different brain regions.

The Psi Swarm: A Low-Cost Robotics Platform and Its Use in an Education Setting

Lecture Notes in Computer Science, 2016

The paper introduces the Psi Swarm robot, a platform developed to allow both affordable research ... more The paper introduces the Psi Swarm robot, a platform developed to allow both affordable research in swarm robotics and versatility for teaching programming and robotics concepts. Motivated by the goals of reducing cost and construction complexity of existing swarm robotics platforms, we have developed a trackable, sensor-rich and expandable platform which needs only a computer with internet browser and no additional software to program. This paper outlines the design of the platform and the development of a tablet-computer based programming environment for the robot, intended to teach primary school aged children programming concepts.