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  • Journal article
    Cheng S, Jin Y, Harrison S, Quilodrán Casas C, Prentice C, Guo Y-K, Arcucci Ret al., 2022,

    Parameter flexible wildfire prediction using machine learning techniques: forward and inverse modelling

    , Remote Sensing, Vol: 14, ISSN: 2072-4292

    Parameter identification for wildfire forecasting models often relies on case-by-case tuning or posterior diagnosis/analysis, which can be computationally expensive due to the complexity of the forward prediction model. In this paper, we introduce an efficient parameter flexible fire prediction algorithm based on machine learning and reduced order modelling techniques. Using a training dataset generated by physics-based fire simulations, the method forecasts burned area at different time steps with a low computational cost. We then address the bottleneck of efficient parameter estimation by developing a novel inverse approach relying on data assimilation techniques (latent assimilation) in the reduced order space. The forward and the inverse modellings are tested on two recent large wildfire events in California. Satellite observations are used to validate the forward prediction approach and identify the model parameters. By combining these forward and inverse approaches, the system manages to integrate real-time observations for parameter adjustment, leading to more accurate future predictions.
  • Conference paper
    Grillotti L, Cully A, 2022,

    Relevance-guided unsupervised discovery of abilities with quality-diversity algorithms

    , Genetic and Evolutionary Computation Conference (GECCO), Publisher: ACM, Pages: 77-85

    Quality-Diversity algorithms provide efficient mechanisms to generate large collections of diverse and high-performing solutions, which have shown to be instrumental for solving downstream tasks. However, most of those algorithms rely on a behavioural descriptor to characterise the diversity that is hand-coded, hence requiring prior knowledge about the considered tasks. In this work, we introduce Relevance-guided Unsupervised Discovery of Abilities; a Quality-Diversity algorithm that autonomously finds a behavioural characterisation tailored to the task at hand. In particular, our method introduces a custom diversity metric that leads to higher densities of solutions near the areas of interest in the learnt behavioural descriptor space. We evaluate our approach on a simulated robotic environment, where the robot has to autonomously discover its abilities based on its full sensory data. We evaluated the algorithms on three tasks: navigation to random targets, moving forward with a high velocity, and performing half-rolls. The experimental results show that our method manages to discover collections of solutions that are not only diverse, but also well-adapted to the considered downstream task.
  • Book chapter
    Lever J, Arcucci R, 2022,

    Towards Social Machine Learning for Natural Disasters

    , Computational Science – ICCS 2022 22nd International Conference, London, UK, June 21–23, 2022, Proceedings, Part III, Publisher: Springer, Pages: 756-769, ISBN: 9783031087561

    The four-volume set LNCS 13350, 13351, 13352, and 13353 constitutes the proceedings of the 22ndt International Conference on Computational Science, ICCS 2022, held in London, UK, in June 2022.* The total of 175 full papers and 78 short ...
  • Journal article
    Schneider R, Bonavita M, Geer A, Arcucci R, Dueben P, Vitolo C, Le Saux B, Demir B, Mathieu P-Pet al., 2022,

    ESA-ECMWF Report on recent progress and research directions in machine learning for Earth System observation and prediction

    , NPJ CLIMATE AND ATMOSPHERIC SCIENCE, Vol: 5, ISSN: 2397-3722
  • Conference paper
    Ward F, Belardinelli F, Toni F, 2022,

    Argumentative Reward Learning: Reasoning About Human Preferences

    , HMCaT 2022 (ICML)
  • Conference paper
    Ward F, Belardinelli F, Toni F, 2022,

    Argumentative Reward Learning: Reasoning About Human Preferences

    , MPREF 2022 (IJCAI-ECAI 2022)
  • Conference paper
    Ward F, Toni F, Belardinelli F, 2022,

    A Casual Perspective on AI Deception

    , CAUSAL 22 (ICLP)
  • Conference paper
    Irwin B, Rago A, Toni F, 2022,

    Argumentative forecasting

    , AAMAS 2022, Publisher: ACM, Pages: 1636-1638

    We introduce the Forecasting Argumentation Framework (FAF), anovel argumentation framework for forecasting informed by re-cent judgmental forecasting research. FAFs comprise update frame-works which empower (human or artificial) agents to argue overtime with and about probability of scenarios, whilst flagging per-ceived irrationality in their behaviour with a view to improvingtheir forecasting accuracy. FAFs include three argument types withfuture forecasts and aggregate the strength of these arguments toinform estimates of the likelihood of scenarios. We describe animplementation of FAFs for supporting forecasting agents.
  • Journal article
    Dmitrewski A, Molina-Solana M, Arcucci R, 2022,

    CNTRLDA: A building energy management control system with real-time adjustments. Application to indoor temperature

    , BUILDING AND ENVIRONMENT, Vol: 215, ISSN: 0360-1323
  • Journal article
    Thanaj M, Mielke J, McGurk K, Bai W, Savioli N, Simoes Monteiro de Marvao A, Meyer H, Zeng L, Sohler F, Lumbers T, Wilkins M, Ware J, Bender C, Rueckert D, MacNamara A, Freitag D, O'Regan Det al., 2022,

    Genetic and environmental determinants of diastolic heart function

    , Nature Cardiovascular Research, Vol: 1, Pages: 361-371, ISSN: 2731-0590

    Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends onmyocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processesand is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiacmotion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wideassociation study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomericfunction under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes wereindependent predictors of diastolic function and we found a causal relationship between genetically-determined ventricularstiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolicfunction that are relevant for identifying causal relationships and potential tractable targets.
  • Conference paper
    Henriksen P, Leofante F, Lomuscio A, 2022,

    Repairing misclassifications in neural networks using limited data

    , SAC '22, Pages: 1031-1038

    We present a novel and computationally efficient method for repairing a feed-forward neural network with respect to a finite set of inputs that are misclassified. The method assumes no access to the training set. We present a formal characterisation for repairing the neural network and study its resulting properties in terms of soundness and minimality. We introduce a gradient-based algorithm that performs localised modifications to the network's weights such that misclassifications are repaired while marginally affecting network accuracy on correctly classified inputs. We introduce an implementation, I-REPAIR, and show it is able to repair neural networks while reducing accuracy drops by up to 90% when compared to other state-of-the-art approaches for repair.
  • Conference paper
    Rago A, Russo F, Albini E, Baroni P, Toni Fet al., 2022,

    Forging argumentative explanations from causal models

    , Proceedings of the 5th Workshop on Advances in Argumentation in Artificial Intelligence 2021 co-located with the 20th International Conference of the Italian Association for Artificial Intelligence (AIxIA 2021), Publisher: CEUR Workshop Proceedings, Pages: 1-15, ISSN: 1613-0073

    We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for models' outputs. The conceptualisation is based on reinterpreting properties of semantics of AFs as explanation moulds, which are means for characterising argumentative relations. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement in bipolar AFs, showing how the extracted bipolar AFs may be used as relation-based explanations for the outputs of causal models.
  • Journal article
    AlAttar A, Chappell D, Kormushev P, 2022,

    Kinematic-model-free predictive control for robotic manipulator target reaching with obstacle avoidance

    , Frontiers in Robotics and AI, Vol: 9, Pages: 1-9, ISSN: 2296-9144

    Model predictive control is a widely used optimal control method for robot path planning andobstacle avoidance. This control method, however, requires a system model to optimize controlover a finite time horizon and possible trajectories. Certain types of robots, such as softrobots, continuum robots, and transforming robots, can be challenging to model, especiallyin unstructured or unknown environments. Kinematic-model-free control can overcome thesechallenges by learning local linear models online. This paper presents a novel perception-basedrobot motion controller, the kinematic-model-free predictive controller, that is capable of controllingrobot manipulators without any prior knowledge of the robot’s kinematic structure and dynamicparameters and is able to perform end-effector obstacle avoidance. Simulations and physicalexperiments were conducted to demonstrate the ability and adaptability of the controller toperform simultaneous target reaching and obstacle avoidance.
  • Journal article
    Wang K, Fei H, Kormushev P, 2022,

    Fast online optimization for terrain-blind bipedal robot walking with a decoupled actuated SLIP model

    , Frontiers in Robotics and AI, Vol: 9, Pages: 1-11, ISSN: 2296-9144

    We present an online optimization algorithm which enables bipedal robots to blindly walk overvarious kinds of uneven terrains while resisting pushes. The proposed optimization algorithmperforms high level motion planning of footstep locations and center-of-mass height variationsusing the decoupled actuated Spring Loaded Inverted Pendulum (aSLIP) model. The decoupledaSLIP model simplifies the original aSLIP with Linear Inverted Pendulum (LIP) dynamics inhorizontal states and spring dynamics in the vertical state. The motion planning can beformulated as a discrete-time Model Predictive Control (MPC) problem and solved at a frequencyof 1 kHz. The output of the motion planner is fed into an inverse-dynamics based whole bodycontroller for execution on the robot. A key result of this controller is that the feet of the robot arecompliant, which further extends the robot’s ability to be robust to unobserved terrain variations.We evaluate our method in simulation with the bipedal robot SLIDER. Results show the robotcan blindly walk over various uneven terrains including slopes, wave fields and stairs. It can alsoresist pushes of up to 40 N for a duration of 0.1 s while walking on uneven terrain.
  • Journal article
    Cursi F, Bai W, Yeatman EM, Kormushev Pet al., 2022,

    GlobDesOpt: a global optimization framework for optimal robot manipulator design

    , IEEE Access, Vol: 10, Pages: 5012-5023, ISSN: 2169-3536

    Robot design is a major component in robotics, as it allows building robots capable of performing properly in given tasks. However, designing a robot with multiple types of parameters and constraints and defining an optimization function analytically for the robot design problem may be intractable or even impossible. Therefore black-box optimization approaches are generally preferred. In this work we propose GlobDesOpt, a simple-to-use open-source optimization framework for robot design based on global optimization methods. The framework allows selecting various design parameters and optimizing for both single and dual-arm robots. The functionalities of the framework are shown here to optimally design a dual-arm surgical robot, comparing the different two optimization strategies.
  • Journal article
    Buizza C, Casas CQ, Nadler P, Mack J, Marrone S, Titus Z, Le Cornec C, Heylen E, Dur T, Ruiz LB, Heaney C, Lopez JAD, Kumar KSS, Arcucci Ret al., 2022,

    Data Learning: Integrating Data Assimilation and Machine Learning

    , JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 58, ISSN: 1877-7503
  • Conference paper
    Cursi F, Chappell D, Kormushev P, 2022,

    Augmenting loss functions of feedforward neural networks with differential relationships for robot kinematic modelling

    , Ljubljana, Slovenia, 20th International Conference on Advanced Robotics (ICAR), Publisher: IEEE, Pages: 201-207

    Model learning is a crucial aspect of robotics as it enables the use of traditional and consolidated model-based controllers to perform desired motion tasks. However, due to the increasing complexity of robotic structures, modelling robots is becoming more and more challenging, and analytical models are very difficult to build, particularly for redundant robots. Machine learning approaches have shown great capabilities in learning complex mapping and have widely been used in robot model learning and control. Generally, inverse kinematics is learned, directly obtaining the desired control commands given a desired task. However, learning forward kinematics is simpler and allows the computation of the robot Jacobian and enables the exploitation of the optimality of controllers. Nevertheless, typical learning methods have no knowledge about the differential relationship between the position and velocity mappings. In this work, we present two novel loss functions to train feedforward Artificial Neural network (ANN) which incorporate this information in learning the forward kinematic model of robotic structures, and carry out a comparison with standard ANN training using position data only. Simulation results show that incorporating the knowledge of the velocity mapping improves the suitability of the learnt model for control tasks.
  • Conference paper
    Cheng S, Quilodran-Casas C, Arcucci R, 2022,

    Reduced Order Surrogate Modelling and Latent Assimilation for Dynamical Systems

    , 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 31-44, ISSN: 0302-9743
  • Conference paper
    Lever J, Arcucci R, 2022,

    Towards Social Machine Learning for Natural Disasters

    , 22nd Annual International Conference on Computational Science (ICCS), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 756-769, ISSN: 0302-9743
  • Conference paper
    Lever J, Arcucci R, Cai J, 2022,

    Social Data Assimilation of Human Sensor Networks for Wildfires

    , 15th ACM International Conference on Pervasive Technologies Related to Assistive Environments (PETRA), Publisher: ASSOC COMPUTING MACHINERY, Pages: 455-462

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