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  • Journal article
    Cheng S, Chen J, Anastasiou C, Angeli P, Matar OKK, Guo Y-K, Pain CCC, Arcucci Ret al., 2023,

    Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models

  • Journal article
    Grillotti L, Cully A, 2022,

    Unsupervised behaviour discovery with quality-diversity optimisation

    , IEEE Transactions on Evolutionary Computation, Vol: 26, Pages: 1539-1552, ISSN: 1089-778X

    Quality-Diversity algorithms refer to a class of evolutionary algorithms designed to find a collection of diverse and high-performing solutions to a given problem. In robotics, such algorithms can be used for generating a collection of controllers covering most of the possible behaviours of a robot. To do so, these algorithms associate a behavioural descriptor to each of these behaviours. Each behavioural descriptor is used for estimating the novelty of one behaviour compared to the others. In most existing algorithms, the behavioural descriptor needs to be hand-coded, thus requiring prior knowledge about the task to solve. In this paper, we introduce: Autonomous Robots Realising their Abilities, an algorithm that uses a dimensionality reduction technique to automatically learn behavioural descriptors based on raw sensory data. The performance of this algorithm is assessed on three robotic tasks in simulation. The experimental results show that it performs similarly to traditional hand-coded approaches without the requirement to provide any hand-coded behavioural descriptor. In the collection of diverse and high-performing solutions, it also manages to find behaviours that are novel with respect to more features than its hand-coded baselines. Finally, we introduce a variant of the algorithm which is robust to the dimensionality of the behavioural descriptor space.

  • Conference paper
    Zhang K, Toni F, Williams M, 2023,

    A federated cox model with non-proportional hazards

    , The 6th International Workshop on ​Health Intelligence, Publisher: Springer, ISSN: 1860-949X

    Recent research has shown the potential for neural networksto improve upon classical survival models such as the Cox model, whichis widely used in clinical practice. Neural networks, however, typicallyrely on data that are centrally available, whereas healthcare data arefrequently held in secure silos. We present a federated Cox model thataccommodates this data setting and also relaxes the proportional hazardsassumption, allowing time-varying covariate effects. In this latter respect,our model does not require explicit specification of the time-varying ef-fects, reducing upfront organisational costs compared to previous works.We experiment with publicly available clinical datasets and demonstratethat the federated model is able to perform as well as a standard model.

  • Conference paper
    Potyka N, Yin X, Toni F, 2022,

    Explaining random forests using bipolar argumentation and Markov networks

    , AAAI 23, Publisher: AAAI, ISSN: 2159-5399

    Random forests are decision tree ensembles that can be used to solve a variety of machine learning problems. However, as the number of trees and their individual size can be large, their decision making process is often incomprehensible. We show that their decision process can be naturally represented as an argumentation problem, which allows creating global explanations via argumentative reasoning. We generalize sufficientand necessary argumentative explanations using a Markov network encoding, discuss the relevance of these explanations and establish relationships to families of abductive explanations from the literature. As the complexity of the explanation problems is high, we present an efficient approximation algorithm with probabilistic approximation guarantees.

  • Conference paper
    Jiang J, Leofante F, Rago A, Toni Fet al., 2022,

    Formalising the robustness of counterfactual explanations for neural networks

    , The 37th AAAI Conference on Artificial Intelligence, Publisher: Association for the Advancement of Artificial Intelligence

    The use of counterfactual explanations (CFXs) is an increasingly popular explanation strategy for machine learning models. However, recent studies have shown that these explanations may not be robust to changes in the underlying model (e.g., following retraining), which raises questions about their reliability in real-world applications. Existing attempts towards solving this problem are heuristic, and the robustness to model changes of the resulting CFXs is evaluated with only a small number of retrained models, failing to provide exhaustive guarantees. To remedy this, we propose the first notion to formally and deterministically assess the robustness (to model changes) of CFXs for neural networks, that we call ∆-robustness. We introduce an abstraction framework based on interval neural networks to verify the ∆-robustness of CFXs against a possibly infinite set of changes to the model parameters, i.e., weights and biases. We then demonstrate the utility of this approach in two distinct ways. First, we analyse the ∆-robustness of a number of CFX generation methods from the literature and show that they unanimously host significant deficiencies in this regard. Second, we demonstrate how embedding ∆-robustness within existing methods can provide CFXs which are provably robust.

  • Conference paper
    Albini E, Rago A, Baroni P, Toni Fet al., 2022,

    Descriptive accuracy in explanations: the case of probabilistic classifiers

    , 15th International Conference on Scalable Uncertainty Management (SUM 2022)

    A user receiving an explanation for outcomes produced byan artificially intelligent system expects that it satisfies the key propertyof descriptive accuracy (DA), i.e. that the explanation contents are incorrespondence with the internal working of the system. Crucial as thisproperty appears to be, it has been somehow overlooked in the XAI literature to date. To address this problem, we consider the questions offormalising DA and of analysing its satisfaction by explanation methods. We provide formal definitions of naive, structural and dialecticalDA, using the family of probabilistic classifiers as the context for ouranalysis. We evaluate the satisfaction of our given notions of DA by several explanation methods, amounting to two popular feature-attributionmethods from the literature and a novel form of explanation that wepropose and complement our analysis with experiments carried out on avaried selection of concrete probabilistic classifiers.

  • Conference paper
    Maurizio P, Toni F, 2022,

    Learning assumption-based argumentation frameworks

    , 31st International Conference on Inductive Logic Programming (ILP 2022)

    . We propose a novel approach to logic-based learning whichgenerates assumption-based argumentation (ABA) frameworks from positive and negative examples, using a given background knowledge. TheseABA frameworks can be mapped onto logic programs with negationas failure that may be non-stratified. Whereas existing argumentationbased methods learn exceptions to general rules by interpreting the exceptions as rebuttal attacks, our approach interprets them as undercutting attacks. Our learning technique is based on the use of transformationrules, including some adapted from logic program transformation rules(notably folding) as well as others, such as rote learning and assumptionintroduction. We present a general strategy that applies the transformation rules in a suitable order to learn stratified frameworks, and we alsopropose a variant that handles the non-stratified case. We illustrate thebenefits of our approach with a number of examples, which show that,on one hand, we are able to easily reconstruct other logic-based learningapproaches and, on the other hand, we can work out in a very simpleand natural way problems that seem to be hard for existing techniques.

  • Conference paper
    Potyka N, Yin X, Toni F, 2022,

    On the tradeoff between correctness and completeness in argumentative explainable AI

    , 1st International Workshop on Argumentation for eXplainable AI, Publisher: CEUR Workshop Proceedings, Pages: 1-8, ISSN: 1613-0073

    Explainable AI aims at making the decisions of autonomous systems human-understandable. Argumentation frameworks are a natural tool for this purpose. Among them, bipolar abstract argumentation frameworks seem well suited to explain the effect of features on a classification decision and their formal properties can potentially be used to derive formal guarantees for explanations. Two particular interesting properties are correctness (if the explanation says that X affects Y, then X affects Y ) and completeness (if X affects Y, then the explanation says that X affects Y ). The reinforcement property of bipolar argumentation frameworks has been used as a natural correctness counterpart in previous work. Applied to the classification context, it basically states that attacking features should decrease and supporting features should increase the confidence of a classifier. In this short discussion paper, we revisit this idea, discuss potential limitations when considering reinforcement without a corresponding completeness property and how these limitations can potentially be overcome.

  • Journal article
    Gong H, Cheng S, Chen Z, Li Q, Quilodran-Casas C, Xiao D, Arcucci Ret al., 2022,

    An efficient digital twin based on machine learning SVD autoencoder and generalised latent assimilation for nuclear reactor physics

    , ANNALS OF NUCLEAR ENERGY, Vol: 179, ISSN: 0306-4549
  • Journal article
    Zhuang Y, Cheng S, Kovalchuk N, Simmons M, Matar OK, Guo Y-K, Arcucci Ret al., 2022,

    Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device

    , Lab on a Chip: miniaturisation for chemistry, physics, biology, materials science and bioengineering, Vol: 22, Pages: 3187-3202, ISSN: 1473-0189

    A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelling techniques are applied to compress the recorded images into low-dimensional spaces and alleviate the computational cost. Recurrent neural networks are then employed to build a surrogate model of drop interactions by learning the dynamics of compressed variables in the reduced-order space. The surrogate model is integrated with real-time observations using data assimilation. In this paper we developed an ensemble-based latent assimilation algorithm scheme which shows an improvement in terms of accuracy with respect to the previous approaches. This work demonstrates the possibility to create a reliable data-driven model enabling a high fidelity prediction of drop interactions in microfluidics device. The performance of the developed system is evaluated against experimental data (i.e., recorded videos), which are excluded from the training of the surrogate model. The developed scheme is general and can be applied to other dynamical systems.

  • Journal article
    Chagot L, Quilodran-Casas C, Kalli M, Kovalchuk NM, Simmons MJH, Matar OK, Arcucci R, Angeli Pet al., 2022,

    Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach

    , LAB ON A CHIP, Vol: 22, Pages: 3848-3859, ISSN: 1473-0197
  • Journal article
    Cheng S, Prentice IC, Huang Y, Jin Y, Guo Y-K, Arcucci Ret al., 2022,

    Data-driven surrogate model with latent data-assimilation: application to wildfire forecasting

    , Journal of Computational Physics, Vol: 464, ISSN: 0021-9991

    The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.

  • Journal article
    Lever J, Arcucci R, 2022,

    Sentimental wildfire: a social-physics machine learning model for wildfire nowcasting

    , Journal of Computational Social Science, Vol: 5, Pages: 1427-1465, ISSN: 2432-2717

    The intensity of wildfires and wildfire season length is increasing due to climate change, causing a greater threat to the local population. Much of this population are increasingly adopting social media, and sites like Twitter are increasingly being used as a real-time human-sensor network during natural disasters; detecting, tracking and documenting events. The human-sensor concept is currently largely omitted by wildfire models, representing a potential loss of information. By including Twitter data as a source in our models, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This paper implements machine learning in a wildfire prediction model, using social media and geophysical data sources with Sentiment Analysis to predict wildfire characteristics with high accuracy. We also use wildfire-specific attributes to predict online social dynamics, as this has been shown to be indicative of localised disaster severity. This may be useful for disaster management teams in identifying areas of immediate danger. We combine geophysical satellite data from the Global Fire Atlas with social data provided by Twitter. We perform data collection and subsequent analysis & visualisation, and compare regional differences in online social sentiment expression. Following this, we compare and contrast different machine learning models for predicting wildfire attributes. We demonstrate social media is a predictor of wildfire activity, and present models which accurately model wildfire attributes. This work develops the concept of the human sensor in the context of wildfires, using users’ Tweets as noisy subjective sentimental accounts of current localised conditions. This work contributes to the development of more socially conscious wildfire models, by incorporating social media data into wildfire prediction and modelling.

  • Conference paper
    Sukpanichnant P, Rago A, Lertvittayakumjorn P, Toni Fet al., 2022,

    Neural QBAFs: explaining neural networks under LRP-based argumentation frameworks

    , International Conference of the Italian Association for Artificial Intelligence, Publisher: Springer International Publishing, Pages: 429-444, ISSN: 0302-9743

    In recent years, there have been many attempts to combine XAI with the field of symbolic AI in order to generate explanations for neural networks that are more interpretable and better align with human reasoning, with one prominent candidate for this synergy being the sub-field of computational argumentation. One method is to represent neural networks with quantitative bipolar argumentation frameworks (QBAFs) equipped with a particular semantics. The resulting QBAF can then be viewed as an explanation for the associated neural network. In this paper, we explore a novel LRP-based semantics under a new QBAF variant, namely neural QBAFs (nQBAFs). Since an nQBAF of a neural network is typically large, the nQBAF must be simplified before being used as an explanation. Our empirical evaluation indicates that the manner of this simplification is all important for the quality of the resulting explanation.

  • Conference paper
    Lim BWT, Grillotti L, Bernasconi L, Cully Aet al., 2022,

    Dynamics-aware quality-diversity for efficient learning of skill repertoires

    , IEEE International Conference on Robotics and Automation, Publisher: IEEE, Pages: 5360-5366

    Quality-Diversity (QD) algorithms are powerful exploration algorithms that allow robots to discover large repertoires of diverse and high-performing skills. However, QD algorithms are sample inefficient and require millionsof evaluations. In this paper, we propose Dynamics-Aware Quality-Diversity (DA-QD), a framework to improve the sample efficiency of QD algorithms through the use of dynamics models. We also show how DA-QD can then be used for continual acquisition of new skill repertoires. To do so, weincrementally train a deep dynamics model from experience obtained when performing skill discovery using QD. We can then perform QD exploration in imagination with an imagined skill repertoire. We evaluate our approach on three robotic experiments. First, our experiments show DA-QD is 20 timesmore sample efficient than existing QD approaches for skill discovery. Second, we demonstrate learning an entirely new skill repertoire in imagination to perform zero-shot learning. Finally, we show how DA-QD is useful and effective for solving a long horizon navigation task and for damage adaptation in the real world. Videos and source code are available at:

  • Conference paper
    Lim BWT, Reichenbach A, Cully A, 2022,

    Learning to walk autonomously via reset-free quality-diversity

    , The Genetic and Evolutionary Computation Conference (GECCO)

    Quality-Diversity (QD) algorithms can discover large and complex behavioural repertoires consisting of both diverse and high-performing skills. However, the generation of behavioural repertoires has mainly been limited to simulation environments instead of real-world learning. This is because existing QD algorithms need large numbers of evaluations as well as episodic resets, which require manual human supervision and interventions. This paper proposes Reset-Free Quality-Diversity optimization (RF-QD) as a step towards autonomous learning for robotics in open-ended environments. We build on Dynamics-Aware Quality-Diversity (DA-QD) and introduce a behaviour selection policy that leverages the diversity of the imagined repertoire and environmental information to intelligently select of behaviours that can act as automatic resets. We demonstrate this through a task of learning to walk within defined training zones with obstacles. Our experiments show that we can learn full repertoires of legged locomotion controllers autonomously without manual resets with high sample efficiency in spite of harsh safety constraints. Finally, using an ablation of different target objectives, we show that it is important for RF-QD to have diverse types solutions available for the behaviour selection policy over solutions optimised with a specific objective. Videos and code available at this https URL.

  • Conference paper
    Allard M, Smith Bize S, Chatzilygeroudis K, Cully Aet al., 2022,

    Hierarchical Quality-Diversity For Online Damage Recovery

    , The Genetic and Evolutionary Computation Conference, Publisher: ACM

    Adaptation capabilities, like damage recovery, are crucial for the deployment of robots in complex environments. Several works have demonstrated that using repertoires of pre-trained skills can enable robots to adapt to unforeseen mechanical damages in a few minutes. These adaptation capabilities are directly linked to the behavioural diversity in the repertoire. The more alternatives the robot has to execute a skill, the better are the chances that it can adapt to a new situation. However, solving complex tasks, like maze navigation, usually requires multiple different skills. Finding a large behavioural diversity for these multiple skills often leads to an intractable exponential growth of the number of required solutions.In this paper, we introduce the Hierarchical Trial and Error algorithm, which uses a hierarchical behavioural repertoire to learn diverse skills and leverages them to make the robot more adaptive to different situations. We show that the hierarchical decomposition of skills enables the robot to learn more complex behaviours while keeping the learning of the repertoire tractable. The experiments with a hexapod robot show that our method solves maze navigation tasks with 20% less actions in the most challenging scenarios than the best baseline while having 57% less complete failures.

  • 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.

  • Conference paper
    Lever J, Arcucci R, Cai J, 2022,

    Social Data Assimilation of Human Sensor Networks for Wildfires

    , Pages: 455-462

    We present an implementation of a human sensor network in the context of wildfires. A human sensor network can be thought of as a socially nuanced abstraction of a physical sensing model, where social media users are considered noisy remote sensors with variable reliability and location. This allows real-time social modelling of physical events. We apply this concept to data collected from Twitter & Reddit in the context of California wildfires, performing sentimental & topical analysis over the period of a wildfire season to extract themes, sentiments and discussions. We assimilate this social media data in a predictive model trained by machine learning approaches for time series. Both Long Short Term Memory (LSTM) & AutoRegressive Integrated Moving Average (ARIMA) models are employed. We assimilate the human sensor networks, to overcome the limitations & biases exhibited by individual social media platform demographics. We implement Optimal Interpolation and Ensemble Kalman Filter architectures on our models & data. Finally we compare and evaluate performance, and discuss how these implementations could benefit current wildfire models.

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