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  • CONFERENCE PAPER
    Altuncu MT, Mayer E, Yaliraki SN, Barahona Met al., 2018,

    From Text to Topics in Healthcare Records: An Unsupervised Graph Partitioning Methodology

    Electronic Healthcare Records contain large volumes of unstructured data,including extensive free text. Yet this source of detailed information oftenremains under-used because of a lack of methodologies to extract interpretablecontent in a timely manner. Here we apply network-theoretical tools to analysefree text in Hospital Patient Incident reports from the National HealthService, to find clusters of documents with similar content in an unsupervisedmanner at different levels of resolution. We combine deep neural networkparagraph vector text-embedding with multiscale Markov Stability communitydetection applied to a sparsified similarity graph of document vectors, andshowcase the approach on incident reports from Imperial College Healthcare NHSTrust, London. The multiscale community structure reveals different levels ofmeaning in the topics of the dataset, as shown by descriptive terms extractedfrom the clusters of records. We also compare a posteriori against hand-codedcategories assigned by healthcare personnel, and show that our approachoutperforms LDA-based models. Our content clusters exhibit good correspondencewith two levels of hand-coded categories, yet they also provide further medicaldetail in certain areas and reveal complementary descriptors of incidentsbeyond the external classification taxonomy.

  • CONFERENCE PAPER
    Altuncu MT, Yaliraki SN, Barahona M, 2018,

    Content-driven, unsupervised clustering of news articles through multiscale graph partitioning

    The explosion in the amount of news and journalistic content being generatedacross the globe, coupled with extended and instantaneous access to informationthrough online media, makes it difficult and time-consuming to monitor newsdevelopments and opinion formation in real time. There is an increasing needfor tools that can pre-process, analyse and classify raw text to extractinterpretable content; specifically, identifying topics and content-drivengroupings of articles. We present here such a methodology that brings togetherpowerful vector embeddings from Natural Language Processing with tools fromGraph Theory that exploit diffusive dynamics on graphs to reveal naturalpartitions across scales. Our framework uses a recent deep neural network textanalysis methodology (Doc2vec) to represent text in vector form and thenapplies a multi-scale community detection method (Markov Stability) topartition a similarity graph of document vectors. The method allows us toobtain clusters of documents with similar content, at different levels ofresolution, in an unsupervised manner. We showcase our approach with theanalysis of a corpus of 9,000 news articles published by Vox Media over oneyear. Our results show consistent groupings of documents according to contentwithout a priori assumptions about the number or type of clusters to be found.The multilevel clustering reveals a quasi-hierarchy of topics and subtopicswith increased intelligibility and improved topic coherence as compared toexternal taxonomy services and standard topic detection methods.

  • CONFERENCE PAPER
    Baroni P, Rago A, Toni F, 2018,

    How Many Properties Do We Need for Gradual Argumentation?

    , Publisher: AAAI Press
  • JOURNAL ARTICLE
    Biffi C, de Marvao A, Attard MI, Dawes TJW, Whiffin N, Bai W, Shi W, Francis C, Meyer H, Buchan R, Cook SA, Rueckert D, O'Regan DPet al., 2018,

    Three-dimensional cardiovascular imaging-genetics: a mass univariate framework

    , BIOINFORMATICS, Vol: 34, Pages: 97-103, ISSN: 1367-4803
  • JOURNAL ARTICLE
    Chamberlain B, Levy-Kramer J, Humby C, Deisenroth MPet al., 2018,

    Real-time community detection in full social networks on a laptop

    , PLoS ONE, Vol: 13, ISSN: 1932-6203

    For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide

  • CONFERENCE PAPER
    Kamthe S, Deisenroth MP, 2018,

    Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control.

    , Artificial Intelligence and Statistics, Publisher: PMLR, Pages: 1701-1710
  • JOURNAL ARTICLE
    Kormushev P, Ugurlu B, Caldwell DG, Tsagarakis NGet al., 2018,

    Learning to exploit passive compliance for energy-efficient gait generation on a compliant humanoid

    , Autonomous Robots, Pages: 1-17, ISSN: 0929-5593

    © 2018 Springer Science+Business Media, LLC, part of Springer Nature Modern humanoid robots include not only active compliance but also passive compliance. Apart from improved safety and dependability, availability of passive elements, such as springs, opens up new possibilities for improving the energy efficiency. With this in mind, this paper addresses the challenging open problem of exploiting the passive compliance for the purpose of energy efficient humanoid walking. To this end, we develop a method comprising two parts: an optimization part that finds an optimal vertical center-of-mass trajectory, and a walking pattern generator part that uses this trajectory to produce a dynamically-balanced gait. For the optimization part, we propose a reinforcement learning approach that dynamically evolves the policy parametrization during the learning process. By gradually increasing the representational power of the policy parametrization, it manages to find better policies in a faster and computationally efficient way. For the walking generator part, we develop a variable-center-of-mass-height ZMP-based bipedal walking pattern generator. The method is tested in real-world experiments with the bipedal robot COMAN and achieves a significant 18% reduction in the electric energy consumption by learning to efficiently use the passive compliance of the robot.

  • JOURNAL ARTICLE
    Muggleton S, Dai WZ, Sammut C, Tamaddoni-Nezhad A, Wen J, Zhou ZHet al., 2018,

    Meta-Interpretive Learning from noisy images

    , Machine Learning, Pages: 1-22, ISSN: 0885-6125

    © 2018 The Author(s) Statistical machine learning is widely used in image classification. However, most techniques (1) require many images to achieve high accuracy and (2) do not provide support for reasoning below the level of classification, and so are unable to support secondary reasoning, such as the existence and position of light sources and other objects outside the image. This paper describes an Inductive Logic Programming approach called Logical Vision which overcomes some of these limitations. LV uses Meta-Interpretive Learning (MIL) combined with low-level extraction of high-contrast points sampled from the image to learn recursive logic programs describing the image. In published work LV was demonstrated capable of high-accuracy prediction of classes such as regular polygon from small numbers of images where Support Vector Machines and Convolutional Neural Networks gave near random predictions in some cases. LV has so far only been applied to noise-free, artificially generated images. This paper extends LV by (a) addressing classification noise using a new noise-telerant version of the MIL system Metagol, (b) addressing attribute noise using primitive-level statistical estimators to identify sub-objects in real images, (c) using a wider class of background models representing classical 2D shapes such as circles and ellipses, (d) providing richer learnable background knowledge in the form of a simple but generic recursive theory of light reflection. In our experiments we consider noisy images in both natural science settings and in a RoboCup competition setting. The natural science settings involve identification of the position of the light source in telescopic and microscopic images, while the RoboCup setting involves identification of the position of the ball. Our results indicate that with real images the new noise-robust version of LV using a single example (i.e. one-shot LV) converges to an accuracy at least comparable to a thirty-shot statisti

  • CONFERENCE PAPER
    Olofsson S, Deisenroth MP, Misener R, 2018,

    Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches.

    , Publisher: JMLR.org, Pages: 3905-3914
  • CONFERENCE PAPER
    Pardo F, Tavakoli A, Levdik V, Kormushev Pet al., 2018,

    Time limits in reinforcement learning

    , International Conference on Machine Learning, Pages: 4042-4051

    In reinforcement learning, it is common to let anagent interact for a fixed amount of time with itsenvironment before resetting it and repeating theprocess in a series of episodes. The task that theagent has to learn can either be to maximize itsperformance over (i) that fixed period, or (ii) anindefinite period where time limits are only usedduring training to diversify experience. In thispaper, we provide a formal account for how timelimits could effectively be handled in each of thetwo cases and explain why not doing so can causestate-aliasing and invalidation of experience re-play, leading to suboptimal policies and traininginstability. In case (i), we argue that the termi-nations due to time limits are in fact part of theenvironment, and thus a notion of the remainingtime should be included as part of the agent’s in-put to avoid violation of the Markov property. Incase (ii), the time limits are not part of the envi-ronment and are only used to facilitate learning.We argue that this insight should be incorporatedby bootstrapping from the value of the state atthe end of each partial episode. For both cases,we illustrate empirically the significance of ourconsiderations in improving the performance andstability of existing reinforcement learning algo-rithms, showing state-of-the-art results on severalcontrol tasks.

  • CONFERENCE PAPER
    Sæmundsson S, Hofmann K, Deisenroth MP, 2018,

    Meta Reinforcement Learning with Latent Variable Gaussian Processes.

    , Uncertainty in Artificial Intelligence
  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

    ResQbot: A Mobile Rescue Robot for Casualty Extraction

    , Pages: 239-240

    © 2018 Authors. Performing search and rescue missions in disaster-struck environments is challenging. Despite the advances in the robotic search phase of the rescue missions, few works have been focused on the physical casualty extraction phase. In this work, we propose a mobile rescue robot that is capable of performing a safe casualty extraction routine. To perform this routine, this robot adopts a loco-manipulation approach. We have designed and built a mobile rescue robot platform called ResQbot as a proof of concept of the proposed system. We have conducted preliminary experiments using a sensorised human-sized dummy as a victim, to confirm that the platform is capable of performing a safe casualty extraction procedure.

  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

    Casualty Detection from 3D Point Cloud Data for Autonomous Ground Mobile Rescue Robots

    , SSRR 2018
  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

    Casualty Detection for Mobile Rescue Robots via Ground-Projected Point Clouds

  • CONFERENCE PAPER
    Saputra RP, Kormushev P, 2018,

    ResQbot: A Mobile Rescue Robot with Immersive Teleperception for Casualty Extraction

  • JOURNAL ARTICLE
    Schulz C, Toni F, 2018,

    On the responsibility for undecisiveness in preferred and stable labellings in abstract argumentation

    , Artificial Intelligence, Vol: 262, Pages: 301-335, ISSN: 0004-3702

    © 2018 Elsevier B.V. Different semantics of abstract Argumentation Frameworks (AFs) provide different levels of decisiveness for reasoning about the acceptability of conflicting arguments. The stable semantics is useful for applications requiring a high level of decisiveness, as it assigns to each argument the label “accepted” or the label “rejected”. Unfortunately, stable labellings are not guaranteed to exist, thus raising the question as to which parts of AFs are responsible for the non-existence. In this paper, we address this question by investigating a more general question concerning preferred labellings (which may be less decisive than stable labellings but are always guaranteed to exist), namely why a given preferred labelling may not be stable and thus undecided on some arguments. In particular, (1) we give various characterisations of parts of an AF, based on the given preferred labelling, and (2) we show that these parts are indeed responsible for the undecisiveness if the preferred labelling is not stable. We then use these characterisations to explain the non-existence of stable labellings. We present two types of characterisations, based on labellings that are more (or equally) committed than the given preferred labelling on the one hand, and based on the structure of the given AF on the other, and compare the respective AF parts deemed responsible. To prove that our characterisations indeed yield responsible parts, we use a notion of enforcement of labels through structural revision, by means of which the preferred labelling of the given AF can be turned into a stable labelling of the structurally revised AF. Rather than prescribing how this structural revision is carried out, we focus on the enforcement of labels and leave the engineering of the revision open to fulfil differing requirements of applications and information available to users.

  • CONFERENCE PAPER
    Tavakoli A, Pardo F, Kormushev P, 2018,

    Action Branching Architectures for Deep Reinforcement Learning

    Discrete-action algorithms have been central to numerous recent successes ofdeep reinforcement learning. However, applying these algorithms tohigh-dimensional action tasks requires tackling the combinatorial increase ofthe number of possible actions with the number of action dimensions. Thisproblem is further exacerbated for continuous-action tasks that require finecontrol of actions via discretization. In this paper, we propose a novel neuralarchitecture featuring a shared decision module followed by several networkbranches, one for each action dimension. This approach achieves a linearincrease of the number of network outputs with the number of degrees of freedomby allowing a level of independence for each individual action dimension. Toillustrate the approach, we present a novel agent, called Branching DuelingQ-Network (BDQ), as a branching variant of the Dueling Double Deep Q-Network(Dueling DDQN). We evaluate the performance of our agent on a set ofchallenging continuous control tasks. The empirical results show that theproposed agent scales gracefully to environments with increasing actiondimensionality and indicate the significance of the shared decision module incoordination of the distributed action branches. Furthermore, we show that theproposed agent performs competitively against a state-of-the-art continuouscontrol algorithm, Deep Deterministic Policy Gradient (DDPG).

  • CONFERENCE PAPER
    Wang K, Shah A, Kormushev P, 2018,

    SLIDER: A Bipedal Robot with Knee-less Legs and Vertical Hip Sliding Motion

  • CONFERENCE PAPER
    Wang K, Shah A, Kormushev P, 2018,

    SLIDER: A Novel Bipedal Walking Robot without Knees

  • JOURNAL ARTICLE
    Arulkumaran K, Deisenroth MP, Brundage M, Bharath AAet al., 2017,

    A brief survey of deep reinforcement learning

    , IEEE Signal Processing Magazine, Vol: 34, Pages: 26-38, ISSN: 1053-5888

    Deep reinforcement learning (DRL) is poised to revolutionize the field of artificial intelligence (AI) and represents a step toward building autonomous systems with a higherlevel understanding of the visual world. Currently, deep learning is enabling reinforcement learning (RL) to scale to problems that were previously intractable, such as learning to play video games directly from pixels. DRL algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of RL, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep RL, including the deep Q-network (DQN), trust region policy optimization (TRPO), and asynchronous advantage actor critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via RL. To conclude, we describe several current areas of research within the field.

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