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    Baroni P, Rago A, Toni F, 2018,

    How Many Properties Do We Need for Gradual Argumentation?

    , Publisher: AAAI Press
    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
    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

    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.

    Bao Z, Čyras K, Toni F, 2017,

    ABAplus: Attack Reversal in Abstract and Structured Argumentation with Preferences

    , Pages: 420-437, ISSN: 0302-9743

    © 2017, Springer International Publishing AG. We present ABAplus, a system that implements reasoning with the argumentation formalism ABA + . ABA + is a structured argumentation formalism that extends Assumption-Based Argumentation (ABA) with preferences and accounts for preferences via attack reversal. ABA + also admits as instance Preference-based Argumentation which accounts for preferences by reversing attacks in abstract argumentation (AA). ABAplus readily implements attack reversal in both AA and ABA-style structured argumentation. ABAplus affords computation, visualisation and comparison of extensions under five argumentation semantics. It is available both as a stand-alone system and as a web application.

    Baroni P, Comini G, Rago A, Toni Fet al., 2017,

    Abstract Games of Argumentation Strategy and Game-Theoretical Argument Strength

    , PRIMA, Publisher: Springer, Pages: 403-419, ISSN: 0302-9743

    We define a generic notion of abstract games of argumentation strategy for (attack-only and bipolar) argumentation frameworks, which are zero-sum games whereby two players put forward sets of arguments and get a reward for their combined choices. The value of these games, in the classical game-theoretic sense, can be used to define measures of (quantitative) game-theoretic strength of arguments, which are different depending on whether either or both players have an “agenda” (i.e. an argument they want to be accepted). We show that this general scheme captures as a special instance a previous proposal in the literature (single agenda, attack-only frameworks), and seamlessly supports the definition of a spectrum of novel measures of game-theoretic strength where both players have an agenda and/or bipolar frameworks are considered. We then discuss the applicability of these instances of game-theoretic strength in different contexts and analyse their basic properties.

    Bellotti A, 2017,

    Reliable region predictions for automated valuation models

    Chamberlain BP, Cardoso A, Liu CHB, Pagliari R, Deisenroth MPet al., 2017,

    Customer Lifetime Value Prediction Using Embeddings

    , International Conference on Knowledge Discovery and Data Mining, Publisher: ACM, Pages: 1753-1762

    We describe the Customer LifeTime Value (CLTV) prediction system deployed, a global online fashion retailer. CLTV prediction is an importantproblem in e-commerce where an accurate estimate of future value allowsretailers to effectively allocate marketing spend, identify and nurture highvalue customers and mitigate exposure to losses. The system at ASOS providesdaily estimates of the future value of every customer and is one of thecornerstones of the personalised shopping experience. The state of the art inthis domain uses large numbers of handcrafted features and ensemble regressorsto forecast value, predict churn and evaluate customer loyalty. Recently,domains including language, vision and speech have shown dramatic advances byreplacing handcrafted features with features that are learned automaticallyfrom data. We detail the system deployed at ASOS and show that learning featurerepresentations is a promising extension to the state of the art in CLTVmodelling. We propose a novel way to generate embeddings of customers, whichaddresses the issue of the ever changing product catalogue and obtain asignificant improvement over an exhaustive set of handcrafted features.

    Cocarascu O, Toni F, 2017,

    Identifying attack and support argumentative relations using deep learning.

    , Publisher: Association for Computational Linguistics, Pages: 1374-1379
    Dawes TJW, de Marvao A, Shi W, Fletcher T, Watson GMJ, Wharton J, Rhodes CJ, Howard LSGE, Gibbs JSR, Rueckert D, Cook SA, Wilkins MR, O'Regan DPet al., 2017,

    Machine Learning of Three-dimensional Right Ventricular Motion Enables Outcome Prediction in Pulmonary Hypertension: A Cardiac MR Imaging Study

    , RADIOLOGY, Vol: 283, Pages: 381-390, ISSN: 0033-8419
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2017,

    Gaussian Process Domain Experts for Modeling of Facial Affect

    , IEEE TRANSACTIONS ON IMAGE PROCESSING, Vol: 26, Pages: 4697-4711, ISSN: 1057-7149
    Kamthe S, Deisenroth MP, 2017,

    Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control.

    Kupcsik A, Deisenroth MP, Peters J, Loh AP, Vadakkepat P, Neumann Get al., 2017,

    Model-based contextual policy search for data-efficient generalization of robot skills

    , Artificial Intelligence, Vol: 247, Pages: 415-439, ISSN: 0004-3702

    © 2014 Elsevier B.V. In robotics, lower-level controllers are typically used to make the robot solve a specific task in a fixed context. For example, the lower-level controller can encode a hitting movement while the context defines the target coordinates to hit. However, in many learning problems the context may change between task executions. To adapt the policy to a new context, we utilize a hierarchical approach by learning an upper-level policy that generalizes the lower-level controllers to new contexts. A common approach to learn such upper-level policies is to use policy search. However, the majority of current contextual policy search approaches are model-free and require a high number of interactions with the robot and its environment. Model-based approaches are known to significantly reduce the amount of robot experiments, however, current model-based techniques cannot be applied straightforwardly to the problem of learning contextual upper-level policies. They rely on specific parametrizations of the policy and the reward function, which are often unrealistic in the contextual policy search formulation. In this paper, we propose a novel model-based contextual policy search algorithm that is able to generalize lower-level controllers, and is data-efficient. Our approach is based on learned probabilistic forward models and information theoretic policy search. Unlike current algorithms, our method does not require any assumption on the parametrization of the policy or the reward function. We show on complex simulated robotic tasks and in a real robot experiment that the proposed learning framework speeds up the learning process by up to two orders of magnitude in comparison to existing methods, while learning high quality policies.

    Rafiq Y, Dickens L, Russo A, Bandara AK, Yang M, Stuart A, Levine M, Calikli G, Price BA, Nuseibeh Bet al., 2017,

    Learning to Share: Engineering Adaptive Decision-Support for Online Social Networks

    , 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE), Publisher: IEEE, Pages: 280-285, ISSN: 1527-1366
    Rago A, Toni F, 2017,

    Quantitative Argumentation Debates with Votes for Opinion Polling

    , PRIMA, Publisher: Springer, Pages: 369-385, ISSN: 0302-9743

    Opinion polls are used in a variety of settings to assess the opinions of a population, but they mostly conceal the reasoning behind these opinions. Argumentation, as understood in AI, can be used to evaluate opinions in dialectical exchanges, transparently articulating the reasoning behind the opinions. We give a method integrating argumentation within opinion polling to empower voters to add new statements that render their opinions in the polls individually rational while at the same time justifying them. We then show how these poll results can be amalgamated to give a collectively rational set of voters in an argumentation framework. Our method relies upon Quantitative Argumentation Debate for Voting (QuAD-V) frameworks, which extend QuAD frameworks (a form of bipolar argumentation frameworks in which arguments have an intrinsic strength) with votes expressing individuals’ opinions on arguments.

    Schulz C, Toni F, 2017,

    Labellings for assumption-based and abstract argumentation

    Law M, Russo A, Broda K, 2016,

    Iterative Learning of Answer Set Programs from Context Dependent Examples

    , 32nd International Conference on Logic Programming (ICLP), Publisher: CAMBRIDGE UNIV PRESS, Pages: 834-848, ISSN: 1471-0684
    Ma J, Le F, Russo A, Lobo Jet al., 2016,

    Declarative Framework for Specification, Simulation and Analysis of Distributed Applications

    Turliuc CR, Dickens L, Russo A, Broda Ket al., 2016,

    Probabilistic abductive logic programming using Dirichlet priors

    Athakravi D, Alrajeh D, Broda K, Russo A, Satoh Ket al., 2015,

    Inductive Learning Using Constraint-Driven Bias

    , 24th International Conference on Inductive Logic Programming (ILP), Publisher: SPRINGER-VERLAG BERLIN, Pages: 16-32, ISSN: 0302-9743

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