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    Baroni P, Rago A, Toni F, 2018,

    How Many Properties Do We Need for Gradual Argumentation?

    , Publisher: AAAI Press
    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).

    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.

    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
    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.

    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.

    Jamisola RS, Kormushev PS, Roberts RG, Caldwell DGet al., 2016,

    Task-Space Modular Dynamics for Dual-Arms Expressed through a Relative Jacobian

    , JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, Vol: 83, Pages: 205-218, ISSN: 0921-0296
    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

    Palomeras N, Carrera A, Hurtos N, Karras GC, Bechlioulis CP, Cashmore M, Magazzeni D, Long D, Fox M, Kyriakopoulos KJ, Kormushev P, Salvi J, Carreras Met al., 2016,

    Toward persistent autonomous intervention in a subsea panel

    , AUTONOMOUS ROBOTS, Vol: 40, Pages: 1279-1306, ISSN: 0929-5593
    Turliuc CR, Dickens L, Russo A, Broda Ket al., 2016,

    Probabilistic abductive logic programming using Dirichlet priors

    Ahmadzadeh SR, Kormushev P, 2015,

    Visuospatial skill learning

    , Studies in Systems, Decision and Control, Pages: 75-99

    © Springer International Publishing Switzerland 2015. This chapter introduces Visuospatial Skill Learning (VSL), which is a novel interactive robot learning approach. VSL is based on visual perception that allows a robot to acquire new skills by observing a single demonstration while interacting with a tutor. The focus of VSL is placed on achieving a desired goal configuration of objects relative to another. VSL captures the object’s context for each demonstrated action. This context is the basis of the visuospatial representation and encodes implicitly the relative positioning of the object with respect to multiple other objects simultaneously. VSL is capable of learning and generalizing multi-operation skills from a single demonstration, while requiring minimum a priori knowledge about the environment. Different capabilities of VSL such as learning and generalization of object reconfiguration, classification, and turn-taking interaction are illustrated through both simulation and real-world experiments.

    Ahmadzadeh SR, Paikan A, Mastrogiovanni F, Natale L, Kormushev P, Caldwell DGet al., 2015,

    Learning Symbolic Representations of Actions from Human Demonstrations

    , IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE COMPUTER SOC, Pages: 3801-3808, ISSN: 1050-4729
    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
    Athakravi D, Satoh K, Law M, Broda K, Russo Aet al., 2015,

    Automated inference of rules with exception from past legal cases using ASP

    , Pages: 83-96, ISSN: 0302-9743

    © Springer International Publishing Switzerland 2015. In legal reasoning, different assumptions are often considered when reaching a final verdict and judgement outcomes strictly depend on these assumptions. In this paper, we propose an approach for generating a declarative model of judgements from past legal cases, that expresses a legal reasoning structure in terms of principle rules and exceptions. Using a logic-based reasoning technique, we are able to identify from given past cases different underlying defaults (legal assumptions) and compute judgements that cover all possible cases (including past cases) within a given set of relevant factors. The extracted declarative model of judgements can then be used to make deterministic automated inference on future judgements, as well as generate explanations of legal decisions.

    Bimbo J, Kormushev P, Althoefer K, Liu Het al., 2015,

    Global estimation of an object's pose using tactile sensing

    , ADVANCED ROBOTICS, Vol: 29, Pages: 363-374, ISSN: 0169-1864
    Carrera A, Palomeras N, Hurtos N, Kormushev P, Carreras Met al., 2015,

    Cognitive system for autonomous underwater intervention

    , PATTERN RECOGNITION LETTERS, Vol: 67, Pages: 91-99, ISSN: 0167-8655
    Carrera A, Palomeras N, Hurtos N, Kormushev P, Carreras Met al., 2015,

    Learning multiple strategies to perform a valve turning with underwater currents using an I-AUV

    , Oceans 2015 Genova, Publisher: IEEE
    Deisenroth MP, Fox D, Rasmussen CE, 2015,

    Gaussian Processes for Data-Efficient Learning in Robotics and Control.

    , IEEE Trans Pattern Anal Mach Intell, Vol: 37, Pages: 408-423

    Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required. However, autonomous reinforcement learning (RL) approaches typically require many interactions with the system to learn controllers, which is a practical limitation in real systems, such as robots, where many interactions can be impractical and time consuming. To address this problem, current learning approaches typically require task-specific knowledge in form of expert demonstrations, realistic simulators, pre-shaped policies, or specific knowledge about the underlying dynamics. In this paper, we follow a different approach and speed up learning by extracting more information from data. In particular, we learn a probabilistic, non-parametric Gaussian process transition model of the system. By explicitly incorporating model uncertainty into long-term planning and controller learning our approach reduces the effects of model errors, a key problem in model-based learning. Compared to state-of-the art RL our model-based policy search method achieves an unprecedented speed of learning. We demonstrate its applicability to autonomous learning in real robot and control tasks.

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