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  • Conference paper
    Dragiev S, Russo A, Broda K, Law M, Turliuc Ret al., 2017,

    An abductive-inductive algorithm for probabilistic inductive logic programming

    , 26th International Conference on Inductive Logic Programming, Pages: 20-26, ISSN: 1613-0073

    The integration of abduction and induction has lead to a variety of non-monotonic ILP systems. XHAIL is one of these systems, in which abduction is used to compute hypotheses that subsume Kernel Sets. On the other hand, Peircebayes is a recently proposed logic-based probabilistic programming approach that combines abduction with parameter learning to learn distributions of most likely explanations. In this paper, we propose an approach for integrating probabilistic inference with ILP. The basic idea is to redefine the inductive task of XHAIL as a statistical abduction, and to use Peircebayes to learn probability distribution of hypotheses. An initial evaluation of the proposed algorithm is given using synthetic data.

  • Conference paper
    Chamberlain BP, Humby C, Deisenroth MP, 2017,

    Probabilistic Inference of Twitter Users' Age Based on What They Follow.

    , Publisher: Springer, Pages: 191-203
  • Journal article
    Broda KB, Law M, Russo A, 2016,

    Iterative Learning of Answer Set Programs with Context Dependent Examples

    , Theory and Practice of Logic Programming, Vol: 16, Pages: 834-848, ISSN: 1475-3081

    In recent years, several frameworks and systems have been proposed that extend InductiveLogic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examplesmust all be explained by a hypothesis together with a given background knowledge. In existingsystems, the background knowledge is the same for all examples; however, examples may becontext-dependent. This means that some examples should be explained in the context ofsome information, whereas others should be explained in different contexts. In this paper, wecapture this notion and present a context-dependent extension of the Learning from OrderedAnswer Sets framework. In this extension, contexts can be used to further structure thebackground knowledge. We then propose a new iterative algorithm, ILASP2i, which exploitsthis feature to scale up the existing ILASP2 system to learning tasks with large numbersof examples. We demonstrate the gain in scalability by applying both algorithms to variouslearning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2isystem can be two orders of magnitude faster and use two orders of magnitude less memory,whilst preserving the same average accuracy

  • Conference paper
    Zambelli M, Fischer T, Petit M, Chang HJ, Cully A, Demiris Yet al., 2016,

    Towards Anchoring Self-Learned Representations to Those of Other Agents

    , Workshop on Bio-inspired Social Robot Learning in Home Scenarios IEEE/RSJ International Conference on Intelligent Robots and Systems, Publisher: Institute of Electrical and Electronics Engineers (IEEE)

    In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in particular 1) self-learns a mapping between motor commands and sensory consequences, 2) rapidly acquires primitives and complex actions by verbal descriptions and instructions from a human partner, 3) discoverscorrespondences between the robots body and other articulated objects and agents, and 4) employs these correspondences to transfer the knowledge acquired from the robots point of view to the viewpoint of the other agent. We show that our approach requires very little a-priori knowledge to achieve imitation learning, to find correspondent body parts of humans, and allows taking the perspective of another agent. This represents a step towards the emergence of a mirror neuron like system based on self-learned representations.

  • Journal article
    Palomeras N, Carrera A, Hurtós 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
  • Journal article
    Jamisola RS, Kormushev P, Roberts RG, Caldwell DGet al., 2016,

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

    , Journal of Intelligent & Robotic Systems, Pages: 1-14, ISSN: 1573-0409
  • Journal article
    Reddy M, Pesl P, Xenou M, Toumazou C, Johnston D, Georgiou P, Herrero P, Oliver Net al., 2016,

    Clinical Safety and Feasibility of the Advanced Bolus Calculator for Type 1 Diabetes Based on Case-Based Reasoning: A 6-Week Nonrandomized Single-Arm Pilot Study.

    , Diabetes Technol Ther, Vol: 18, Pages: 487-493

    BACKGROUND: The Advanced Bolus Calculator for Diabetes (ABC4D) is an insulin bolus dose decision support system based on case-based reasoning (CBR). The system is implemented in a smartphone application to provide personalized and adaptive insulin bolus advice for people with type 1 diabetes. We aimed to assess proof of concept, safety, and feasibility of ABC4D in a free-living environment over 6 weeks. METHODS: Prospective nonrandomized single-arm pilot study. Participants used the ABC4D smartphone application for 6 weeks in their home environment, attending the clinical research facility weekly for data upload, revision, and adaptation of the CBR case base. The primary outcome was postprandial hypoglycemia. RESULTS: Ten adults with type 1 diabetes, on multiple daily injections of insulin, mean (standard deviation) age 47 (17), diabetes duration 25 (16), and HbA1c 68 (16) mmol/mol (8.4 (1.5) %) participated. A total of 182 and 150 meals, in week 1 and week 6, respectively, were included in the analysis of postprandial outcomes. The median (interquartile range) number of postprandial hypoglycemia episodes within 6-h after the meal was 4.5 (2.0-8.2) in week 1 versus 2.0 (0.5-6.5) in week 6 (P = 0.1). No episodes of severe hypoglycemia occurred during the study. CONCLUSION: The ABC4D is safe for use as a decision support tool for insulin bolus dosing in self-management of type 1 diabetes. A trend suggesting a reduction in postprandial hypoglycemia was observed in the final week compared with week 1.

  • Conference paper
    Tarapore D, Clune J, Cully AHR, Mouret J-Bet al., 2016,

    How do different encodings influence the performance of the MAP-Elites algorithm?

    , Proceedings of the Genetic and Evolutionary Computation Conference 2016, Publisher: ACM, Pages: 173-180

    The recently introduced Intelligent Trial and Error algorithm (IT&E) both improves the ability to automatically generate controllers that transfer to real robots, and enables robots to creatively adapt to damage in less than 2 minutes. A key component of IT&E is a new evolutionary algorithm called MAP-Elites, which creates a behavior-performance map that is provided as a set of "creative" ideas to an online learning algorithm. To date, all experiments with MAP-Elites have been performed with a directly encoded list of parameters: it is therefore unknown how MAP-Elites would behave with more advanced encodings, like HyperNeat and SUPG. In addition, because we ultimately want robots that respond to their environments via sensors, we investigate the ability of MAP-Elites to evolve closed-loop controllers, which are more complicated, but also more powerful. Our results show that the encoding critically impacts the quality of the results of MAP-Elites, and that the differences are likely linked to the locality of the encoding (the likelihood of generating a similar behavior after a single mutation). Overall, these results improve our understanding of both the dynamics of the MAP-Elites algorithm and how to best harness MAP-Elites to evolve effective and adaptable robotic controllers.

  • Journal article
    Turliuc R, Dickens L, Russo AM, Broda Ket al., 2016,

    Probabilistic abductive logic programming using Dirichlet priors

    , International Journal of Approximate Reasoning, Vol: 78, Pages: 223-240, ISSN: 1873-4731

    Probabilistic programming is an area of research that aims to develop general inference algorithms for probabilistic models expressed as probabilistic programs whose execution corresponds to inferring the parameters of those models. In this paper, we introduce a probabilistic programming language (PPL) based on abductive logic programming for performing inference in probabilistic models involving categorical distributions with Dirichlet priors. We encode these models as abductive logic programs enriched with probabilistic definitions and queries, and show how to execute and compile them to boolean formulas. Using the latter, we perform generalized inference using one of two proposed Markov Chain Monte Carlo (MCMC) sampling algorithms: an adaptation of uncollapsed Gibbs sampling from related work and a novel collapsed Gibbs sampling (CGS). We show that CGS converges faster than the uncollapsed version on a latent Dirichlet allocation (LDA) task using synthetic data. On similar data, we compare our PPL with LDA-specific algorithms and other PPLs. We find that all methods, except one, perform similarly and that the more expressive the PPL, the slower it is. We illustrate applications of our PPL on real data in two variants of LDA models (Seed and Cluster LDA), and in the repeated insertion model (RIM). In the latter, our PPL yields similar conclusions to inference with EM for Mallows models.

  • Book chapter
    Georgiou P, Pesl P, Oliver N, Reddy M, Herrero Vinas Pet al., 2016,

    An Advanced Insulin Bolus Calculator for Type 1 Diabetes

    , Wireless Medical Systems and Algorithms Design and Applications, Publisher: CRC Press, ISBN: 9781498700788

    Design and Applications Pietro Salvo, Miguel Hernandez-Silveira ... VLSI: Circuits for Emerging Applications Tomasz Wojcicki Wireless Medical Systems and Algorithms: Design and Applications ... Wireless Technologies: Circuits, Systems, and Devices Krzysztof Iniewski Wireless Transceiver Circuits: System Perspectives ...

  • Journal article
    Cully A, Mouret J-B, 2016,

    Evolving a behavioral repertoire for a walking robot

    , Evolutionary Computation, Vol: 24, Pages: 59-88, ISSN: 1063-6560

    Numerous algorithms have been proposed to allow legged robots to learn to walk.However, most of these algorithms are devised to learn walking in a straight line,which is not sufficient to accomplish any real-world mission. Here we introduce theTransferability-based Behavioral Repertoire Evolution algorithm (TBR-Evolution), anovel evolutionary algorithm that simultaneously discovers several hundreds of simplewalking controllers, one for each possible direction. By taking advantage of solutionsthat are usually discarded by evolutionary processes, TBR-Evolution is substantiallyfaster than independently evolving each controller. Our technique relies on two meth-ods: (1) novelty search with local competition, which searches for both high-performingand diverse solutions, and (2) the transferability approach, which combines simulationsand real tests to evolve controllers for a physical robot. We evaluate this new techniqueon a hexapod robot. Results show that with only a few dozen short experiments per-formed on the robot, the algorithm learns a repertoire of controllers that allows therobot to reach every point in its reachable space. Overall, TBR-Evolution introduceda new kind of learning algorithm that simultaneously optimizes all the achievablebehaviors of a robot.

  • Journal article
    Reddy M, Pesl P, Xenou M, Toumazou C, Johnston D, Georgiou P, Herrero P, Oliver Net al., 2016,


    , DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 18, Pages: A34-A35, ISSN: 1520-9156
  • Book chapter
    Kormushev P, Ahmadzadeh SR, 2016,

    Robot Learning for Persistent Autonomy

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 3-28, ISBN: 978-3-319-26327-4
  • Book chapter
    Ahmadzadeh SR, Kormushev P, 2016,

    Visuospatial Skill Learning

    , Handling Uncertainty and Networked Structure in Robot Control, Editors: Busoniu, Tamás, Publisher: Springer International Publishing, Pages: 75-99, ISBN: 978-3-319-26327-4
  • Conference paper
    Maurelli F, Lane D, Kormushev P, Caldwell D, Carreras M, Salvi J, Fox M, Long D, Kyriakopoulos K, Karras Get al., 2016,

    The PANDORA project: a success story in AUV autonomy

    , OCEANS Conference 2016, Publisher: IEEE, ISSN: 0197-7385

    This paper presents some of the results of the EU-funded project PANDORA - Persistent Autonomy Through Learning Adaptation Observation and Re-planning. The project was three and a half years long and involved several organisations across Europe. The application domain is underwater inspection and intervention, a topic particularly interesting for the oil and gas sector, whose representatives constituted the Industrial Advisory Board. Field trials were performed at The Underwater Centre, in Loch Linnhe, Scotland, and in harbour conditions close to Girona, Spain.

  • Journal article
    Russo AM, Ma J, Lobo J, Le Fet al., 2016,

    Declarative Framework for Specification, Simulation and Analysis of Distributed Applications

    , IEEE Transactions on Knowledge and Data Engineering, Vol: 28, Pages: 1489-1502, ISSN: 1558-2191

    Researchers have recently shown that declarative database query languages, such as Datalog, could naturally be used to specify and implement network protocols and services. In this paper we present a declarative framework for the specification, execution, simulation and analysis of distributed applications. Distributed applications, including routing protocols, can be specified using a Declarative Networking language, called D2C, whose semantics captures the notion of a Distributed State Machine (DSM), i.e. a network of computational nodes that communicate with each other through the exchange of data. The D2C specification can be directly executed using the DSM computational infrastructure of our framework. The same specification can be simulated and formally verified. The simulation component integrates the DSM tool within a network simulation environment and allows developers to simulate network dynamics and collect data about the execution in order to evaluate application responses to network changes. The formal analysis component of our framework, instead, complements the empirical testing by supporting the verification of different classes of properties of distributed algorithms, including convergence of network routing protocols. To demonstrate the generality of our framework, we show how it can be used to analyse two classes of network routing protocols, a path vector and a Mobile Ad-Hoc Network (MANET) routing protocol, and execute a distributed algorithm for pattern formation in multi-robot systems.

  • Conference paper
    Pesl P, Herrero P, Reddy M, Oliver N, Toumazou C, Georgiou Pet al., 2016,

    Live Demonstration: Smartwatch Implementation of an Advanced Insulin Bolus Calculator for Diabetes

    , IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2370-2370, ISSN: 0271-4302
  • Conference paper
    Eleftheriadis S, Rudovic O, Deisenroth MP, Pantic Met al., 2016,

    Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units.

    , Pages: 154-170
  • Conference paper
    Maestre C, Cully AHR, Gonzales C, Doncieux Set al., 2015,

    Bootstrapping interactions with objects from raw sensorimotor data: a Novelty Search based approach

    , 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), Publisher: IEEE

    Determining in advance all objects that a robot will interact with in an open environment is very challenging, if not impossible. It makes difficult the development of models that will allow to perceive and recognize objects, to interact with them and to predict how these objects will react to interactions with other objects or with the robot. Developmental robotics proposes to make robots learn by themselves such models through a dedicated exploration step. It raises a chicken-and-egg problem: the robot needs to learn about objects to discover how to interact with them and, to this end, it needs to interact with them. In this work, we propose Novelty-driven Evolutionary Babbling (NovEB), an approach enabling to bootstrap this process and to acquire knowledge about objects in the surrounding environment without requiring to include a priori knowledge about the environment, including objects, or about the means to interact with them. Our approach consists in using an evolutionary algorithm driven by a novelty criterion defined in the raw sensorimotor flow: behaviours, described by a trajectory of the robot end effector, are generated with the goal to maximize the novelty of raw perceptions. The approach is tested on a simulated PR2 robot and is compared to a random motor babbling.

  • Conference paper
    Athakravi D, Satoh K, Law M, Broda K, Russo AMet al., 2015,

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

    , International Conference on Logic Programming and Non Monotonic Reasoning (LPNMR 2015), Publisher: Springer, Pages: 83-96, ISSN: 0302-9743

    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 (i) cover all possible cases (including past cases) within a given set of relevant factors, and (ii) can make deterministic predictions on final verdicts for unseen cases. The extracted declarative model of judgements can then be used to make automated inference of future judgements, and generate explanations of legal decisions.

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