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  • Conference paper
    Herrero Vinas P, Pesl P, Reddy M, Oliver N, Georgiou Pet al., 2017,

    Atomatic adjustment of Basal insulin infusion rates in type 1 diabetes using run-to-run control and case-based reasoning

    , Artificial Intelligence in Medicine

    People with type 1 diabetes mellitus rely on a basal-bolus insulinregimen to roughly emulate how a non-diabetic person’s body delivers insulin.Adjusting such regime is a challenging process usually conducted by an expertclinical. Despite several guidelines exist for such purpose, they are usuallyimpractical and fall short in achieving optimal glycemic outcomes. Therefore,there is a need for more automated and efficient strategies to adjust such regime.This paper presents, and in silico validates, a novel technique to automaticallyadapt the basal insulin profile of a person with person with type 1 diabetes. Thepresented technique, which is based on Run-to-Run control and Case-BasedReasoning, overcomes some of the limitations of previously proposedapproaches and has been proved to be robust in front of realistic intra-dayvariability. Over a period of 5 weeks on 10 virtual adult subjects, a significantreduction on the percentage of time in hyperglycemia (<70mg/dl) (from 14.3±5.6to 1.6±1.7, p< 0.01), without a significant increase on the percentage of time inhypoglycemia (>180mg/dl) (from 10.2±5.9 to 1.6±1.7, p=0.1), was achieved.
  • Journal article
    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.
  • Journal article
    Dawes T, Simoes monteiro de marvao A, Shi W, Fletcher T, Watson G, Wharton J, Rhodes C, Howard L, Gibbs J, Rueckert D, Cook S, Wilkins M, 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: 1527-1315

    Purpose: To determine if patient survival and mechanisms of right ventricular (RV) failure in pulmonary hypertension (PH) could be predicted using supervised machine learning of three dimensional patterns of systolic cardiac motion. Materials and methods: The study was approved by a research ethics committee and participants gave written informed consent. 256 patients (143 females, mean age 63 ± 17) with newly diagnosed PH underwent cardiac MR imaging, right heart catheterization (RHC) and six minute walk testing (6MWT) with a median follow up of 4.0 years. Semi automated segmentation of short axis cine images was used to create a three dimensional model of right ventricular motion. Supervised principal components analysis identified patterns of systolic motion which were most strongly predictive of survival. Survival prediction was assessed by the difference in median survival time and the area under the curve (AUC) using time dependent receiver operator characteristic for one year survival. Results: At the end of follow up 33% (93/256) died and one underwent lung transplantation. Poor outcome was predicted by a loss of effective contraction in the septum and freewall coupled with reduced basal longitudinal motion. When added to conventional imaging, hemodynamic, functional and clinical markers, three dimensional cardiac motion improved survival prediction (area under the curve 0.73 vs 0.60, p<0.001) and provided greater differentiation by difference in median survival time between high and low risk groups (13.8 vs 10.7 years, p<0.001). Conclusion:Three dimensional motion modeling with machine learning approaches reveal the adaptations in function that occur early in right heart failure and independently predict outcomes in newly diagnosed PH patients.
  • Journal article
    Rankothge W, Le F, Russo A, Lobo Jet al., 2017,

    Optimizing resource allocation for virtualized network functions in a cloud center using genetic algorithms

    , IEEE Transactions on Network and Service Management, Vol: 14, Pages: 343-356, ISSN: 1932-4537

    With the introduction of network function virtualization technology, migrating entire enterprise data centers into the cloud has become a possibility. However, for a cloud service provider (CSP) to offer such services, several research problems still need to be addressed. In previous work, we have introduced a platform, called network function center (NFC), to study research issues related to virtualized network functions (VNFs). In an NFC, we assume VNFs to be implemented on virtual machines that can be deployed in any server in the CSP network. We have proposed a resource allocation algorithm for VNFs based on genetic algorithms (GAs). In this paper, we present a comprehensive analysis of two resource allocation algorithms based on GA for: 1) the initial placement of VNFs and 2) the scaling of VNFs to support traffic changes. We compare the performance of the proposed algorithms with a traditional integer linear programming resource allocation technique. We then combine data from previous empirical analyses to generate realistic VNF chains and traffic patterns, and evaluate the resource allocation decision making algorithms. We assume different architectures for the data center, implement different fitness functions with GA, and compare their performance when scaling over the time.
  • Journal article
    Schulz C, Toni F, 2017,

    Labellings for assumption-based and abstract argumentation

    , International Journal of Approximate Reasoning, Vol: 84, Pages: 110-149, ISSN: 1873-4731

    The semantics of Assumption-Based Argumentation (ABA) frameworks are traditionally characterised as assumption extensions, i.e. sets of accepted assumptions. Assumption labellings are an alternative way to express the semantics of flat ABA frameworks, where one of the labels in, out, or undec is assigned to each assumption. They are beneficial for applications where it is important to distinguish not only between accepted and non-accepted assumptions, but further divide the non-accepted assumptions into those which are clearly rejected and those which are neither accepted nor rejected and thus undecided. We prove one-to-one correspondences between assumption labellings and extensions for the admissible, grounded, complete, preferred, ideal, semi-stable and stable semantics. We also show how the definition of assumption labellings for flat ABA frameworks can be extended to assumption labellings for any (flat and non-flat) ABA framework, enabling reasoning with a wider range of scenarios. Since flat ABA frameworks are structured instances of Abstract Argumentation (AA) frameworks, we furthermore investigate the relation between assumption labellings for flat ABA frameworks and argument labellings for AA frameworks. Building upon prior work on complete assumption and argument labellings, we prove one-to-one correspondences between grounded, preferred, ideal, and stable assumption and argument labellings, and a one-to-many correspondence between admissible assumption and argument labellings. Inspired by the notion of admissible assumption labellings we introduce committed admissible argument labellings for AA frameworks, which correspond more closely to admissible assumption labellings of ABA frameworks than admissible argument labellings do.
  • Journal article
    Erasmus JC, Bruche S, Pizarro L, Maimari N, Poggioli T, Tomlinson C, Lees J, Zalivina I, Wheeler A, Alberts A, Russo A, Braga VMMet al., 2017,

    Corrigendum: Defining functional interactions during biogenesis of epithelial junctions

    , Nature Communications, Vol: 8, Pages: 14195-14195, ISSN: 2041-1723

    The original version of this Article (https://doi.org/10.1038/ncomms13542) contained an error in the spelling of the author Tommaso Poggioli, which was incorrectly given as Tommaso Pogglioli. This has now been corrected in both the PDF and HTML versions of the Article.
  • Journal article
    Pesl P, Herrero P, Reddy M, Oliver N, Johnston DG, Toumazou C, Georgiou Pet al., 2017,

    Case-Based Reasoning for Insulin Bolus Advice.

    , J Diabetes Sci Technol, Vol: 11, Pages: 37-42

    BACKGROUND: Insulin bolus calculators assist people with Type 1 diabetes (T1D) to calculate the amount of insulin required for meals to achieve optimal glucose levels but lack adaptability and personalization. We have proposed enhancing bolus calculators by the means of case-based reasoning (CBR), an established problem-solving methodology, by individualizing and optimizing insulin therapy for various meal situations. CBR learns from experiences of past similar meals, which are described in cases through a set of parameters (eg, time of meal, alcohol, exercise). This work discusses the selection, representation and effect of case parameters used for a CBR-based Advanced Bolus Calculator for Diabetes (ABC4D). METHODS: We analyzed the usage and effect of selected parameters during a pilot study (n = 10), where participants used ABC4D for 6 weeks. Retrospectively, we evaluated the effect of glucose rate of change before the meal on the glycemic excursion. Feedback from study participants about the choice of parameters was obtained through a nonvalidated questionnaire. RESULTS: Exercise and alcohol were the most frequently used parameters, which was congruent with the feedback from study participants, who found these parameters most useful. Furthermore, cases including either exercise or alcohol as parameter showed a trend in reduction of insulin at the end of the study. A significant difference ( P < .01) was found in glycemic outcomes for meals where glucose rate of change was rising compared to stable rate of change. CONCLUSIONS: Results from the 6-week study indicate the potential benefit of including parameters exercise, alcohol and glucose-rate of change for insulin dosing decision support.
  • 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&nbsp;...
  • 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,

    CLINICAL SAFETY AND FEASIBILITY OF THE ADVANCED BOLUS CALCULATOR FOR TYPE 1 DIABETES BASED ON CASE-BASED REASONING: A 6-WEEK NON-RANDOMISED SINGLE-ARM PILOT STUDY

    , 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

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