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Journal articleReddy M, Pesl P, Xenou M, et 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: 487-493BACKGROUND: 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.
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Journal articleJamisola RS, Kormushev P, Roberts RG, et 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 -
Conference paperTarapore D, Clune J, Cully AHR, et 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: 173-180The 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.
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Journal articleTurliuc R, Dickens L, Russo AM, et al., 2016,
Probabilistic abductive logic programming using Dirichlet priors
, International Journal of Approximate Reasoning, Vol: 78, Pages: 223-240, ISSN: 1873-4731Probabilistic 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.
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Journal articleRusso AM, Ma J, Lobo J, et 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: 1558-2191Researchers 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.
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Book chapterGeorgiou P, Pesl P, Oliver N, et al., 2016,
An Advanced Insulin Bolus Calculator for Type 1 Diabetes
, Wireless Medical Systems and Algorithms Design and Applications, Publisher: CRC Press, ISBN: 9781498700788Design 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 ...
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Journal articleCully A, Mouret J-B, 2016,
Evolving a behavioral repertoire for a walking robot
, Evolutionary Computation, Vol: 24, Pages: 59-88, ISSN: 1063-6560Numerous 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.
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Journal articleReddy M, Pesl P, Xenou M, et 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 chapterAhmadzadeh 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 -
Book chapterKormushev 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 -
Conference paperMaurelli F, Lane D, Kormushev P, et al., 2016,
The PANDORA project: a success story in AUV autonomy
, OCEANS Conference 2016, Publisher: IEEE, ISSN: 0197-7385This 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.
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Conference paperEleftheriadis S, Rudovic O, Deisenroth MP, et al., 2016,
Variational Gaussian Process Auto-Encoder for Ordinal Prediction of Facial Action Units.
, Pages: 154-170 -
Conference paperPesl P, Herrero P, Reddy M, et 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 paperMaestre C, Cully AHR, Gonzales C, et 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: IEEEDetermining 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.
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Conference paperAthakravi D, Satoh K, Law M, et 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: 0302-9743In 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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Journal articleLaw M, Russo A, Broda K, 2015,
Learning weak constraints in answer set programming
, Theory and Practice of Logic Programming, Vol: 15, Pages: 511-525, ISSN: 1475-3081This paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set Programming (ASP). The framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.
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Conference paperKryczka P, Kormushev P, Tsagarakis N, et al., 2015,
Online Regeneration of Bipedal Walking Gait Optimizing Footstep Placement and Timing
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Conference paperKormushev P, Demiris Y, Caldwell DG, 2015,
Kinematic-free Position Control of a 2-DOF Planar Robot Arm
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Journal articleCarrera A, Palomeras N, Hurtós N, et al., 2015,
Cognitive System for Autonomous Underwater Intervention
, Pattern Recognition Letters, ISSN: 0167-8655 -
Journal articleCully A, Clune J, Tarapore D, et al., 2015,
Robots that can adapt like animals
, Nature, Vol: 521, Pages: 503-507, ISSN: 0028-0836As robots leave the controlled environments of factories to autonomouslyfunction in more complex, natural environments, they will have to respond tothe inevitable fact that they will become damaged. However, while animals canquickly adapt to a wide variety of injuries, current robots cannot "thinkoutside the box" to find a compensatory behavior when damaged: they are limitedto their pre-specified self-sensing abilities, can diagnose only anticipatedfailure modes, and require a pre-programmed contingency plan for every type ofpotential damage, an impracticality for complex robots. Here we introduce anintelligent trial and error algorithm that allows robots to adapt to damage inless than two minutes, without requiring self-diagnosis or pre-specifiedcontingency plans. Before deployment, a robot exploits a novel algorithm tocreate a detailed map of the space of high-performing behaviors: This maprepresents the robot's intuitions about what behaviors it can perform and theirvalue. If the robot is damaged, it uses these intuitions to guide atrial-and-error learning algorithm that conducts intelligent experiments torapidly discover a compensatory behavior that works in spite of the damage.Experiments reveal successful adaptations for a legged robot injured in fivedifferent ways, including damaged, broken, and missing legs, and for a roboticarm with joints broken in 14 different ways. This new technique will enablemore robust, effective, autonomous robots, and suggests principles that animalsmay use to adapt to injury.
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