Search or filter publications

Filter by type:

Filter by publication type

Filter by year:

to

Results

  • Showing results for:
  • Reset all filters

Search results

  • Conference paper
    Čyras K, Karamlou A, Lee M, Letsios D, Misener R, Toni Fet al., 2020,

    AI-assisted schedule explainer for nurse rostering

    , AAMAS, Pages: 2101-2103, ISSN: 1548-8403

    We present an argumentation-supported explanation generating system, called Schedule Explainer, that assists with makespan scheduling. Our stand-alone generic tool explains to a lay user why a resource allocation schedule is good or not, and offers actions to improve the schedule given the user's constraints. Schedule Explainer provides actionable textual explanations via an interactive graphical interface. We illustrate our system with a proof-of-concept application tool in a nurse rostering scenario whereby a shift-lead nurse aims to account for unexpected events by rescheduling some patient procedures to nurses and is aided by the system to do so.
  • Conference paper
    Albini E, Rago A, Baroni P, Toni Fet al., 2020,

    Relation-Based Counterfactual Explanations for Bayesian Network Classifiers

    , The 29th International Joint Conference on Artificial Intelligence (IJCAI 2020)
  • Journal article
    Tsai Y-Y, Xiao B, Johns E, Yang G-Zet al., 2020,

    Constrained-space optimization and reinforcement learning for complex tasks

    , IEEE Robotics and Automation Letters, Vol: 5, Pages: 683-690, ISSN: 2377-3766

    Learning from demonstration is increasingly used for transferring operator manipulation skills to robots. In practice, it is important to cater for limited data and imperfect human demonstrations, as well as underlying safety constraints. This article presents a constrained-space optimization and reinforcement learning scheme for managing complex tasks. Through interactions within the constrained space, the reinforcement learning agent is trained to optimize the manipulation skills according to a defined reward function. After learning, the optimal policy is derived from the well-trained reinforcement learning agent, which is then implemented to guide the robot to conduct tasks that are similar to the experts' demonstrations. The effectiveness of the proposed method is verified with a robotic suturing task, demonstrating that the learned policy outperformed the experts' demonstrations in terms of the smoothness of the joint motion and end-effector trajectories, as well as the overall task completion time.
  • Journal article
    Dur TH, Arcucci R, Mottet L, Molina Solana M, Pain C, Guo Y-Ket al., 2020,

    Weak Constraint Gaussian Processes for optimal sensor placement

    , JOURNAL OF COMPUTATIONAL SCIENCE, Vol: 42, ISSN: 1877-7503
  • Journal article
    Wu P, Sun J, Chang X, Zhang W, Arcucci R, Guo Y, Pain CCet al., 2020,

    Data-driven reduced order model with temporal convolutional neural network

    , COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 360, ISSN: 0045-7825
  • Journal article
    Calvo RA, Peters D, Cave S, 2020,

    Advancing impact assessment for intelligent systems

    , Nature Machine Intelligence, Vol: 2, Pages: 89-91, ISSN: 2522-5839
  • Conference paper
    Pardo F, Levdik V, Kormushev P, 2020,

    Scaling all-goals updates in reinforcement learning using convolutional neural networks

    , 34th AAAI Conference on Artificial Intelligence (AAAI 2020), Publisher: Association for the Advancement of Artificial Intelligence, Pages: 5355-5362, ISSN: 2374-3468

    Being able to reach any desired location in the environmentcan be a valuable asset for an agent. Learning a policy to nav-igate between all pairs of states individually is often not fea-sible. Anall-goals updatingalgorithm uses each transitionto learn Q-values towards all goals simultaneously and off-policy. However the expensive numerous updates in parallellimited the approach to small tabular cases so far. To tacklethis problem we propose to use convolutional network archi-tectures to generate Q-values and updates for a large numberof goals at once. We demonstrate the accuracy and generaliza-tion qualities of the proposed method on randomly generatedmazes and Sokoban puzzles. In the case of on-screen goalcoordinates the resulting mapping from frames todistance-mapsdirectly informs the agent about which places are reach-able and in how many steps. As an example of applicationwe show that replacing the random actions inε-greedy ex-ploration by several actions towards feasible goals generatesbetter exploratory trajectories on Montezuma’s Revenge andSuper Mario All-Stars games.
  • Book
    Deisenroth MP, Faisal AA, Ong CS, 2020,

    Mathematics for Machine Learning

    , Publisher: Cambridge University Press, ISBN: 9781108455145
  • Conference paper
    Saputra RP, Rakicevic N, Kormushev P, 2020,

    Sim-to-real learning for casualty detection from ground projected point cloud data

    , 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), Publisher: IEEE

    This paper addresses the problem of human body detection-particularly a human body lying on the ground (a.k.a. casualty)-using point cloud data. This ability to detect a casualty is one of the most important features of mobile rescue robots, in order for them to be able to operate autonomously. We propose a deep-learning-based casualty detection method using a deep convolutional neural network (CNN). This network is trained to be able to detect a casualty using a point-cloud data input. In the method we propose, the point cloud input is pre-processed to generate a depth image-like ground-projected heightmap. This heightmap is generated based on the projected distance of each point onto the detected ground plane within the point cloud data. The generated heightmap-in image form-is then used as an input for the CNN to detect a human body lying on the ground. To train the neural network, we propose a novel sim-to-real approach, in which the network model is trained using synthetic data obtained in simulation and then tested on real sensor data. To make the model transferable to real data implementations, during the training we adopt specific data augmentation strategies with the synthetic training data. The experimental results show that data augmentation introduced during the training process is essential for improving the performance of the trained model on real data. More specifically, the results demonstrate that the data augmentations on raw point-cloud data have contributed to a considerable improvement of the trained model performance.
  • Journal article
    Stimberg M, Goodman D, Nowotny T, 2020,

    Brian2GeNN: accelerating spiking neural network simulations with graphics hardware

    , Scientific Reports, Vol: 10, Pages: 1-12, ISSN: 2045-2322

    “Brian” is a popular Python-based simulator for spiking neural networks, commonly used in computational neuroscience. GeNNis a C++-based meta-compiler for accelerating spiking neural network simulations using consumer or high performance gradegraphics processing units (GPUs). Here we introduce a new software package, Brian2GeNN, that connects the two systems sothat users can make use of GeNN GPU acceleration when developing their models in Brian, without requiring any technicalknowledge about GPUs, C++ or GeNN. The new Brian2GeNN software uses a pipeline of code generation to translate Brianscripts into C++ code that can be used as input to GeNN, and subsequently can be run on suitable NVIDIA GPU accelerators.From the user’s perspective, the entire pipeline is invoked by adding two simple lines to their Brian scripts. We have shown thatusing Brian2GeNN, two non-trivial models from the literature can run tens to hundreds of times faster than on CPU.
  • Conference paper
    Johns E, Liu S, Davison A, 2020,

    End-to-end multi-task learning with attention

    , The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, Publisher: IEEE

    We propose a novel multi-task learning architecture, which allows learning of task-specific feature-level attention. Our design, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with a soft-attention module for each task. These modules allow for learning of task-specific features from the global features, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be trained end-to-end and can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. We evaluate our approach on a variety of datasets, across both image-to-image predictions and image classification tasks. We show that our architecture is state-of-the-art in multi-task learning compared to existing methods, and is also less sensitive to various weighting schemes in the multi-task loss function. Code is available at https://github.com/lorenmt/mtan.
  • Book chapter
    Cocarascu O, Toni F, 2020,

    Deploying Machine Learning Classifiers for Argumentative Relations “in the Wild”

    , Argumentation Library, Pages: 269-285

    Argument Mining (AM) aims at automatically identifying arguments and components of arguments in text, as well as at determining the relations between these arguments, on various annotated corpora using machine learning techniques (Lippi & Torroni, 2016).
  • Conference paper
    Nadler P, Arcucci R, Guo Y, 2020,

    An Econophysical Analysis of the Blockchain Ecosystem

    , Pages: 27-42, ISSN: 2198-7246

    We propose a novel modelling approach for the cryptocurrency ecosystem. We model on-chain and off-chain interactions as econophysical systems and employ methods from physical sciences to conduct interpretation of latent parameters describing the cryptocurrency ecosystem as well as to generate predictions. We work with an extracted dataset from the Ethereum blockchain which we combine with off-chain data from exchanges. This allows us to study a large part of the transaction flows related to the cryptocurrency ecosystem. From this aggregate system view we deduct that movements on the blockchain and price and trading action on exchanges are interrelated. The relationship is one directional: On-chain token flows towards exchanges have little effect on prices and trading volume, but changes in price and volume affect the flow of tokens towards the exchange.
  • Journal article
    Zambelli M, Cully A, Demiris Y, 2020,

    Multimodal representation models for prediction and control from partial information

    , Robotics and Autonomous Systems, Vol: 123, ISSN: 0921-8890

    Similar to humans, robots benefit from interacting with their environment through a number of different sensor modalities, such as vision, touch, sound. However, learning from different sensor modalities is difficult, because the learning model must be able to handle diverse types of signals, and learn a coherent representation even when parts of the sensor inputs are missing. In this paper, a multimodal variational autoencoder is proposed to enable an iCub humanoid robot to learn representations of its sensorimotor capabilities from different sensor modalities. The proposed model is able to (1) reconstruct missing sensory modalities, (2) predict the sensorimotor state of self and the visual trajectories of other agents actions, and (3) control the agent to imitate an observed visual trajectory. Also, the proposed multimodal variational autoencoder can capture the kinematic redundancy of the robot motion through the learned probability distribution. Training multimodal models is not trivial due to the combinatorial complexity given by the possibility of missing modalities. We propose a strategy to train multimodal models, which successfully achieves improved performance of different reconstruction models. Finally, extensive experiments have been carried out using an iCub humanoid robot, showing high performance in multiple reconstruction, prediction and imitation tasks.
  • Conference paper
    Jha R, Belardinelli F, Toni F, 2020,

    Formal verification of debates in argumentation theory.

    , Publisher: ACM, Pages: 940-947
  • Conference paper
    Arcucci R, Casas CQ, Xiao D, Mottet L, Fang F, Wu P, Pain C, Guo Y-Ket al., 2020,

    A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA)

    , Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 189-198, ISSN: 0927-5452
  • Conference paper
    Nadler P, Arcucci R, Guo Y-K, 2020,

    A Scalable Approach to Econometric Inference

    , Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 59-68, ISSN: 0927-5452
  • Conference paper
    Arcucci R, Mottet L, Casas CAQ, Guitton F, Pain C, Guo Y-Ket al., 2020,

    Adaptive Domain Decomposition for Effective Data Assimilation

    , 25th International Conference on Parallel and Distributed Computing (Euro-Par), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 583-595, ISSN: 0302-9743
  • Book chapter
    Arcucci R, Moutiq L, Guo Y-K, 2020,

    Neural Assimilation

    , Editors: Krzhizhanovskaya, Zavodszky, Lees, Dongarra, Sloot, Brissos, Teixeira, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 155-168, ISBN: 978-3-030-50432-8
  • Conference paper
    Cocarascu O, Cabrio E, Villata S, Toni Fet al., 2020,

    Dataset Independent Baselines for Relation Prediction in Argument Mining.

    , Publisher: IOS Press, Pages: 45-52

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.

Request URL: http://www.imperial.ac.uk:80/respub/WEB-INF/jsp/search-t4-html.jsp Request URI: /respub/WEB-INF/jsp/search-t4-html.jsp Query String: id=989&limit=20&page=11&respub-action=search.html Current Millis: 1730824660808 Current Time: Tue Nov 05 16:37:40 GMT 2024

Contact us

Artificial Intelligence Network
South Kensington Campus
Imperial College London
SW7 2AZ

To reach the elected speaker of the network, Dr Rossella Arcucci, please contact:

ai-speaker@imperial.ac.uk

To reach the network manager, Diana O'Malley - including to join the network - please contact:

ai-net-manager@imperial.ac.uk

SUBSCRIBE: Join our AI Talks mailing list and keep up to date with the latest news
For enquiries about student positions, please contact members from our People page.