User modelling for robotic companions using stochastic context-free grammars

Creating models about others is a sophisticated human ability that robotic companions need to develop in order to have successful interactions. This thesis proposes user modelling frameworks to personalise the interaction between a robot and its user and devises novel scenarios where robotic companions may apply these user modelling techniques. We tackle the creation of user models in a hierarchical manner, using a streamlined version of the Hierarchical Attentive Multiple-Models for Execution and Recognition (HAMMER) architecture to detect low-level user actions and taking advantage of Stochastic Context-Free Grammars (SCFGs) to instantiate higher-level models which recognise uncertain and recursive sequences of low-level actions. We discuss a couple of distinct scenarios for robotic companions: a humanoid sidekick for power-wheelchair users and a companion of hospital patients. Next, we address the limitations of the previous scenarios by applying our user modelling techniques and designing two further scenarios that fully take advantage of the user model. These scenarios are: a wheelchair driving tutor which models the user abilities, and the musical collaborator which learns the preferences of its users. The methodology produced interesting results in all scenarios: users preferred the actual robot over a simulator as a wheelchair sidekick. Hospital patients rated positively their interactions with the companion independently of their age. Moreover, most users agreed that the music collaborator had become a better accompanist with our framework. Finally, we observed that users' driving performance improved when the robotic tutor instructed them to repeat a task. As our workforce ages and the care requirements in our society grow, robots will need to play a role in helping us lead better lives. This thesis shows that, through the use of SCFGs, adaptive user models may be generated which then can be used by robots to assist their users.Open Acces

BSML: A Binding Schema Markup Language for Data Interchange in Problem Solving Environments (PSEs)

We describe a binding schema markup language (BSML) for describing data interchange between scientific codes. Such a facility is an important constituent of scientific problem solving environments (PSEs). BSML is designed to integrate with a PSE or application composition system that views model specification and execution as a problem of managing semistructured data. The data interchange problem is addressed by three techniques for processing semistructured data: validation, binding, and conversion. We present BSML and describe its application to a PSE for wireless communications system design

A Domain-Specific Language and Editor for Parallel Particle Methods

Domain-specific languages (DSLs) are of increasing importance in scientific high-performance computing to reduce development costs, raise the level of abstraction and, thus, ease scientific programming. However, designing and implementing DSLs is not an easy task, as it requires knowledge of the application domain and experience in language engineering and compilers. Consequently, many DSLs follow a weak approach using macros or text generators, which lack many of the features that make a DSL a comfortable for programmers. Some of these features---e.g., syntax highlighting, type inference, error reporting, and code completion---are easily provided by language workbenches, which combine language engineering techniques and tools in a common ecosystem. In this paper, we present the Parallel Particle-Mesh Environment (PPME), a DSL and development environment for numerical simulations based on particle methods and hybrid particle-mesh methods. PPME uses the meta programming system (MPS), a projectional language workbench. PPME is the successor of the Parallel Particle-Mesh Language (PPML), a Fortran-based DSL that used conventional implementation strategies. We analyze and compare both languages and demonstrate how the programmer's experience can be improved using static analyses and projectional editing. Furthermore, we present an explicit domain model for particle abstractions and the first formal type system for particle methods.Comment: Submitted to ACM Transactions on Mathematical Software on Dec. 25, 201

SynJax: Structured Probability Distributions for JAX

The development of deep learning software libraries enabled significant progress in the field by allowing users to focus on modeling, while letting the library to take care of the tedious and time-consuming task of optimizing execution for modern hardware accelerators. However, this has benefited only particular types of deep learning models, such as Transformers, whose primitives map easily to the vectorized computation. The models that explicitly account for structured objects, such as trees and segmentations, did not benefit equally because they require custom algorithms that are difficult to implement in a vectorized form. SynJax directly addresses this problem by providing an efficient vectorized implementation of inference algorithms for structured distributions covering alignment, tagging, segmentation, constituency trees and spanning trees. With SynJax we can build large-scale differentiable models that explicitly model structure in the data. The code is available at https://github.com/deepmind/synjax

Machine Learning Models for Efficient and Robust Natural Language Processing

Natural language processing (NLP) has come of age. For example, semantic role labeling (SRL), which automatically annotates sentences with a labeled graph representing who did what to whom, has in the past ten years seen nearly 40% reduction in error, bringing it to useful accuracy. As a result, a myriad of practitioners now want to deploy NLP systems on billions of documents across many domains. However, state-of-the-art NLP systems are typically not optimized for cross-domain robustness nor computational efficiency. In this dissertation I develop machine learning methods to facilitate fast and robust inference across many common NLP tasks. First, I describe paired learning and inference algorithms for dynamic feature selection which accelerate inference in linear classifiers, the heart of the fastest NLP models, by 5-10 times. I then present iterated dilated convolutional neural networks (ID-CNNs), a distinct combination of network structure, parameter sharing and training procedures that increase inference speed by 14-20 times with accuracy matching bidirectional LSTMs, the most accurate models for NLP sequence labeling. Finally, I describe linguistically-informed self-attention (LISA), a neural network model that combines multi-head self-attention with multi-task learning to facilitate improved generalization to new domains. We show that incorporating linguistic structure in this way leads to substantial improvements over the previous state-of-the-art (syntax-free) neural network models for SRL, especially when evaluating out-of-domain. I conclude with a brief discussion of potential future directions stemming from my thesis work

Doctor of Philosophy

dissertationOver 40 years ago, the first computer simulation of a protein was reported: the atomic motions of a 58 amino acid protein were simulated for few picoseconds. With today's supercomputers, simulations of large biomolecular systems with hundreds of thousands of atoms can reach biologically significant timescales. Through dynamics information biomolecular simulations can provide new insights into molecular structure and function to support the development of new drugs or therapies. While the recent advances in high-performance computing hardware and computational methods have enabled scientists to run longer simulations, they also created new challenges for data management. Investigators need to use local and national resources to run these simulations and store their output, which can reach terabytes of data on disk. Because of the wide variety of computational methods and software packages available to the community, no standard data representation has been established to describe the computational protocol and the output of these simulations, preventing data sharing and collaboration. Data exchange is also limited due to the lack of repositories and tools to summarize, index, and search biomolecular simulation datasets. In this dissertation a common data model for biomolecular simulations is proposed to guide the design of future databases and APIs. The data model was then extended to a controlled vocabulary that can be used in the context of the semantic web. Two different approaches to data management are also proposed. The iBIOMES repository offers a distributed environment where input and output files are indexed via common data elements. The repository includes a dynamic web interface to summarize, visualize, search, and download published data. A simpler tool, iBIOMES Lite, was developed to generate summaries of datasets hosted at remote sites where user privileges and/or IT resources might be limited. These two informatics-based approaches to data management offer new means for the community to keep track of distributed and heterogeneous biomolecular simulation data and create collaborative networks