Future state maximisation as an intrinsic motivation for decision making

The concept of an “intrinsic motivation" is used in the psychology literature to distinguish between behaviour which is motivated by the expectation of an immediate, quantifiable reward (“extrinsic motivation") and behaviour which arises because it is inherently useful, interesting or enjoyable. Examples of the latter can include curiosity driven behaviour such as exploration and the accumulation of knowledge, as well as developing skills that might not be immediately useful but that have the potential to be re-used in a variety of different future situations. In this thesis, we examine a candidate for an intrinsic motivation with wide-ranging applicability which we refer to as “future state maximisation". Loosely speaking this is the idea that, taking everything else to be equal, decisions should be made so as to maximally keep one's options open, or to give the maximal amount of control over what one can potentially do in the future. Our goal is to study how this principle can be applied in a quantitative manner, as well as identifying examples of systems where doing so could be useful in either explaining or generating behaviour. We consider a number of examples, however our primary application is to a model of collective motion in which we consider a group of agents equipped with simple visual sensors, moving around in two dimensions. In this model, agents aim to make decisions about how to move so as to maximise the amount of control they have over the potential visual states that they can access in the future. We find that with each agent following this simple, low-level motivational principle a swarm spontaneously emerges in which the agents exhibit rich collective behaviour, remaining cohesive and highly-aligned. Remarkably, the emergent swarm also shares a number of features which are observed in real flocks of starlings, including scale free correlations and marginal opacity. We go on to explore how the model can be developed to allow us to manipulate and control the swarm, as well as looking at heuristics which are able to mimic future state maximisation whilst requiring significantly less computation, and so which could plausibly operate under animal cognition

An information-theoretic account of human–computer interaction

This thesis presents a theoretical framework for the study of interactive systems, using methods from information theory, machine learning and control theory. The framework builds on the information-theoretic capacities of empowerment, relevant information and mutual information, which I adapt and apply to the domain of human-computer interaction. Three user studies exploring dynamic interactive scenarios - one car-tracking and two collaborative target-acquisition experiments - provide empirical data for the development of probabilistic models, used in the characterisation of specific aspects of human performance, such as the level of control, the quality of decision-making, and the level of engagement in interpersonal coordination. Human control models are extended to accommodate for the inherent lags, characteristic for human-computer and human-human interaction, in a principled way. Optimal controllers, describing particular patterns of human behaviour, are built on these theoretical models, providing evidence for specific limits of human performance through simulations. The thesis describes the potential of empowerment, as a generic task-independent measure of control, to characterise the uncertainty in human-machine interfaces. This work builds an important bridge between theory and experiments, and suggests that the proposed information-theoretic concepts could provide analytical tools for supporting the design and evaluation of interactive systems, by elucidating novel aspects of human performance complementing standard measures. The thesis provides proof of concept examples for the application of such information-theoretic measures, and demonstrates how they can be treated naturally side-by-side along traditional metrics used in HCI research. It emphasises the acquisition cost of accurate theoretical models, necessary to ensure the reliability of such measures

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Statistical modelling of algorithms for signal processing in systems based on environment perception

One cornerstone for realising automated driving systems is an appropriate handling of uncertainties in the environment perception and situation interpretation. Uncertainties arise due to noisy sensor measurements or the unknown future evolution of a traffic situation. This work contributes to the understanding of these uncertainties by modelling and propagating them with parametric probability distributions

An Overview of Deep-Learning-Based Audio-Visual Speech Enhancement and Separation

Speech enhancement and speech separation are two related tasks, whose purpose is to extract either one or more target speech signals, respectively, from a mixture of sounds generated by several sources. Traditionally, these tasks have been tackled using signal processing and machine learning techniques applied to the available acoustic signals. Since the visual aspect of speech is essentially unaffected by the acoustic environment, visual information from the target speakers, such as lip movements and facial expressions, has also been used for speech enhancement and speech separation systems. In order to efficiently fuse acoustic and visual information, researchers have exploited the flexibility of data-driven approaches, specifically deep learning, achieving strong performance. The ceaseless proposal of a large number of techniques to extract features and fuse multimodal information has highlighted the need for an overview that comprehensively describes and discusses audio-visual speech enhancement and separation based on deep learning. In this paper, we provide a systematic survey of this research topic, focusing on the main elements that characterise the systems in the literature: acoustic features; visual features; deep learning methods; fusion techniques; training targets and objective functions. In addition, we review deep-learning-based methods for speech reconstruction from silent videos and audio-visual sound source separation for non-speech signals, since these methods can be more or less directly applied to audio-visual speech enhancement and separation. Finally, we survey commonly employed audio-visual speech datasets, given their central role in the development of data-driven approaches, and evaluation methods, because they are generally used to compare different systems and determine their performance

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence