Application of Elementary Neural Networks and Preview Sensors for Representing Driver Steering Control Behaviour

This paper demonstrates the use of elementary neural networks for modelling and representing driver steering behaviour in path regulation control tasks. Areas of application include uses by vehicle simulation experts who need to model and represent specific instances of driver steering control behaviour, potential on-board vehicle technologies aimed at representing and tracking driver steering control behaviour over time, and use by human factors specialists interested in representing or classifying specific families of driver steering behaviour. Example applications are shown for data obtained from a driver/vehicle numerical simulation, a basic driving simulator, and an experimental on-road test vehicle equipped with a camera and sensor processing system.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65023/1/MacAdam & Johnson 1996 VSD NNet Control paper.pd

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Understanding and Modeling the Human Driver

This paper examines the role of the human driver as the primary control element within the traditional driver-vehicle system. Lateral and longitudinal control tasks such as path-following, obstacle avoidance, and headway control are examples of steering and braking activities performed by the human driver. Physical limitations as well as various attributes that make the human driver unique and help to characterize human control behavior are described. Example driver models containing such traits and that are commonly used to predict the performance of the combined driver-vehicle system in lateral and longitudinal control tasks are identified.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/65021/1/MacAdam_2003 VSD Understanding and Modelling the Driver.pd

Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

L'abstract è presente nell'allegato / the abstract is in the attachmen

Journey of Artificial Intelligence Frontier: A Comprehensive Overview

The field of Artificial Intelligence AI is a transformational force with limitless promise in the age of fast technological growth This paper sets out on a thorough tour through the frontiers of AI providing a detailed understanding of its complex environment Starting with a historical context followed by the development of AI seeing its beginnings and growth On this journey fundamental ideas are explored looking at things like Machine Learning Neural Networks and Natural Language Processing Taking center stage are ethical issues and societal repercussions emphasising the significance of responsible AI application This voyage comes to a close by looking ahead to AI s potential for human-AI collaboration ground-breaking discoveries and the difficult obstacles that lie ahead This provides with a well-informed view on AI s past present and the unexplored regions it promises to explore by thoroughly navigating this terrai

Learning human navigational skill for smart wheelchair.

by Hon Nin Chow.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 79-84).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Organization of the Thesis --- p.3Chapter 2 --- Literature Survey --- p.6Chapter 2.1 --- Learning-by-Demonstration --- p.6Chapter 2.2 --- Neural Networks --- p.7Chapter 2.3 --- Navigation Learning --- p.8Chapter 2.4 --- Localization --- p.9Chapter 2.5 --- Robotic Wheelchair --- p.10Chapter 3 --- System Implementation --- p.12Chapter 3.1 --- Hardware Platform --- p.12Chapter 3.2 --- Software Platform --- p.14Chapter 3.3 --- Basic Functionality --- p.15Chapter 3.3.1 --- Collision Avoidance --- p.15Chapter 3.3.2 --- Wearable Eye-jaw Control Interface --- p.16Chapter 4 --- Learning Human Navigational Skill --- p.22Chapter 4.1 --- Introduction --- p.22Chapter 4.2 --- Problem Formulation --- p.23Chapter 4.3 --- Approach --- p.23Chapter 4.4 --- Experimental Study --- p.26Chapter 4.4.1 --- Settings --- p.26Chapter 4.4.2 --- Results --- p.30Chapter 4.5 --- Discussions --- p.31Chapter 5 --- Learning from Multi-phase Demonstrations --- p.33Chapter 5.1 --- Introduction --- p.33Chapter 5.2 --- Problem Formulation --- p.34Chapter 5.3 --- Approach --- p.35Chapter 5.4 --- Experimental Study --- p.35Chapter 5.4.1 --- Settings --- p.35Chapter 5.4.2 --- Results --- p.37Chapter 5.5 --- Evaluation of Learning Performance --- p.37Chapter 5.6 --- Discussions --- p.43Chapter 6 --- Localization Learning --- p.44Chapter 6.1 --- Introduction --- p.44Chapter 6.2 --- Problem Formulation --- p.45Chapter 6.3 --- Approach --- p.45Chapter 6.4 --- Experimental Study --- p.46Chapter 6.4.1 --- Settings --- p.46Chapter 6.4.2 --- Result 1: Localization Performance --- p.47Chapter 6.4.3 --- Result 2: Similar Sensor Patterns in Various Configurations . --- p.53Chapter 6.4.4 --- Result 3: Small Variations in Major Dimensions of Environ- mental Feature along the Route --- p.53Chapter 6.5 --- Discussions --- p.59Chapter 6.5.1 --- Accuracy --- p.59Chapter 6.5.2 --- Choices of Sensor-Configuration Mappings --- p.60Chapter 7 --- Conclusion --- p.62Chapter 7.1 --- Contributions --- p.62Chapter 7.2 --- Future Work --- p.65Chapter A --- Cascade Neural Network --- p.67Chapter B --- Trajectories for the Navigation Learning in Chapter 4 --- p.69Chapter C --- Publications Resulted from the Study --- p.7

Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning

Teaching a Robot to Drive - A Skill Learning Inspired Approach

Roboter können unser Leben erleichtern, indem sie für uns unangenehme, oder sogar gefährliche Aufgaben übernehmen. Um sie effizient einsetzen zu können, sollten sie autonom, adaptiv und einfach zu instruieren sein. Traditionelle 'white-box'-Ansätze in der Robotik basieren auf dem Verständnis des Ingenieurs der unterliegenden physikalischen Struktur des gegebenen Problems. Ausgehend von diesem Verständnis kann der Ingenieur eine mögliche Lösung finden und es in dem System implementieren. Dieser Ansatz ist sehr mächtig, aber gleichwohl limitiert. Der wichtigste Nachteil ist, dass derart erstellte Systeme von vordefiniertem Wissen abhängen und deswegen jedes neue Verhalten den gleichen, teuren Entwicklungszyklus benötigt. Im Gegensatz dazu sind Menschen und einige andere Tiere nicht auf ihre angeborene Verhalten beschränkt, sondern können während ihrer Lebenszeit vielzählige weitere Fähigkeiten erwerben. Zusätzlich scheinen sie dazu kein detailliertes Wissen über den (physikalische) Ablauf einer gegebenen Aufgabe zu benötigen. Diese Eigenschaften sind auch für künstliche Systeme wünschenswert. Deswegen untersuchen wir in dieser Dissertation die Hypothese, dass Prinzipien des menschlichen Fähigkeitslernens zu alternativen Methoden für adaptive Systemkontrolle führen können. Wir untersuchen diese Hypothese anhand der Aufgabe des Autonomen Fahrens, welche ein klassiches Problem der Systemkontrolle darstellt und die Möglichkeit für vielfältige Applikationen bietet. Die genaue Aufgabe ist das Erlernen eines grundlegenden, antizipatorischen Fahrverhaltens von einem menschlichem Lehrer. Nachdem wir relevante Aspekte bezüglich des menschlichen Fähigkeitslernen aufgezeigt haben, und die Begriffe 'interne Modelle' und 'chunking' eingeführt haben, beschreiben wir die Anwendung dieser auf die gegebene Aufgabe. Wir realisieren chunking mit Hilfe einer Datenbank in welcher Beispiele menschlichen Fahreverhaltens gespeichert werden und mit Beschreibungen der visuell erfassten Strassentrajektorie verknüpft werden. Dies wird zunächst innerhalb einer Laborumgebung mit Hilfe eines Roboters verwirklicht und später, im Laufe des Europäischen DRIVSCO Projektes, auf ein echtes Auto übertragen. Wir untersuchen ausserdem das Erlernen visueller 'Vorwärtsmodelle', welche zu den internen Modellen gehören, sowie ihren Effekt auf die Kontrollperformanz beim Roboter. Das Hauptresultat dieser interdisziplinären und anwendungsorientierten Arbeit ist ein System, welches in der Lage ist als Antwort auf die visuell wahrgenommene Strassentrajektorie entsprechende Aktionspläne zu generieren, ohne das dazu metrische Informationen benötigt werden. Die vorhergesagten Aktionen in der Laborumgebung sind Lenken und Geschwindigkeit. Für das echte Auto Lenken und Beschleunigung, wobei die prediktive Kapazität des Systems für Letzteres beschränkt ist. D.h. der Roboter lernt autonomes Fahren von einem menschlichen Lehrer und das Auto lernt die Vorhersage menschlichen Fahrverhaltens. Letzteres wurde während der Begutachtung des Projektes duch ein internationales Expertenteam erfolgreich demonstriert. Das Ergebnis dieser Arbeit ist relevant für Anwendungen in der Roboterkontrolle und dabei besonders in dem Bereich intelligenter Fahrerassistenzsysteme

Deep Reinforcement Learning Approaches for Technology Enhanced Learning

Artificial Intelligence (AI) has advanced significantly in recent years, transforming various industries and domains. Its ability to extract patterns and insights from large volumes of data has revolutionised areas such as image recognition, natural language processing, and autonomous systems. As AI systems become increasingly integrated into daily human life, there is a growing need for meaningful collaboration and mutual engagement between humans and AI, known as Human-AI Collaboration. This collaboration involves combining AI with human workflows to achieve shared objectives. In the current educational landscape, the integration of AI methods in Technology Enhanced Learning (TEL) has become crucial for providing high-quality education and facilitating lifelong learning. Human-AI Collaboration also plays a vital role in the field of Technology Enhanced Learning (TEL), particularly in Intelligent Tutoring Systems (ITS). The COVID-19 pandemic has further emphasised the need for effective educational technologies to support remote learning and bridge the gap between traditional classrooms and online platforms. To maximise the performance of ITS while minimising the input and interaction required from students, it is essential to design collaborative systems that effectively leverage the capabilities of AI and foster effective collaboration between students and ITS. However, there are several challenges that need to be addressed in this context. One challenge is the lack of clear guidance on designing and building user-friendly systems that facilitate collaboration between humans and AI. This challenge is relevant not only to education researchers but also to Human-Computer Interaction (HCI) researchers and developers. Another challenge is the scarcity of interaction data in the early stages of ITS development, which hampers the accurate modelling of students' knowledge states and learning trajectories, known as the cold start problem. Moreover, the effectiveness of Intelligent Tutoring Systems (ITS) in delivering personalised instruction is hindered by the limitations of existing Knowledge Tracing (KT) models, which often struggle to provide accurate predictions. Therefore, addressing these challenges is crucial for enhancing the collaborative process between humans and AI in the development of ITS. This thesis aims to address these challenges and improve the collaborative process between students and ITS in TEL. It proposes innovative approaches to generate simulated student behavioural data and enhance the performance of KT models. The thesis starts with a comprehensive survey of human-AI collaborative systems, identifying key challenges and opportunities. It then presents a structured framework for the student-ITS collaborative process, providing insights into designing user-friendly and efficient systems. To overcome the challenge of data scarcity in ITS development, the thesis proposes two student modelling approaches: Sim-GAIL and SimStu. SimStu leverages a deep learning method, the Decision Transformer, to simulate student interactions and enhance ITS training. Sim-GAIL utilises a reinforcement learning method, Generative Adversarial Imitation Learning (GAIL), to generate high-fidelity and diverse simulated student behavioural data, addressing the cold start problem in ITS training. Furthermore, the thesis focuses on improving the performance of KT models. It introduces the MLFBKT model, which integrates multiple features and mines latent relations in student interaction data, aiming to improve the accuracy and efficiency of KT models. Additionally, the thesis proposes the LBKT model, which combines the strengths of the BERT model and LSTM to process long sequence data in KT models effectively. Overall, this thesis contributes to the field of Human-AI collaboration in TEL by addressing key challenges and proposing innovative approaches to enhance ITS training and KT model performance. The findings have the potential to improve the learning experiences and outcomes of students in educational settings