A Personalized Human Drivers\u27 Risk Sensitive Characteristics Depicting Stochastic Optimal Control Algorithm for Adaptive Cruise Control

This paper presents a personalized stochastic optimal adaptive cruise control (ACC) algorithm for automated vehicles (AVs) incorporating human drivers\u27 risk-sensitivity under system and measurement uncertainties. The proposed controller is designed as a linear exponential-of-quadratic Gaussian (LEQG) problem, which utilizes the stochastic optimal control mechanism to feedback the deviation from the design car-following target. With the risk-sensitive parameter embedded in LEQG, the proposed method has the capability to characterize risk preference heterogeneity of each AV against uncertainties according to each human drivers\u27 preference. Further, the established control theory can achieve both expensive control mode and non-expensive control mode via changing the weighting matrix of the cost function in LEQG to reveal different treatments on input. Simulation tests validate the proposed approach can characterize different driving behaviors and its effectiveness in terms of reducing the deviation from equilibrium state. The ability to produce different trajectories and generate smooth control of the proposed algorithm is also verified

Robot Manipulators

Robot manipulators are developing more in the direction of industrial robots than of human workers. Recently, the applications of robot manipulators are spreading their focus, for example Da Vinci as a medical robot, ASIMO as a humanoid robot and so on. There are many research topics within the field of robot manipulators, e.g. motion planning, cooperation with a human, and fusion with external sensors like vision, haptic and force, etc. Moreover, these include both technical problems in the industry and theoretical problems in the academic fields. This book is a collection of papers presenting the latest research issues from around the world

Control of Multi-agent Reinforcement Learning Systems Under Adversarial Attacks

This Ph.D. dissertation studies the control of multi-agent reinforcement learning (MARL) and multi-agent deep reinforcement learning (MADRL) systems under adversarial attacks. Various attacks are investigated, and several defence algorithms (mitigation approaches) are proposed to assist the consensus control and proper data transmission. We studied the consensus problem of a leaderless, homogeneous MARL system using actor-critic algorithms, with and without malicious agents. We considered various distance-based immediate reward functions to improve the system's performance. In addition to proposing four different immediate reward functions based on Euclidean, n-norm, and Chebyshev distances, we rigorously demonstrated which reward function performs better based on a cumulative reward for each agent and the entire team of agents. The claims have been proven theoretically, and the simulation confirmed theoretical findings. We examined whether modifying the malicious agent's neural network (NN) structure, as well as providing a compatible combination of the mean squared error (MSE) loss function and the sigmoid activation function can mitigate the destructive effects of the malicious agent on the leaderless, homogeneous, MARL system performance. In addition to the theoretical support, the simulation confirmed the findings of the theory. We studied the gradient-based adversarial attacks on cluster-based, heterogeneous MADRL systems with time-delayed data transmission using deep Q-network (DQN) algorithms. We introduced two novel observations, termed on-time and time-delay observations, considered when the data transmission channel is idle and the data is transmitted on-time or time-delayed. By considering the distance between the neighbouring agents, we presented a novel immediate reward function that appends a distance-based reward to the previously utilized reward to improve the MADRL system performance. We considered three types of gradient-based attacks to investigate the robustness of the proposed system data transmission. Two defence methods were proposed to reduce the effects of the discussed malicious attacks. The theoretical results are illustrated and verified with simulation examples. We also investigated the data transmission robustness between agents of a cluster-based, heterogeneous MADRL system under a gradient-based adversarial attack. An algorithm using a DQN approach and a proportional feedback controller to defend against the fast gradient sign method (FGSM) attack and improve the DQN agent performance was proposed. Simulation results are included to verify the presented results

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

A Cultural Heritage Management Methodology for Assessing the Vulnerabilities of Archaeological Sites to Predicted Climate Change Focuing on Ireland\u27s Two World Heritage Sites

The affect climate change will have on cultural heritage preservation poses a global challenge and is being addressed by international organisations such as UNESCO and ICOMOS. The aim of this doctoral research is to assist heritage managers in understanding the implications of climate change for the sites in their care. It addresses the question of how to approach the assessment and measurement of climate change impacts on cultural heritage. The potential future effects of climate change on cultural heritage in temperate climates are discussed and current international practice in the management of climate change impacts on cultural heritage is investigated. The results reveal several issues currently of concern amongst practitioners; namely ‘what’ to monitor, ‘how’ to monitor and how to interpret results when dealing with the highly complex and long-term issue of climate change impacts. A Vulnerability Framework for site based evaluations is defined and adapted specifically for cultural heritage. This six step method relies on expert judgement and stakeholder involvement; it is a place based approach studying the coupled ‘human-environment system’. The Framework is illustrated through the assessment of the vulnerability of Ireland’s World Heritage Sites, Skellig Michael and Brú na Bóinne, to the impacts of projected climate change up to 2100. The results suggest that the projected alterations in rainfall will be the most problematic climate change factor for both sites. Climate change indicators developed as part of the Vulnerability Framework are proposed as a solution to the problem of longterm monitoring. The development of a general Toolbox of Indicators is accompanied by the design and pilot trial of a Legacy Indicator Tool (LegIT). This tool, for tracking the surface weathering of stone and related materials, can be tailored to the needs of individual heritage sites and is currently being piloted at five monuments in Ireland, including the two case studies. Phase One – Initial Vulnerability Assessment Cycle. Phase Two – Subsequent ongoing Adaptation and Review Cycle. Cultural Heritage Management Model developed for the assessment of, and adaptation to, climate change impacts In this research transferable methodologies for the site level assessment and measurement of climate change vulnerabilities are developed and applied in practice. The Vulnerability Framework, Impacts Matrix, Toolbox of Indicators and Legacy Indicator Tool (LegIT) are original and transferable outputs. They will aid decision makers with planning and prioritisation for the case study sites and provide a management model that has the potential to facilitate assessments at other sites in Ireland and internationally