A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

A simulation-driven approach to non-compliance

This dissertation proposes a methodological framework for the use of simulation-based methods to investigate questions of non-compliance in a legal context. Its aim is to generate observed or previously unobserved instances of non-compliance and use them to improve compliance and trust in a given socio-economic infrastructure. The framework consists of three components: a normative system implemented as an agent-based model, a profit-driven agent generating instances of non-compliance, and a formalization process transforming the generated behavior into a formal model.The most sophisticated ways of law-breaking are typically associated with economic crime. For this reason, we investigated three case studies in the financial domain. The first case study develops an agent-based model investigating the collective response of compliant agents to market disturbances originated by fraudulent activity, as during the U.S. subprime mortgage crisis in 2007. The second case study investigates the price evolution in the Bitcoin market under the influence of the price manipulation that occurred in 2017/18. The third case study investigates Ponzi schemes on smart contracts. All case studies showed a high level of agreement with qualitative and quantitative observations. Identification, extraction, and formalization of non-compliant behavior generated via simulation is a central topic in the later chapters of the thesis. We introduce a method that considers fraudulent schemes as neighborhoods of profitable non-compliant behavior. We illustrate the method on a grid environment with a path-finding agent. This simplified case study has been chosen as it captures fundamental features of non-compliance, yet, further generalization is needed for real-world scenarios

Intelligent manipulation technique for multi-branch robotic systems

New analytical development in kinematics planning is reported. The INtelligent KInematics Planner (INKIP) consists of the kinematics spline theory and the adaptive logic annealing process. Also, a novel framework of robot learning mechanism is introduced. The FUzzy LOgic Self Organized Neural Networks (FULOSONN) integrates fuzzy logic in commands, control, searching, and reasoning, the embedded expert system for nominal robotics knowledge implementation, and the self organized neural networks for the dynamic knowledge evolutionary process. Progress on the mechanical construction of SRA Advanced Robotic System (SRAARS) and the real time robot vision system is also reported. A decision was made to incorporate the Local Area Network (LAN) technology in the overall communication system

Flexible human-robot cooperation models for assisted shop-floor tasks

The Industry 4.0 paradigm emphasizes the crucial benefits that collaborative robots, i.e., robots able to work alongside and together with humans, could bring to the whole production process. In this context, an enabling technology yet unreached is the design of flexible robots able to deal at all levels with humans' intrinsic variability, which is not only a necessary element for a comfortable working experience for the person but also a precious capability for efficiently dealing with unexpected events. In this paper, a sensing, representation, planning and control architecture for flexible human-robot cooperation, referred to as FlexHRC, is proposed. FlexHRC relies on wearable sensors for human action recognition, AND/OR graphs for the representation of and reasoning upon cooperation models, and a Task Priority framework to decouple action planning from robot motion planning and control.Comment: Submitted to Mechatronics (Elsevier

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Analytical/ML Mixed Approach for Concurrency Regulation in Software Transactional Memory

In this article we exploit a combination of analytical and Machine Learning (ML) techniques in order to build a performance model allowing to dynamically tune the level of concurrency of applications based on Software Transactional Memory (STM). Our mixed approach has the advantage of reducing the training time of pure machine learning methods, and avoiding approximation errors typically affecting pure analytical approaches. Hence it allows very fast construction of highly reliable performance models, which can be promptly and effectively exploited for optimizing actual application runs. We also present a real implementation of a concurrency regulation architecture, based on the mixed modeling approach, which has been integrated with the open source Tiny STM package, together with experimental data related to runs of applications taken from the STAMP benchmark suite demonstrating the effectiveness of our proposal. © 2014 IEEE

Space exploration: The interstellar goal and Titan demonstration

Automated interstellar space exploration is reviewed. The Titan demonstration mission is discussed. Remote sensing and automated modeling are considered. Nuclear electric propulsion, main orbiting spacecraft, lander/rover, subsatellites, atmospheric probes, powered air vehicles, and a surface science network comprise mission component concepts. Machine, intelligence in space exploration is discussed