Organizational Posthumanism

Building on existing forms of critical, cultural, biopolitical, and sociopolitical posthumanism, in this text a new framework is developed for understanding and guiding the forces of technologization and posthumanization that are reshaping contemporary organizations. This ‘organizational posthumanism’ is an approach to analyzing, creating, and managing organizations that employs a post-dualistic and post-anthropocentric perspective and which recognizes that emerging technologies will increasingly transform the kinds of members, structures, systems, processes, physical and virtual spaces, and external ecosystems that are available for organizations to utilize. It is argued that this posthumanizing technologization of organizations will especially be driven by developments in three areas: 1) technologies for human augmentation and enhancement, including many forms of neuroprosthetics and genetic engineering; 2) technologies for synthetic agency, including robotics, artificial intelligence, and artificial life; and 3) technologies for digital-physical ecosystems and networks that create the environments within which and infrastructure through which human and artificial agents will interact. Drawing on a typology of contemporary posthumanism, organizational posthumanism is shown to be a hybrid form of posthumanism that combines both analytic, synthetic, theoretical, and practical elements. Like analytic forms of posthumanism, organizational posthumanism recognizes the extent to which posthumanization has already transformed businesses and other organizations; it thus occupies itself with understanding organizations as they exist today and developing strategies and best practices for responding to the forces of posthumanization. On the other hand, like synthetic forms of posthumanism, organizational posthumanism anticipates the fact that intensifying and accelerating processes of posthumanization will create future realities quite different from those seen today; it thus attempts to develop conceptual schemas to account for such potential developments, both as a means of expanding our theoretical knowledge of organizations and of enhancing the ability of contemporary organizational stakeholders to conduct strategic planning for a radically posthumanized long-term future

Effective task allocation frameworks for large-scale multiple agent systems.

This research aims to develop innovative and transformative decision-making frameworks that enable a large-scale multi-robot system, called robotic swarm, to autonomously address multi-robot task allocation problem: given a set of complicated tasks, requiring cooperation, how to partition themselves into subgroups (or called coalitions) and assign the subgroups to each task while maximising the system performance. The frameworks should be executable based on local information in a decentralised manner, operable for a wide range of the system size (i.e., scalable), predictable in terms of collective behaviours, adaptable to dynamic environments, operable asynchronously, and preferably able to accommodate heterogeneous agents. Firstly, for homogeneous robots, this thesis proposes two frameworks based on biological inspiration and game theories, respectively. The former, called LICA-MC (Markov-Chan-based approach under Local Information Consistency Assumption), is inspired by fish in nature: despite insufficient awareness of the entire group, they are well-coordinated by sensing social distances from neighbours. Analogously, each agent in the framework relies only on local information and requires its local consistency over neighbouring agents to adaptively generate the stochastic policy. This feature offers various advantages such as less inter-agent communication, a shorter timescale for using new information, and the potential to accommodate asynchronous behaviours of agents. We prove that the agents can converge to a desired collective status without resorting to any global information, while maintaining scalability, flexibility, and long-term system efficiency. Numerical experiments show that the framework is robust in a realistic environment where information sharing over agents is partially and temporarily disconnected. Furthermore, we explicitly present the design requirements to have all these advantages, and implementation examples concerning travelling costs minimisation, over-congestion avoidance, and quorum models, respectively. The game-theoretical framework, called GRAPE (GRoup Agent Partitioning and placing Event), regards each robot as a self-interested player attempting to join the most preferred coalition according to its individual preferences regarding the size of each coalition. We prove that selfish agents who have social inhibition can always converge to a Nash stable partition (i.e., a social agreement) within polynomial time under the proposed framework. The framework is executable based on local interactions with neighbour agents under a strongly-connected communication network and even in asynchronous environments. This study analyses an outcome’s minimum-guaranteed suboptimality, and additionally shows that at least 50% is guaranteed if social utilities are non-decreasing functions with respect to the number of co-working agents. Numerical experiments confirm that the framework is scalable, fast adaptable against dynamical environments, and robust even in a realistic situation where some of the agents temporarily halt operation during a mission. The two proposed frameworks are compared in the domain of division of labour. Empirical results show that LICA-MC provides excellent scalability with respect to the number of agents, whereas GRAPE has polynomial complexity but is more efficient in terms of convergence time (especially when accommodating a moderate number of robots) and total travelling costs. It also turns out that GRAPE is sensitive to traffic congestion, meanwhile LICA-MC suffers from slower robot speed. We discuss other implicit advantages of the frameworks such as mission suitability and additionally-builtin decision-making functions. Importantly, it is found that GRAPE has the potential to accommodate heterogeneous agents to some extent, which is not the case for LICA-MC. Accordingly, this study attempts to extend GRAPE to incorporate the heterogeneity of agents. Particularly, we consider the case where each task has its minimum workload requirement to be fulfilled by multiple agents and the agents have different work capacities and costs depending on the tasks. The objective is to find an assignment that minimises the total cost of assigned agents while satisfying the requirements. GRAPE cannot be directly used because of the heterogeneity, so we adopt tabu-learning heuristics where an agent penalises its previously chosen coalition whenever it changes decision: this variant is called T-GRAPE. We prove that, by doing so, a Nash stable partition is always guaranteed to be determined in a decentralised manner. Experi-mental results present the properties of the proposed approach regarding suboptimality and algorithmic complexity. Finally, the thesis addresses a more complex decision-making problem involving team formation, team-to-task assignment, agent-to-working-position selection, fair resource allocation concerning tasks’ minimum requirements for completion, and trajectory optimisation with collision avoidance. We propose an integrated framework that decouples the original problem into three subproblems (i.e., coalition formation, position allocation, and path planning) and deals with them sequentially by three respective modules. The coalition formation module based on T-GRAPE deals with a max-min problem, balancing the work resources of agents in proportion to the task’s requirements. We show that, given reasonable assumptions, the position allocation subproblem can be solved efficiently in terms of computational complexity. For the path planning, we utilise an MPC-SCP (Model Predictive Control and Sequential Convex Programming) approach that enables the agents to produce collision-free trajectories. As a proof of concept, we implement the framework into a cooperative stand-in jamming mission scenario using multiple UAVs. Numerical experiments suggest that the framework could be computationally feasible, fault-tolerant, and near-optimal. Comparison of the proposed frameworks for multi-robot task allocation is discussed in the last chapter regarding the desired features described at first (i.e., decentralisation, scalability, predictability, flexibility, asynchronisation, heterogeneity), along with future work and possible applications in other domains.PhD in Aerospac

Living Longer Using today’s emerging technology to address issues related to aging in Canada in the year 2032

In April 2009, Canada’s Special Senate Committee on Aging released its final report listing the issues affecting Older Adults (age 65 and over). This demographic will account for one quarter of Canada’s population by the year 2032. The report indicates the need for further research on aging and promotes technology as a tool to address these issues. Using Rogers’ theory of Diffusion of Innovations, Riley’s theory of Structural Lag, Davis’ Technology Acceptance Model and ethnographic research methods to observe trends in attitudinal shifts, this research paper explores the adoption, affinity and application of existing and emerging technology to address the issues related to aging of Canadian Older Adults in the year 2032

White Paper 11: Artificial intelligence, robotics & data science

198 p. : 17 cmSIC white paper on Artificial Intelligence, Robotics and Data Science sketches a preliminary roadmap for addressing current R&D challenges associated with automated and autonomous machines. More than 50 research challenges investigated all over Spain by more than 150 experts within CSIC are presented in eight chapters. Chapter One introduces key concepts and tackles the issue of the integration of knowledge (representation), reasoning and learning in the design of artificial entities. Chapter Two analyses challenges associated with the development of theories –and supporting technologies– for modelling the behaviour of autonomous agents. Specifically, it pays attention to the interplay between elements at micro level (individual autonomous agent interactions) with the macro world (the properties we seek in large and complex societies). While Chapter Three discusses the variety of data science applications currently used in all fields of science, paying particular attention to Machine Learning (ML) techniques, Chapter Four presents current development in various areas of robotics. Chapter Five explores the challenges associated with computational cognitive models. Chapter Six pays attention to the ethical, legal, economic and social challenges coming alongside the development of smart systems. Chapter Seven engages with the problem of the environmental sustainability of deploying intelligent systems at large scale. Finally, Chapter Eight deals with the complexity of ensuring the security, safety, resilience and privacy-protection of smart systems against cyber threats.18 EXECUTIVE SUMMARY ARTIFICIAL INTELLIGENCE, ROBOTICS AND DATA SCIENCE Topic Coordinators Sara Degli Esposti ( IPP-CCHS, CSIC ) and Carles Sierra ( IIIA, CSIC ) 18 CHALLENGE 1 INTEGRATING KNOWLEDGE, REASONING AND LEARNING Challenge Coordinators Felip Manyà ( IIIA, CSIC ) and Adrià Colomé ( IRI, CSIC – UPC ) 38 CHALLENGE 2 MULTIAGENT SYSTEMS Challenge Coordinators N. Osman ( IIIA, CSIC ) and D. López ( IFS, CSIC ) 54 CHALLENGE 3 MACHINE LEARNING AND DATA SCIENCE Challenge Coordinators J. J. Ramasco Sukia ( IFISC ) and L. Lloret Iglesias ( IFCA, CSIC ) 80 CHALLENGE 4 INTELLIGENT ROBOTICS Topic Coordinators G. Alenyà ( IRI, CSIC – UPC ) and J. Villagra ( CAR, CSIC ) 100 CHALLENGE 5 COMPUTATIONAL COGNITIVE MODELS Challenge Coordinators M. D. del Castillo ( CAR, CSIC) and M. Schorlemmer ( IIIA, CSIC ) 120 CHALLENGE 6 ETHICAL, LEGAL, ECONOMIC, AND SOCIAL IMPLICATIONS Challenge Coordinators P. Noriega ( IIIA, CSIC ) and T. Ausín ( IFS, CSIC ) 142 CHALLENGE 7 LOW-POWER SUSTAINABLE HARDWARE FOR AI Challenge Coordinators T. Serrano ( IMSE-CNM, CSIC – US ) and A. Oyanguren ( IFIC, CSIC - UV ) 160 CHALLENGE 8 SMART CYBERSECURITY Challenge Coordinators D. Arroyo Guardeño ( ITEFI, CSIC ) and P. Brox Jiménez ( IMSE-CNM, CSIC – US )Peer reviewe

Bio-inspired optimization in integrated river basin management

Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

Cognitive Hyperconnected Digital Transformation

Cognitive Hyperconnected Digital Transformation provides an overview of the current Internet of Things (IoT) landscape, ranging from research, innovation and development priorities to enabling technologies in a global context. It is intended as a standalone book in a series that covers the Internet of Things activities of the IERC-Internet of Things European Research Cluster, including both research and technological innovation, validation and deployment. The book builds on the ideas put forward by the European Research Cluster, the IoT European Platform Initiative (IoT-EPI) and the IoT European Large-Scale Pilots Programme, presenting global views and state-of-the-art results regarding the challenges facing IoT research, innovation, development and deployment in the next years. Hyperconnected environments integrating industrial/business/consumer IoT technologies and applications require new IoT open systems architectures integrated with network architecture (a knowledge-centric network for IoT), IoT system design and open, horizontal and interoperable platforms managing things that are digital, automated and connected and that function in real-time with remote access and control based on Internet-enabled tools. The IoT is bridging the physical world with the virtual world by combining augmented reality (AR), virtual reality (VR), machine learning and artificial intelligence (AI) to support the physical-digital integrations in the Internet of mobile things based on sensors/actuators, communication, analytics technologies, cyber-physical systems, software, cognitive systems and IoT platforms with multiple functionalities. These IoT systems have the potential to understand, learn, predict, adapt and operate autonomously. They can change future behaviour, while the combination of extensive parallel processing power, advanced algorithms and data sets feed the cognitive algorithms that allow the IoT systems to develop new services and propose new solutions. IoT technologies are moving into the industrial space and enhancing traditional industrial platforms with solutions that break free of device-, operating system- and protocol-dependency. Secure edge computing solutions replace local networks, web services replace software, and devices with networked programmable logic controllers (NPLCs) based on Internet protocols replace devices that use proprietary protocols. Information captured by edge devices on the factory floor is secure and accessible from any location in real time, opening the communication gateway both vertically (connecting machines across the factory and enabling the instant availability of data to stakeholders within operational silos) and horizontally (with one framework for the entire supply chain, across departments, business units, global factory locations and other markets). End-to-end security and privacy solutions in IoT space require agile, context-aware and scalable components with mechanisms that are both fluid and adaptive. The convergence of IT (information technology) and OT (operational technology) makes security and privacy by default a new important element where security is addressed at the architecture level, across applications and domains, using multi-layered distributed security measures. Blockchain is transforming industry operating models by adding trust to untrusted environments, providing distributed security mechanisms and transparent access to the information in the chain. Digital technology platforms are evolving, with IoT platforms integrating complex information systems, customer experience, analytics and intelligence to enable new capabilities and business models for digital business

Comprehensive Taxonomies of Nature- and Bio-inspired Optimization: Inspiration versus Algorithmic Behavior, Critical Analysis and Recommendations

In recent years, a great variety of nature- and bio-inspired algorithms has been reported in the literature. This algorithmic family simulates different biological processes observed in Nature in order to efficiently address complex optimization problems. In the last years the number of bio-inspired optimization approaches in literature has grown considerably, reaching unprecedented levels that dark the future prospects of this field of research. This paper addresses this problem by proposing two comprehensive, principle-based taxonomies that allow researchers to organize existing and future algorithmic developments into well-defined categories, considering two different criteria: the source of inspiration and the behavior of each algorithm. Using these taxonomies we review more than three hundred publications dealing with nature-inspired and bio-inspired algorithms, and proposals falling within each of these categories are examined, leading to a critical summary of design trends and similarities between them, and the identification of the most similar classical algorithm for each reviewed paper. From our analysis we conclude that a poor relationship is often found between the natural inspiration of an algorithm and its behavior. Furthermore, similarities in terms of behavior between different algorithms are greater than what is claimed in their public disclosure: specifically, we show that more than one-third of the reviewed bio-inspired solvers are versions of classical algorithms. Grounded on the conclusions of our critical analysis, we give several recommendations and points of improvement for better methodological practices in this active and growing research field.Comment: 76 pages, 6 figure