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in Architecture & Planning
Periodicals

A Comprehensive Review of Blockchain Consensus Mechanisms

Since the advent of distributed ledger technologies, they have provided diverse opportunities in a wide range of application domains. This article brings a comprehensive review of the fundamentals of distributed ledger and its variants. Analyzing 185 publications, ranging from academic journals to industry websites, it provides a comparative analysis of 130 consensus algorithms using a novel architectural classification. The distribution of the reviewed algorithms is analyzed in terms of the proposed classification and different application domains, along with the applicability of each class among the top 10 platforms in the most prominent blockchain application domains. Additional conclusions are drawn from the evolution of consensus mechanisms, and the analysis concludes envisaging future prospects for consensus as an important part of distributed ledger technology.

Architecture & Planning2021Institute of Electrical and Electronics Engineers
Periodicals

Metabolism of Oligosaccharides and Starch in Lactobacilli: A Review

Oligosaccharides, compounds that are composed of 2-10 monosaccharide residues, are major carbohydrate sources in habitats populated by lactobacilli. Moreover, oligosaccharide metabolism is essential for ecological fitness of lactobacilli. Disaccharide metabolism by lactobacilli is well understood; however, few data on the metabolism of higher oligosaccharides are available. Research on the ecology of intestinal microbiota as well as the commercial application of prebiotics has shifted the interest from (digestible) disaccharides to (indigestible) higher oligosaccharides. This review provides an overview on oligosaccharide metabolism in lactobacilli. Emphasis is placed on maltodextrins, isomalto-oligosaccharides, fructo-oligosaccharides, galacto-oligosaccharides, and raffinose-family oligosaccharides. Starch is also considered. Metabolism is discussed on the basis of metabolic studies related to oligosaccharide metabolism, information on the cellular location and substrate specificity of carbohydrate transport systems, glycosyl hydrolases and phosphorylases, and the presence of metabolic genes in genomes of 38 strains of lactobacilli. Metabolic pathways for disaccharide metabolism often also enable the metabolism of tri- and tetrasaccharides. However, with the exception of amylase and levansucrase, metabolic enzymes for oligosaccharide conversion are intracellular and oligosaccharide metabolism is limited by transport. This general restriction to intracellular glycosyl hydrolases differentiates lactobacilli from other bacteria that adapted to intestinal habitats, particularly Bifidobacterium spp.

Architecture & Planning2012Frontiers Media
Periodicals

Technology Development of Electric Vehicles: A Review

To reduce the dependence on oil and environmental pollution, the development of electric vehicles has been accelerated in many countries. The implementation of EVs, especially battery electric vehicles, is considered a solution to the energy crisis and environmental issues. This paper provides a comprehensive review of the technical development of EVs and emerging technologies for their future application. Key technologies regarding batteries, charging technology, electric motors and control, and charging infrastructure of EVs are summarized. This paper also highlights the technical challenges and emerging technologies for the improvement of efficiency, reliability, and safety of EVs in the coming stages as another contribution.

Architecture & Planning2019Multidisciplinary Digital Publishing Institute
Periodicals

The future of Earth observation in hydrology

In just the past five years, the field of Earth observation has progressed beyond the offerings of conventional space agency based platforms to include a plethora of sensing opportunities afforded by CubeSats, Unmanned Aerial Vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically on the order of one billion dollars per satellite and with concept-to-launch timelines on the order of two decades (for new missions). More recently, the proliferation of smartphones has helped to miniaturise sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist five years ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of the cost of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-meter resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen-scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the Internet of Things as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilise and exploit these new observing systems to enhance our understanding of the Earth and its linked processes.

Architecture & Planning2017Copernicus Publications
Periodicals

A conceptual framework for supply chain collaboration: empirical evidence from the agri‐food industry

Purpose The purpose of this paper is to analyse the concept of supply chain collaboration and to provide an overall framework that can be used as a conceptual landmark for further empirical research. In addition, the concept is explored in the context of agri‐food industry and particularities are identified. Finally, the paper submits empirical evidence from an exploratory case study in the agri‐food industry, at the grower‐processor interface, and information regarding the way the concept is actually applied in small medium‐sized enterprises (SMEs) is presented. Design/methodology/approach The paper employed case study research by conducting in‐depth interviews in the two companies. Findings Supply chain collaboration concept is of significant importance for the agri‐food industry however, some constraints arise due to the nature of industry's products, and the specific structure of the sector. Subsequently, collaboration in the supply chain is often limited to operational issues and to logistics‐related activities. Research limitations/implications Research is limited to a single case study and further qualitative testing of the conceptual model is needed in order to adjust the model before large scale testing. Practical implications Case study findings may be transferable to other similar dual relationships at the grower‐processor interface. Weaker parts in asymmetric relationships have opportunities to improve their position, altering the dependence balance, by achieving product/process excellence. Originality/value The paper provides evidence regarding the applicability of the supply chain collaboration concept in the agri‐food industry. It takes into consideration not relationships between big multinational companies, but SMEs.

Architecture & Planning2007Emerald Publishing Limited
Periodicals

Tumor Ablation with Irreversible Electroporation

We report the first successful use of irreversible electroporation for the minimally invasive treatment of aggressive cutaneous tumors implanted in mice. Irreversible electroporation is a newly developed non-thermal tissue ablation technique in which certain short duration electrical fields are used to permanently permeabilize the cell membrane, presumably through the formation of nanoscale defects in the cell membrane. Mathematical models of the electrical and thermal fields that develop during the application of the pulses were used to design an efficient treatment protocol with minimal heating of the tissue. Tumor regression was confirmed by histological studies which also revealed that it occurred as a direct result of irreversible cell membrane permeabilization. Parametric studies show that the successful outcome of the procedure is related to the applied electric field strength, the total pulse duration as well as the temporal mode of delivery of the pulses. Our best results were obtained using plate electrodes to deliver across the tumor 80 pulses of 100 micros at 0.3 Hz with an electrical field magnitude of 2500 V/cm. These conditions induced complete regression in 12 out of 13 treated tumors, (92%), in the absence of tissue heating. Irreversible electroporation is thus a new effective modality for non-thermal tumor ablation.

Architecture & Planning2007Public Library of Science
Periodicals

COVID-19 Detection Through Transfer Learning Using Multimodal Imaging Data

Detecting COVID-19 early may help in devising an appropriate treatment plan and disease containment decisions. In this study, we demonstrate how transfer learning from deep learning models can be used to perform COVID-19 detection using images from three most commonly used medical imaging modes X-Ray, Ultrasound, and CT scan. The aim is to provide over-stressed medical professionals a second pair of eyes through intelligent deep learning image classification models. We identify a suitableConvolutional Neural Network(CNN) model through initial comparative study of several popular CNN models. We then optimize the selected VGG19 model for the image modalities to show how the models can be used for the highly scarce and challenging COVID-19 datasets. We highlight the challenges (including dataset size and quality) in utilizing current publicly available COVID-19 datasets for developing useful deep learning models and how it adversely impacts the trainability of complex models. We also propose an image pre-processing stage to create a trustworthy image dataset for developing and testing the deep learning models. The new approach is aimed to reduce unwanted noise from the images so that deep learning models can focus on detecting diseases with specific features from them. Our results indicate that Ultrasound images provide superior detection accuracy compared to X-Ray and CT scans. The experimental results highlight that with limited data, most of the deeper networks struggle to train well and provides less consistency over the three imaging modes we are using. The selected VGG19 model, which is then extensively tuned with appropriate parameters, performs in considerable levels of COVID-19 detection against pneumonia or normal for all three lung image modes with the precision of up to 86% for X-Ray, 100% for Ultrasound and 84% for CT scans.

Architecture & Planning2020Institute of Electrical and Electronics Engineers
Periodicals

Deep reinforcement learning based mobile robot navigation: A review

Navigation is a fundamental problem of mobile robots, for which Deep Reinforcement Learning (DRL) has received significant attention because of its strong representation and experience learning abilities. There is a growing trend of applying DRL to mobile robot navigation. In this paper, we review DRL methods and DRL-based navigation frameworks. Then we systematically compare and analyze the relationship and differences between four typical application scenarios: local obstacle avoidance, indoor navigation, multi-robot navigation, and social navigation. Next, we describe the development of DRL-based navigation. Last, we discuss the challenges and some possible solutions regarding DRL-based navigation.

Architecture & Planning2021Tsinghua University Press
Periodicals

Multi-Agent Deep Reinforcement Learning-Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.

Architecture & Planning2020Institute of Electrical and Electronics Engineers
Periodicals

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

Architecture & Planning2020Multidisciplinary Digital Publishing Institute
Periodicals

A Comparative Survey of VANET Clustering Techniques

A vehicular ad hoc network (VANET) is a mobile ad hoc network in which network nodes are vehicles-most commonly road vehicles. VANETs present a unique range of challenges and opportunities for routing protocols due to the semi-organized nature of vehicular movements subject to the constraints of road geometry and rules, and the obstacles which limit physical connectivity in urban environments. In particular, the problems of routing protocol reliability and scalability across large urban VANETs are currently the subject of intense research. Clustering can be used to improve routing scalability and reliability in VANETs, as it results in the distributed formation of hierarchical network structures by grouping vehicles together based on correlated spatial distribution and relative velocity. In addition to the benefits to routing, these groups can serve as the foundation for accident or congestion detection, information dissemination and entertainment applications. This paper explores the design choices made in the development of clustering algorithms targeted at VANETs. It presents a taxonomy of the techniques applied to solve the problems of cluster head election, cluster affiliation, and cluster management, and identifies new directions and recent trends in the design of these algorithms. Additionally, methodologies for validating clustering performance are reviewed, and a key shortcoming-the lack of realistic vehicular channel modeling-is identified. The importance of a rigorous and standardized performance evaluation regime utilizing realistic vehicular channel models is demonstrated.

Architecture & Planning2016Institute of Electrical and Electronics Engineers
Periodicals

The Parent's Capacity to Treat the Child as a Psychological Agent: Constructs, Measures and Implications for Developmental Psychopathology

Abstract Recent studies of the relationship between parenting and child development have included a focus on the parent's capacity to treat the child as a psychological agent. Several constructs have been developed to refer to this capacity, for example maternal mind‐mindedness, reflective functioning, and parental mentalizing. In this review article, we compare and contrast different constructs from diverse theoretical backgrounds that have been developed to operationalize parental mentalizing. We examine the empirical evidence to date in support of each of the constructs and review the relevant measures associated with each construct. Next, we discuss the possibility that these apparently diverse constructs may tap into the same underlying neurobiological socio‐cognitive system. We conclude by proposing a testable model for describing the links between parental mentalization, the development of mentalizing in children, and child psychopathology.

Architecture & Planning2008Wiley
Periodicals

From Axial to Road-Centre Lines: A New Representation for Space Syntax and a New Model of Route Choice for Transport Network Analysis

Axial analysis is one of the fundamental components of space syntax. The space syntax community has suggested that it picks up qualities of configurational relationships between spaces not illuminated by other representations. However, critics have questioned the absolute necessity of axial lines to space syntax, as well as the exact definition of axial lines. Why not another representation? In particular, why not road-centre lines, which are easily available in many countries for use within geographical information systems? Here I propose that a recently introduced method of analysis, angular segment analysis, can marry axial and road-centre line representations, and in doing so reflect a cognitive model of how route choice decisions may be made. I show that angular segment analysis can be applied generally to road-centre line segments or axial segments, through a simple length-weighted normalisation procedure that makes values between the two maps comparable. I make comparative quantitative assessments for a real urban system, not just investigating angular analysis between axial and road-centre line networks, but also including more intuitive measures based on metric (or block) distances between locations. I show that the new angular segment analysis algorithm produces better correlation with observed vehicular flow than both standard axial analysis and metric distance measures. The results imply that there is no reason why space syntax inspired measures cannot be combined with transportation network analysis representations in order to create a new, cognitively coherent, model of movement in the city.

Architecture & Planning2007SAGE Publishing
Periodicals

Multiple sclerosis

Multiple sclerosis (MS) is a multifocal demyelinating disease with progressive neurodegeneration caused by an autoimmune response to self-antigens in a genetically susceptible individual. While the formation and persistence of meningeal lymphoid follicles suggest persistence of antigens to drive the continuing inflammatory and humoral response, the identity of an antigen or infectious agent leading to the oligoclonal expansion of B and T cells is unknown. In this review we examine new paradigms for understanding the immunopathology of MS, present recent data defining the common genetic variants underlying disease susceptibility, and explore how improved understanding of immune pathway disruption can inform MS prognosis and treatment decisions.

Architecture & Planning2012American Society for Clinical Investigation
Periodicals

Routing Protocols in Wireless Sensor Networks - A Survey

Advances in wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. Variety of sensing capabilities results in profusion of application areas. However, the characteristics of wireless sensor networks require more effective methods for data forwarding and processing.

Architecture & Planning2010
Periodicals

Tumor Cell Marker PVRL4 (Nectin 4) Is an Epithelial Cell Receptor for Measles Virus

Vaccine and laboratory adapted strains of measles virus can use CD46 as a receptor to infect many human cell lines. However, wild type isolates of measles virus cannot use CD46, and they infect activated lymphocytes, dendritic cells, and macrophages via the receptor CD150/SLAM. Wild type virus can also infect epithelial cells of the respiratory tract through an unidentified receptor. We demonstrate that wild type measles virus infects primary airway epithelial cells grown in fetal calf serum and many adenocarcinoma cell lines of the lung, breast, and colon. Transfection of non-infectable adenocarcinoma cell lines with an expression vector encoding CD150/SLAM rendered them susceptible to measles virus, indicating that they were virus replication competent, but lacked a receptor for virus attachment and entry. Microarray analysis of susceptible versus non-susceptible cell lines was performed, and comparison of membrane protein gene transcripts produced a list of 11 candidate receptors. Of these, only the human tumor cell marker PVRL4 (Nectin 4) rendered cells amenable to measles virus infections. Flow cytometry confirmed that PVRL4 is highly expressed on the surfaces of susceptible lung, breast, and colon adenocarcinoma cell lines. Measles virus preferentially infected adenocarcinoma cell lines from the apical surface, although basolateral infection was observed with reduced kinetics. Confocal immune fluorescence microscopy and surface biotinylation experiments revealed that PVRL4 was expressed on both the apical and basolateral surfaces of these cell lines. Antibodies and siRNA directed against PVRL4 were able to block measles virus infections in MCF7 and NCI-H358 cancer cells. A virus binding assay indicated that PVRL4 was a bona fide receptor that supported virus attachment to the host cell. Several strains of measles virus were also shown to use PVRL4 as a receptor. Measles virus infection reduced PVRL4 surface expression in MCF7 cells, a property that is characteristic of receptor-associated viral infections.

Architecture & Planning2011Public Library of Science
Periodicals

The Evolution of Quantum Key Distribution Networks: On the Road to the Qinternet

Quantum key distribution (QKD) constitutes a symmetric secret key negotiation protocol capable of maintaining information-theoretic security. Given the recent advances in QKD networks, they have evolved from academic research to some preliminary applications. A QKD network consists of two or more QKD nodes interconnected by optical fiber or free space links. The secret keys are negotiated between any pair of QKD nodes, and then they can be delivered to multiple users in various areas for ensuring long-term protection and forward secrecy. We commence by introducing the QKD basics, followed by reviewing the development of QKD networks and their implementation in practice. Subsequently, we describe the general QKD network architecture, its elements, as well as its interfaces and protocols. Next, we provide an in-depth overview of the associated physical layer and network layer solutions, followed by the standardization efforts as well as the application scenarios associated with QKD networks. Finally, we discuss the potential future research directions and provide design guidelines for QKD networks.

Architecture & Planning2022Institute of Electrical and Electronics Engineers
Periodicals

Introduction of Microbial Biopolymers in Soil Treatment for Future Environmentally-Friendly and Sustainable Geotechnical Engineering

Soil treatment and improvement is commonly performed in the field of geotechnical engineering. Methods and materials to achieve this such as soil stabilization and mixing with cementitious binders have been utilized in engineered soil applications since the beginning of human civilization. Demand for environment-friendly and sustainable alternatives is currently rising. Since cement, the most commonly applied and effective soil treatment material, is responsible for heavy greenhouse gas emissions, alternatives such as geosynthetics, chemical polymers, geopolymers, microbial induction, and biopolymers are being actively studied. This study provides an overall review of the recent applications of biopolymers in geotechnical engineering. Biopolymers are microbially induced polymers that are high-tensile, innocuous, and eco-friendly. Soil–biopolymer interactions and related soil strengthening mechanisms are discussed in the context of recent experimental and microscopic studies. In addition, the economic feasibility of biopolymer implementation in the field is analyzed in comparison to ordinary cement, from environmental perspectives. Findings from this study demonstrate that biopolymers have strong potential to replace cement as a soil treatment material within the context of environment-friendly construction and development. Moreover, continuing research is suggested to ensure performance in terms of practical implementation, reliability, and durability of in situ biopolymer applications for geotechnical engineering purposes.

Architecture & Planning2016Multidisciplinary Digital Publishing Institute
Periodicals

The Fast Downward Planning System

Fast Downward is a classical planning system based on heuristic search. It can deal with general deterministic planning problems encoded in the propositional fragment of PDDL2.2, including advanced features like ADL conditions and effects and derived predicates (axioms). Like other well-known planners such as HSP and FF, Fast Downward is a progression planner, searching the space of world states of a planning task in the forward direction. However, unlike other PDDL planning systems, Fast Downward does not use the propositional PDDL representation of a planning task directly. Instead, the input is first translated into an alternative representation called multi-valued planning tasks, which makes many of the implicit constraints of a propositional planning task explicit. Exploiting this alternative representation, Fast Downward uses hierarchical decompositions of planning tasks for computing its heuristic function, called the causal graph heuristic, which is very different from traditional HSP-like heuristics based on ignoring negative interactions of operators. In this article, we give a full account of Fast Downward's approach to solving multi-valued planning tasks. We extend our earlier discussion of the causal graph heuristic to tasks involving axioms and conditional effects and present some novel techniques for search control that are used within Fast Downward's best-first search algorithm: preferred operators transfer the idea of helpful actions from local search to global best-first search, deferred evaluation of heuristic functions mitigates the negative effect of large branching factors on search performance, and multi-heuristic best-first search combines several heuristic evaluation functions within a single search algorithm in an orthogonal way. We also describe efficient data structures for fast state expansion (successor generators and axiom evaluators) and present a new non-heuristic search algorithm called focused iterative-broadening search, which utilizes the information encoded in causal graphs in a novel way. Fast Downward has proven remarkably successful: It won the "classical'' (i.e., propositional, non-optimising) track of the 4th International Planning Competition at ICAPS 2004, following in the footsteps of planners such as FF and LPG. Our experiments show that it also performs very well on the benchmarks of the earlier planning competitions and provide some insights about the usefulness of the new search enhancements.

Architecture & Planning2006AI Access Foundation
Periodicals

Mapping the Economic Costs and Benefits of Conservation

Resources for biodiversity conservation are severely limited, requiring strategic investment. Understanding both the economic benefits and costs of conserving ecosystems will help to allocate scarce dollars most efficiently. However, although cost-benefit analyses are common in many areas of policy, they are not typically used in conservation planning. We conducted a spatial evaluation of the costs and benefits of conservation for a landscape in the Atlantic forests of Paraguay. We considered five ecosystem services (i.e., sustainable bushmeat harvest, sustainable timber harvest, bioprospecting for pharmaceutical products, existence value, and carbon storage in aboveground biomass) and compared them to estimates of the opportunity costs of conservation. We found a high degree of spatial variability in both costs and benefits over this relatively small (approximately 3,000 km(2)) landscape. Benefits exceeded costs in some areas, with carbon storage dominating the ecosystem service values and swamping opportunity costs. Other benefits associated with conservation were more modest and exceeded costs only in protected areas and indigenous reserves. We used this cost-benefit information to show that one potential corridor between two large forest patches had net benefits that were three times greater than two otherwise similar alternatives. Spatial cost-benefit analysis can powerfully inform conservation planning, even though the availability of relevant data may be limited, as was the case in our study area. It can help us understand the synergies between biodiversity conservation and economic development when the two are indeed aligned and to clearly understand the trade-offs when they are not.

Architecture & Planning2006Public Library of Science
Periodicals

Meaningful Human Control over Autonomous Systems: A Philosophical Account

Debates on lethal autonomous weapon systems have proliferated in the past 5 years. Ethical concerns have been voiced about a possible raise in the number of wrongs and crimes in military operations and about the creation of a "responsibility gap" for harms caused by these systems. To address these concerns, the principle of "meaningful human control" has been introduced in the legal-political debate; according to this principle, humans not computers and their algorithms should ultimately remain in control of, and thus morally responsible for, relevant decisions about (lethal) military operations. However, policy-makers and technical designers lack a detailed theory of what "meaningful human control" exactly means. In this paper, we lay the foundation of a philosophical account of meaningful human control, based on the concept of "guidance control" as elaborated in the philosophical debate on free will and moral responsibility. Following the ideals of "Responsible Innovation" and "Value-sensitive Design," our account of meaningful human control is cast in the form of design requirements. We identify two general necessary conditions to be satisfied for an autonomous system to remain under meaningful human control: first, a "tracking" condition, according to which the system should be able to respond to both the relevant moral reasons of the humans designing and deploying the system and the relevant facts in the environment in which the system operates; second, a "tracing" condition, according to which the system should be designed in such a way as to grant the possibility to always trace back the outcome of its operations to at least one human along the chain of design and operation. As we think that meaningful human control can be one of the central notions in ethics of robotics and AI, in the last part of the paper, we start exploring the implications of our account for the design and use of non-military autonomous systems, for instance, self-driving cars.

Architecture & Planning2018Frontiers Media