Search results for 'Human Machine Systems Design' (try it on Scholar)

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  1.  2
    Jon Young (1989). Human-Centred Knowledge Based Systems Design. AI and Society 3 (2):80-87.
    It is held that the quality of the working environment afforded to an individual critically affects the health and well-being of that individual. This has consequences for both the quality of work which that individual can actually perform, and for the quality of the society in which that individual has a place. Conceptions of a fit working environment have led to the idea of a human-centred system, and this idea is applicable to the area of knowledge-based systems (KBS). (...)
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  2. Mike Cooley (1987). Human Centred Systems: An Urgent Problem for Systems Designers. [REVIEW] AI and Society 1 (1):37-46.
    Systems, machines and organisation of forms developed in the Engineering and Manufacturing sectors frequently lay the basis for systems design philosophy at a general level. An analysis of technological change in these sectors reveals that the resultant deskilling is not limited to the shop floor and is now spreading to intellectual work. The impact of ‘machine based systems’ on designers is explored in some detail and suggests the need for alternatives which are based on ‘ (...) centred systems’. Some attempts to design sub-systems are described. (shrink)
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  3.  18
    D. Alexander Varakin, Daniel T. Levin & Roger Fidler (2004). Unseen and Unaware: Implications of Recent Research on Failures of Visual Awareness for Human-Computer Interface Design. Human-Computer Interaction 19 (4):389-422.
  4.  5
    Nils O. Larsson (2000). Decision Settings Analysis €“ a Tool for Analysis and Design of Human Activity Systems. Theory and Decision 49 (4):339-360.
    The paper describes a methodology to be used for analysis and design of human activity systems. The methodology is based on an analysis of the decision settings whereas most other decision analysis methodologies are analysing the process. The decision concept is analysed and discussed. A distinction between programmed and programmable as well as non-programmed and non-programmable decisions is proposed. A classification of different information types for decision making is presented. A methodology based on a systemic and systematic (...)
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  5.  2
    Evans E. Woherem (1991). Human Factors in Information Technology: The Socio-Organisational Aspects of Expert Systems Design. [REVIEW] AI and Society 5 (1):18-33.
    This paper looks beyond the mostly technical and business issues that currently inform the design of knowledge-based systems (e.g., expert systems) to point out that there is also a social and organisational (a socio-organisational) dimension to the issues affecting the design decisions of expert systems and other information technologies. It argues that whilst technical and business issues are considered before the design of Expert Systems, that socio-organisational issues determine the acceptance and long-run utility (...)
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  6.  3
    Takao Nuki (1990). The 'Transfer of Skill' and the 'Transfer of Human Relations' to Machine Systems. AI and Society 4 (3):173-182.
    The necessity and opportunity for face-to-face contact with other colleagues is being increasingly reduced as a result of factory automation (FA) or office automation (OA). This means that human functions which are a result of human contact and relationships are substituted for by the function of machine systems. This “transfer of relations” from the human “system” to the machine system causes isolation of the individual in the process of work. This chapter considers some reasons (...)
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  7.  10
    Lars Qvortrup (1996). Scandinavian Human-Centred Systems Design: Theoretical Reflections and Challenges. [REVIEW] AI and Society 10 (2):164-180.
    Currently there is a clear trend towards questioning the traditional sovereign human self which for two hundred years has had an undisputed central status within European culture and philosophy. This challenges the tradition of anthropocentrism which in a Scandinavian computer science context has had two theoretical foundations: the workoriented design theory represented by the Scandinavian participatory design philosophy, and the idea of the computer to a rather passive medium for human communication. The process, reducing the computer (...)
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  8. Barbara Gorayska & Kevin Cox (1992). Expert Systems as Extensions of the Human Mind: A User Oriented, Holistic Approach to the Design of Multiple Reasoning System Environments and Interfaces. [REVIEW] AI and Society 6 (3):245-262.
    Expert systems have had little impact as computing artifacts. In this paper we argue that the reason for this stems from the underlying assumption of most builders of expert systems that an expert system needs to acquire information and to control the interaction between the human user and itself. We show that this assumption has serious linguistic and usability flaws which diminish the likelihood of producing socially acceptable expert systems. We propose a reversal of this paradigm, (...)
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  9. Brian P. Bailey & Joseph A. Konstan (2006). On the Need for Attention-Aware Systems: Measuring Effects of Interruption on Task Performance, Error Rate, and Affective State. Computers in Human Behavior 22 (4):685-708.
     
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  10.  13
    Tom Froese (2014). Bio-Machine Hybrid Technology: A Theoretical Assessment and Some Suggestions for Improved Future Design. [REVIEW] Philosophy and Technology 27 (4):539-560.
    In sociology, there has been a controversy about whether there is any essential difference between a human being and a tool, or if the tool–user relationship can be defined by co-actor symmetry. This issue becomes more complex when we consider examples of AI and robots, and even more so following progress in the development of various bio-machine hybrid technologies, such as robots that include organic parts, human brain implants, and adaptive prosthetics. It is argued that a concept (...)
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  11.  11
    Peter A. Hancock (2009). Mind, Machine and Morality: Toward a Philosophy of Human-Technology Symbiosis. Ashgate.
    Historically, this work is a modern-day child of Bacon's hope for the 'Great Instauration.' However, unlike its forebear, the focus here is on human-machine systems.
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  12.  18
    Herbert A. Simon & Stuart A. Eisenstadt (1998). Human and Machine Interpretation of Expressions in Formal Systems. Synthese 116 (3):439-461.
    This paper uses a proof of Gödels theorem, implemented on a computer, to explore how a person or a computer can examine such a proof, understand it, and evaluate its validity. It is argued that, in order to recognize it (1) as Gödel's theorem, and (2) as a proof that there is an undecidable statement in the language of PM, a person must possess a suitable semantics. As our analysis reveals no differences between the processes required by people and machines (...)
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  13. Hannah Cornish (2010). Investigating How Cultural Transmission Leads to the Appearance of Design Without a Designer in Human Communication Systems. Interaction Studiesinteraction Studies Social Behaviour and Communication in Biological and Artificial Systems 11 (1):112-137.
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  14.  1
    Felix Rauner, Lauge Rasmussen & J. Martin Corbett (1988). The Social Shaping of Technology and Work: Human Centred CIM Systems. [REVIEW] AI and Society 2 (1):47-61.
    This paper decribes the theoretical and methodological issues involved in the social shaping of technology and work, with particular reference to human centred computer integrated manufacturing (CIM) systems. Conventional approaches to the understanding and shaping of the relationship between technology, work and human development are criticised, and an alternative, human centred approach is outlined. The methods and processes whereby the design of human centred CIM systems may be shaped and evaluated are then described (...)
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  15.  4
    Hannah Cornish (2010). Investigating How Cultural Transmission Leads to the Appearance of Design Without a Designer in Human Communication Systems. Interaction Studies 11 (1):112-137.
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  16. Erich Jantsch (1975). Design for Evolution: Self-Organization and Planning in the Life of Human Systems. G. Braziller.
     
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  17. Nils O. Larsson (2000). Decision Settings Analysis–a Tool for Analysis and Design of Human Activity Systems. Theory and Decision 49 (4):339-360.
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  18. P. A. A. van den Besselaar (1996). From Human Centred Systems Towards Human Centred Infrastructures: From Design Towards Politics. AI and Society 9:220-238.
  19.  50
    Myles Bogner, Uma Ramamurthy & Stan Franklin (2000). Consciousness and Conceptual Learning in a Socially Situated Agent. In Kerstin Dauthenhahn (ed.), Human Cognition and Social Agent Technology. Amsterdam: John Benjamins Publishing Company 113--135.
  20. Peter Kroes, Pieter E. Vermaas, Andrew Light, Steven A. Moore & Sd Noam Cook (2008). Design and Responsibility: The Interdependence of Natural, Artifactual, and Human Systems. In Pieter E. Vermaas, Peter Kroes, Andrew Light & Steven A. Moore (eds.), Philosophy and Design: From Engineering to Architecture. Springer
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  21.  49
    Hiroshi Ishiguro (2006). Android Science: Conscious and Subconscious Recognition. Connection Science 18 (4):319-332.
  22.  9
    Jens Rasmussen (1998). The Concept of Human Error: Is It Useful for the Design of Safe Systems? Philosophy 3 (1).
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  23. Veikko Ikonen, Eija Kaasinen, Päivi Heikkilä & Marketta Niemelä (2015). Human-Driven Design of Micro- and Nanotechnology Based Future Sensor Systems. Journal of Information, Communication and Ethics in Society 13 (2):110-129.
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  24.  9
    William Green & Boris de Ruyter (2010). The Design and Evaluation of Interactive Systems with Perceived Social Intelligence: Five Challenges. [REVIEW] AI and Society 25 (2):203-210.
    This paper reflects on discussions within the Social Intelligence for Tele-healthcare (SIFT) project. The SIFT project aims to establish a model of social intelligence, to support the user-centred design of social intelligence in interactive systems. The conceptual background of social intelligence for the SIFT project is presented. Five challenges identified for the design of socially aware interactions are described, and their implications are discussed.
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  25.  5
    Tom Stonier (1988). Machine Intelligence and the Long-Term Future of the Human Species. AI and Society 2 (2):133-139.
    Intelligence is not a property unique to the human brain; rather it represents a spectrum of phenomena. An understanding of the evolution of intelligence makes it clear that the evolution of machine intelligence has no theoretical limits — unlike the evolution of the human brain. Machine intelligence will outpace human intelligence and very likely will do so during the lifetime of our children. The mix of advanced machine intelligence with human individual and communal (...)
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  26. Karamjit S. Gill (1989). Reflections on Participatory Design. AI and Society 3 (4):297-314.
    The human-centred debate in Britain focuses on the idea of human-machine symbiosis, and the “Dialogue” debate in Scandinavia focuses on the deep understanding of human communication, through a process of inner reflection. Both of these debates provide a framework for the participatory design of AI systems.The emergence of “social Europe” creates the desirability for a sharing of social and cultural knowledge and resources among the citizens of Europe. This raises the possibility of exploiting the (...)
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  27.  15
    Tomasz M. Rutkowski, Andrzej Cichocki, Danilo P. Mandic & Toyoaki Nishida (2011). Emotional Empathy Transition Patterns From Human Brain Responses in Interactive Communication Situations. AI and Society 26 (3):301-315.
    The paper reports our research aiming at utilization of human interactive communication modeling principles in application to a novel interaction paradigm designed for brain–computer/machine-interfacing (BCI/BMI) technologies as well as for socially aware intelligent environments or communication support systems. Automatic procedures for human affective responses or emotional states estimation are still a hot topic of contemporary research. We propose to utilize human brain and bodily physiological responses for affective/emotional as well as communicative interactivity estimation, (...)
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  28. H. H. Rosenbrock (1990). Machines with a Purpose. Oxford University Press.
    There is at present a widespread unease about the direction in which our technology is taking us, apparently against our will. Promising advances seem to carry with them unforeseen negative consequences, including damage to the environment and the reduction of work to the trivial mechanical repetition of actions which have no human meaning. However, attempts to design a better, human-centered technology--one that complements rather than rejects human skills--are all too often frustrated by the prevailing belief that (...)
     
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  29.  3
    Peter Storkerson (2010). Naturalistic Cognition: A Research Paradigm for Human-Centered Design. Journal of Research Practice 6 (2):Article M12.
    Naturalistic thinking and knowing, the tacit, experiential, and intuitive reasoning of everyday interaction, have long been regarded as inferior to formal reason and labeled primitive, fallible, subjective, superstitious, and in some cases ineffable. But, naturalistic thinking is more rational and definable than it appears. It is also relevant to design. Inquiry into the mechanisms of naturalistic thinking and knowledge can bring its resources into focus and enable designers to create better, human-centered designs for use in real-world settings. This (...)
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  30.  1
    J. Martin Corbett (1989). Automate or Innervate? The Role of Knowledge in Advanced Manufacturing Systems. AI and Society 3 (3):198-208.
    This chapter examines the role of shopfloor knowledge in the operation of advanced manufacturing systems. Design trends towards full automation are contrasted with those toward hybrid, human-centred systems with particular emphasis on job design and the development and reproduction of knowledge. The chapter concludes with a short discussion of the problems inherent in hybrid design.
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  31.  4
    Michael Paetau (1991). 'Adaptive' and 'Cooperative' Computer Systems — A Challenge for Sociological Research. AI and Society 5 (1):61-70.
    The vision of the new generation of office systems is based on the hypothesis that an automatic support system is all the more useful and acceptable, the more systems behaviour and performance are in accordance with features ofhuman behaviour. Consequently recent development activities are influenced by the paradigm of the computer as man's “cooperative assistant”. The metaphors ofassistance andcooperation illustrate some major requirements to be met by new office systems. Cooperative office systems will raise (...)
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  32.  16
    Marco C. Bettoni (1995). Kant and the Software Crisis: Suggestions for the Construction of Human-Centred Software Systems. [REVIEW] AI and Society 9 (4):396-401.
    -/- In this article I deal with the question “How could we renew and enrich computer technology with Kant's help?”. By this I would like to invite computer scientists and engineers to initiate or intensify their cooperation with Kant experts. -/- What I am looking for is a better “method of definition” for software systems, particularly for the development of object-oriented and knowledge-based systems. -/- After a description of the “software crisis”, I deal first with the question why (...)
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  33. Barrie Lipscombe (1989). Expert Systems and Computer-Controlled Decision Making in Medicine. AI and Society 3 (3):184-197.
    The search for “usable” expert systems is leading somemedical researchers to question the appropriate role of these programs. Most current systems assume a limited role for the human user, delegating situated “decision-control” to the machine. As expert systems are only able to replace a narrow range of human intellectual functions, this leaves the programs unable to cope with the “constructivist” nature of human knowledge-use. In returning practical control to the human doctor, some (...)
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  34.  1
    Yoshihiro Sato (1995). Requirement Acquisition in System Development: A Human-Centred Perspective of the Tacit Requirements. [REVIEW] AI and Society 9 (2-3):208-217.
    Specification acquisition in the system design process has been improved since the middle of the 1980s when the upper CASE tools appeared. On the contrary the quality of requirement acquisition in the upper processes of system design has not been enhanced as much as specification acquisition. Understanding the user's requirements is indispensable as one of the basic conditions for building systems that can really satisfy users.This article discusses obtaining requirement knowledge, in terms of human-centred design. (...)
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  35.  27
    Markus F. Peschl & Chris Stary (1998). The Role of Cognitive Modeling for User Interface Design Representations: An Epistemological Analysis of Knowledge Engineering in the Context of Human-Computer Interaction. [REVIEW] Minds and Machines 8 (2):203-236.
    In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that (...)
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  36.  23
    Wendell Wallach, Colin Allen & Iva Smit (2007). Machine Morality: Bottom-Up and Top-Down Approaches for Modelling Human Moral Faculties. [REVIEW] AI and Society 22 (4):565-582.
    The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the (...)
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  37.  7
    Peter Brödner (2013). Reflective Design of Technology for Human Needs. AI and Society 28 (1):27-37.
    Inspired by an economic interpretation of the Faustus drama allegorically disclosing the ‘alchemical’ nature of modern economy, the paper presents a critical view on the development of technology as concomitant phenomenon of work practices with particular focus on manufacturing. It starts with a theoretical perspective on the dynamics of creating explicit propositional knowledge and its re-appropriation for practical use. This lays the ground for understanding how technical artefacts emerge from and, in turn, affect social practices. It further helps (...)
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  38.  9
    Alexander Laszlo & Kathia Laszlo (2002). The Evolution of Evolutionary Systems Design. World Futures 58 (5 & 6):351 – 363.
    This article presents the genesis of Evolutionary Systems Design (ESD) as a praxis that draws on General Evolution Theory and Social Systems Design methodology, in addition to Critical Systems Theory, to engage in lifelong learning and human development in partnership with the Earth. The contributions of Bela H. Banathy to the creation of ESD are portrayed as bridging evolutionary consciousness and evolutionary action. Following a brief description of the inspiration and mentorship provided by Bela (...)
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  39.  3
    Alexander Laszlo (1999). Evolutionary Systems Design: A Soft Technology for Hard Challenges. World Futures 54 (4):313-335.
    (1999). Evolutionary systems design: A soft technology for hard challenges. World Futures: Vol. 54, Challenges of Evolution at Pat I: The Human Factor in Evolution, pp. 313-335.
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  40.  2
    Peter J. Carew, Larry Stapleton & Gabriel J. Byrne (2008). Implications of an Ethic of Privacy for Human-Centred Systems Engineering. AI and Society 22 (3):385-403.
    Privacy remains an intractable ethical issue for the information society, and one that is exacerbated by modern applications of artificial intelligence. Given its complicity, there is a moral obligation to redress privacy issues in systems engineering practice itself. This paper investigates the role the concept of privacy plays in contemporary systems engineering practice. Ontologically a nominalist human concept, privacy is considered from an appropriate engineering perspective: human-centred design. Two human-centred design standards are selected (...)
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  41. Maarten Boudry & Massimo Pigliucci (2013). The Mismeasure of Machine: Synthetic Biology and the Trouble with Engineering Metaphors. Studies in History and Philosophy of Biological and Biomedical Sciences (4):660-668.
    The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their func- tionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adapta- tions, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in (...)
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  42.  28
    Doren Recker (2010). How to Confuse Organisms with Mousetraps: Machine Metaphors and Intelligent Design. Zygon 45 (3):647-664.
    Why do design arguments—particularly those emphasizing machine metaphors such as “Organisms and/or their parts are machines”—continue to be so convincing to so many people after they have been repeatedly refuted? In this essay I review various interpretations and refutations of design arguments and make a distinction between rationally refuting such arguments (RefutingR) and rendering them psychologically unconvincing (RefutingP). Expanding on this distinction, I provide support from recent work on the cognitive power of metaphors and developmental psychological work (...)
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  43.  71
    D. King (1996). Is the Human Mind a Turing Machine? Synthese 108 (3):379-89.
    In this paper I discuss the topics of mechanism and algorithmicity. I emphasise that a characterisation of algorithmicity such as the Turing machine is iterative; and I argue that if the human mind can solve problems that no Turing machine can, the mind must depend on some non-iterative principle — in fact, Cantor's second principle of generation, a principle of the actual infinite rather than the potential infinite of Turing machines. But as there has been theorisation that (...)
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  44. Steve Torrance & Ron Chrisley, Modelling Consciousness-Dependent Expertise in Machine Medical Moral Agents.
    It is suggested that some limitations of current designs for medical AI systems (be they autonomous or advisory) stem from the failure of those designs to address issues of artificial (or machine) consciousness. Consciousness would appear to play a key role in the expertise, particularly the moral expertise, of human medical agents, including, for example, autonomous weighting of options in (e.g.,) diagnosis; planning treatment; use of imaginative creativity to generate courses of action; sensorimotor flexibility and sensitivity; empathetic (...)
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  45.  3
    Frank Emspak & Sharon Trimborn (1998). The Nursing Information Systems: Collaborative Design of Healthcare Information Systems. [REVIEW] AI and Society 12 (1-2):64-70.
    This paper will describe a participatory design process by which individuals from many levels of hierarchy and diverse technical background envisioned and then determined the design criteria for the software system to support the delivery of high quality nursing services.
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  46.  29
    John M. Artz (1999). Human Values and the Design of Computer Technology, Edited by Batya Friedman. Ethics and Information Technology 1 (4):305-306.
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  47.  1
    Denis Newman (1991). Interpreting an Intelligent Tutor's Algorithmic Task: A Role for Apprenticeship as a Model for Instructional Design. [REVIEW] AI and Society 5 (2):93-109.
    The interpretive processes required to understand the context and goals of an algorithmic task are illustrated in the use of an intelligent instructional system developed to train soldiers to monitor a computerized missile's system automatic identification of aircraft. The problems students had in understanding the identification task were addressed in INCOFT, a simulation-based intelligent instructional system that depends, in part, on human instructors to convey the task framework. Supported by recent advances in the cognitive science of instruction, the concept (...)
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  48.  31
    Argyris Arnellos, Thomas Spyrou & Ioannis Darzentas (2007). Exploring Creativity in the Design Process: A Systems-Semiotic Perspective. Cybernetics and Human Knowing 14 (1):37-64.
    This paper attempts to establish a systems-semiotic framework explaining creativity in the design process, where the design process is considered to have as its basis the cognitive process. The design process is considered as the interaction between two or more cognitive systems resulting in a purposeful and ongoing transformation of their already complex representational structures and the production of newer ones, in order to fulfill an ill-defined goal. Creativity is considered as the result of an (...)
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  49.  2
    Achille Ardigo (1988). Artificial Intelligence: A Contribution to Systems Theories of Sociology. [REVIEW] AI and Society 2 (2):113-120.
    The aim of my contribution is to try to analyse some points of similarity and difference between post-Parsonian social systems theory models for sociology — with special reference to those of W. Buckley, F.E. Emery and N. Luhmann — and expert systems models1 from Artificial Intelligence. I keep specifically to post-Parsonian systems theories within sociology because they assume some postulates and criteria derived from cybernetics and which are at the roots of AI. I refer in particular to (...)
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  50.  73
    Aaron Sloman (1992). Prolegomena to a Theory of Communication and Affect. In Andrew Ortony, Jon Slack & Oliviero Stock (eds.), Communication from an Artificial Intelligence Perspective: Theoretical and Applied Issues. Springer
    As a step towards comprehensive computer models of communication, and effective human machine dialogue, some of the relationships between communication and affect are explored. An outline theory is presented of the architecture that makes various kinds of affective states possible, or even inevitable, in intelligent agents, along with some of the implications of this theory for various communicative processes. The model implies that human beings typically have many different, hierarchically organized, dispositions capable of interacting with new information (...)
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