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  1. Artificial intelligence: an empirical science.Herbert A. Simon - 1995 - Artificial Intelligence 77 (1):95-127.
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  • A glimpse at the metaphysics of Bongard problems.Alexandre Linhares - 2000 - Artificial Intelligence 121 (1-2):251-270.
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  • Response to my critics.Hubert L. Dreyfus - 1996 - Artificial Intelligence 80 (1):171-191.
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  • (1 other version)Against the iDoctor: why artificial intelligence should not replace physician judgment.Kyle E. Karches - 2018 - Theoretical Medicine and Bioethics 39 (2):91-110.
    Experts in medical informatics have argued for the incorporation of ever more machine-learning algorithms into medical care. As artificial intelligence research advances, such technologies raise the possibility of an “iDoctor,” a machine theoretically capable of replacing the judgment of primary care physicians. In this article, I draw on Martin Heidegger’s critique of technology to show how an algorithmic approach to medicine distorts the physician–patient relationship. Among other problems, AI cannot adapt guidelines according to the individual patient’s needs. In response to (...)
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  • (1 other version)A Multilevel, Interdisciplinary Approach to Phenomenal Consciousness.Robert G. Burton - 2010 - Southern Journal of Philosophy 43 (4):531-543.
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  • Embodied artificial intelligence.Ron Chrisley - 2003 - Artificial Intelligence 149 (1):131-150.
    Mike Anderson1 has given us a thoughtful and useful field guide: Not in the genre of a bird-watcher’s guide which is carried in the field and which contains detailed descriptions of possible sightings, but in the sense of a guide to a field (in this case embodied cognition) which aims to identify that field’s general principles and properties. I’d like to make some comments that will hopefully complement Anderson’s work, highlighting points of agreement and disagreement between his view of the (...)
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  • The epigenesis of meaning in human beings, and possibly in robots.Jordan Zlatev - 2001 - Minds and Machines 11 (2):155-195.
    This article addresses a classical question: Can a machine use language meaningfully and if so, how can this be achieved? The first part of the paper is mainly philosophical. Since meaning implies intentionality on the part of the language user, artificial systems which obviously lack intentionality will be `meaningless'. There is, however, no good reason to assume that intentionality is an exclusively biological property and thus a robot with bodily structures, interaction patterns and development similar to those of human beings (...)
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  • Robert G. Burton, ed., natural and artificial minds, SUNY series, scientific studies in natural and artificial intelligence, albany: State university of new York press, 1993, VII + 245 pp., $21.95 (paper), ISBN 0-7914-1508-. [REVIEW]Stan Franklin - 1999 - Minds and Machines 9 (1):143-156.
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  • The roles of philosophy in cognitive science.Tim Van Gelder - 1998 - Philosophical Psychology 11 (2):117-36.
    When the various disciplines participating in cognitive science are listed, philosophy almost always gets a guernsey. Yet, a couple of years ago at the conference of the Cognitive Science Society in Boulder (USA), there was no philosophy or philosopher with any prominence on the program. When queried on this point, the organizer (one of the "superstars" of the field) claimed it was partly an accident, but partly also due to an impression among members of the committee that philosophy is basically (...)
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  • Inside Doubt: On the Non-Identity of the Theory of Mind and Propositional Attitude Psychology. [REVIEW]David Landy - 2005 - Minds and Machines 15 (3-4):399-414.
    Eliminative materialism is a popular view of the mind which holds that propositional attitudes, the typical units of our traditional understanding, are unsupported by modern connectionist psychology and neuroscience, and consequently that propositional attitudes are a poor scientific postulate, and do not exist. Since our traditional folk psychology employs propositional attitudes, the usual argument runs, it too represents a poor theory, and may in the future be replaced by a more successful neurologically grounded theory, resulting in a drastic improvement in (...)
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  • Because mere calculating isn't thinking: Comments on Hauser's Why Isn't My Pocket Calculator a Thinking Thing?.William J. Rapaport - 1993 - Minds and Machines 3 (1):11-20.
    Hauser argues that his pocket calculator (Cal) has certain arithmetical abilities: it seems Cal calculates. That calculating is thinking seems equally untendentious. Yet these two claims together provide premises for a seemingly valid syllogism whose conclusion - Cal thinks - most would deny. He considers several ways to avoid this conclusion, and finds them mostly wanting. Either we ourselves can't be said to think or calculate if our calculation-like performances are judged by the standards proposed to rule out Cal; or (...)
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  • A metaphysical approach to the mind.Susan Stuart - 2003 - Phenomenology and the Cognitive Sciences 2 (3):223-37.
    It is argued that, based on Kant's descriptive metaphysics, one can prescribe the necessary metaphysical underpinnings for the possibility of conscious experience in an artificial system. This project is developed by giving an account of the a priori concepts of the understanding in such a system. A specification and implementation of the nomological conditions for a conscious system allows one to know a priori that any system possessing this structure will be conscious; thus enabling us to avoid possible false-indicators of (...)
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  • (1 other version)A multilevel, interdisciplinary approach to phenomenal consciousness.Robert G. Burton - 2005 - Southern Journal of Philosophy 43 (4):531-543.
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  • DENDRAL: A case study of the first expert system for scientific hypothesis formation.Robert K. Lindsay, Bruce G. Buchanan, Edward A. Feigenbaum & Joshua Lederberg - 1993 - Artificial Intelligence 61 (2):209-261.
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  • A Feminist Critique of Artificial Intelligence.Alison Adam - 1995 - European Journal of Women's Studies 2 (3):355-377.
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  • Epistemological approach to the process of practice.Richard Dazeley & Beyong Ho Kang - 2008 - Minds and Machines 18 (4):547-567.
    Systems based on symbolic knowledge have performed extremely well in processing reason, yet, remain beset with problems of brittleness in many domains. Connectionist approaches do similarly well in emulating interactive domains, however, have struggled when modelling higher brain functions. Neither of these dichotomous approaches, however, have provided many inroads into the area of human reasoning that psychology and sociology refer to as the process of practice. This paper argues that the absence of a model for the process of practise in (...)
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  • Holism, conceptual-role semantics, and syntactic semantics.William J. Rapaport - 2002 - Minds and Machines 12 (1):3-59.
    This essay continues my investigation of `syntactic semantics': the theory that, pace Searle's Chinese-Room Argument, syntax does suffice for semantics (in particular, for the semantics needed for a computational cognitive theory of natural-language understanding). Here, I argue that syntactic semantics (which is internal and first-person) is what has been called a conceptual-role semantics: The meaning of any expression is the role that it plays in the complete system of expressions. Such a `narrow', conceptual-role semantics is the appropriate sort of semantics (...)
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  • DENDRAL and Meta-DENDRAL: roots of knowledge systems and expert system applications.Edward A. Feigenbaum & Bruce G. Buchanan - 1993 - Artificial Intelligence 59 (1-2):233-240.
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  • Logic and artificial intelligence.Nils J. Nilsson - 1991 - Artificial Intelligence 47 (1-3):31-56.
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  • An architecture for adaptive intelligent systems.Barbara Hayes-Roth - 1995 - Artificial Intelligence 72 (1-2):329-365.
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  • Embedded or embodied? a review of Hubert Dreyfus' What Computers Still Can't Do.H. M. Collins - 1996 - Artificial Intelligence 80 (1):99-117.
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  • Modelling learning as modelling.Scott Moss & Bruce Edmonds - unknown
    Economists tend to represent learning as a procedure for estimating the parameters of the "correct" econometric model. We extend this approach by assuming that agents specify as well as estimate models. Learning thus takes the form of a dynamic process of developing models using an internal language of representation where expectations are formed by forecasting with the best current model. This introduces a distinction between the form and content of the internal models which is particularly relevant for boundedly rational agents. (...)
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  • Foundations of AI: The big issues.David Kirsh - 1991 - Artificial Intelligence 47 (1-3):3-30.
    The objective of research in the foundations of Al is to explore such basic questions as: What is a theory in Al? What are the most abstract assumptions underlying the competing visions of intelligence? What are the basic arguments for and against each assumption? In this essay I discuss five foundational issues: (1) Core Al is the study of conceptualization and should begin with knowledge level theories. (2) Cognition can be studied as a disembodied process without solving the symbol grounding (...)
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  • Building large knowledge-based systems: Representation and inference in the cyc project.Drew McDermott - 1993 - Artificial Intelligence 61 (1):53-63.
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  • Creativity refined: Bypassing the gatekeepers of appropriateness and value.Alan Dorin & Kevin Korb - unknown
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  • The creative mind: Myths and mechanisms: six reviews and a response.Mark Stefik & Stephen Smoliar - 1995 - Artificial Intelligence 79 (1):65-67.
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  • Controlling cooperative problem solving in industrial multi-agent systems using joint intentions.N. R. Jennings - 1995 - Artificial Intelligence 75 (2):195-240.
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  • Using relations within conceptual systems to translate across conceptual systems.R. Goldstone - 2002 - Cognition 84 (3):295-320.
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  • A comparison of different cognitive paradigms using simple animats in a virtual laboratory, with implications to the notion of cognition.Carlos Gershenson - 2002
    In this thesis I present a virtual laboratory which implements five different models for controlling animats: a rule-based system, a behaviour-based system, a concept-based system, a neural network, and a Braitenberg architecture. Through different experiments, I compare the performance of the models and conclude that there is no best model, since different models are better for different things in different contexts. The models I chose, although quite simple, represent different approaches for studying cognition. Using the results as an empirical philosophical (...)
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