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  1. Pascal Acot, Sandrine Charles & Marie-Laure Delignette-Muller (2000). Artificial Intelligence and Meaning — Some Philosophical Aspects of Decision-Making. Acta Biotheoretica 48 (3-4).
  2. Irma Alm (1994). Simulating Human Cognition: A Ghost Story. [REVIEW] AI and Society 8 (1):78-84.
    The intentions to simulate human cognition are permanently increasing. Nonetheless, our knowledge about human cognition is based on fragments of different points of view. Hence, it is necessary to examine which demands these points of view make on technologies aiming at simulating human cognition. In this paper it is argued that no technology can function beyond the cognitive abilities of its constructor. It seems that the cognitive limits and constrains of the constructor will also be implanted in the technologies. It (...)
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  3. Bernard J. Baars, Uma Ramamurthy & Stan Franklin (2007). How Deliberate, Spontaneous, and Unwanted Memories Emerge in a Computational Model of Consciousness. In John H. Mace (ed.), Involuntary Memory. New Perspectives in Cognitive Psychology. Blackwell Publishing. 177-207.
  4. Istvan S. Berkeley (2008). What the <0.70, 1.17, 0.99, 1.07> is a Symbol? Minds and Machines 18 (1):93-105.
    The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...)
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  5. Selmer Bringsjord & David A. Ferrucci (1998). Logic and Artificial Intelligence: Divorced, Still Married, Separated ...? [REVIEW] Minds and Machines 8 (2):273-308.
    Though it''s difficult to agree on the exact date of their union, logic and artificial intelligence (AI) were married by the late 1950s, and, at least during their honeymoon, were happily united. What connubial permutation do logic and AI find themselves in now? Are they still (happily) married? Are they divorced? Or are they only separated, both still keeping alive the promise of a future in which the old magic is rekindled? This paper is an attempt to answer these questions (...)
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  6. Bruce G. Buchanan (1988). AI as an Experimental Science. In James H. Fetzer (ed.), Aspects of AI. Kluwer.
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  7. A. Bundy (1990). What Kind of Field is AI? In Derek Partridge & Y. Wilks (eds.), The Foundations of Artificial Intelligence: A Sourcebook. Cambridge University Press.
  8. Leslie Burkholder (ed.) (1992). Philosophy and the Computer. Westview Press.
  9. David J. Chalmers, Robert M. French & Douglas R. Hofstadter (1992). High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology. Journal of Experimental and Theoretical Artificial Intellige 4 (3):185 - 211.
    High-level perception--”the process of making sense of complex data at an abstract, conceptual level--”is fundamental to human cognition. Through high-level perception, chaotic environmen- tal stimuli are organized into the mental representations that are used throughout cognitive pro- cessing. Much work in traditional artificial intelligence has ignored the process of high-level perception, by starting with hand-coded representations. In this paper, we argue that this dis- missal of perceptual processes leads to distorted models of human cognition. We examine some existing artificial-intelligence models--”notably (...)
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  10. Jon Cogburn & Jason Megill (2010). Are Turing Machines Platonists? Inferentialism and the Computational Theory of Mind. Minds and Machines 20 (3):423-439.
    We first discuss Michael Dummett’s philosophy of mathematics and Robert Brandom’s philosophy of language to demonstrate that inferentialism entails the falsity of Church’s Thesis and, as a consequence, the Computational Theory of Mind. This amounts to an entirely novel critique of mechanism in the philosophy of mind, one we show to have tremendous advantages over the traditional Lucas-Penrose argument.
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  11. David Cole (2012). Richard Menary (Ed): The Extended Mind. [REVIEW] Minds and Machines 22 (1):47-51.
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  12. David Cole (2010). Anthony Chemero: Radical Embodied Cognitive Science. [REVIEW] Minds and Machines 20 (3):475-479.
  13. Roberto Cordeschi (2007). AI Turns Fifty: Revisiting its Origins. Applied Artificial Intelligence 21:259-279.
    The expression ‘‘artificial intelligence’’ (AI) was introduced by John McCarthy, and the official birth of AI is unanimously considered to be the 1956 Dartmouth Conference. Thus, AI turned fifty in 2006. How did AI begin? Several differently motivated analyses have been proposed as to its origins. In this paper a brief look at those that might be considered steps towards Dartmouth is attempted, with the aim of showing how a number of research topics and controversies that marked the short history (...)
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  14. Roberto Cordeschi (2006). Searching in a Maze, in Search of Knowledge: Issues in Early Artificial Intelligence. In Lecture Notes In Computer Science, vol. 4155. Springer. 1-23.
    Heuristic programming was the first area in which AI methods were tested. The favourite case-studies were fairly simple toy- problems, such as cryptarithmetic, games, such as checker or chess, and formal problems, such as logic or geometry theorem-proving. These problems are well-defined, roughly speaking, at least in comparison to real-life problems, and as such have played the role of Drosophila in early AI. In this chapter I will investigate the origins of heuristic programming and the shift to more knowledge-based and (...)
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  15. Roberto Cordeschi (2002). The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics. Kluwer.
    The book provides a valuable text for undergraduate and graduate courses on the historical and theoretical issues of Cognitive Science, Artificial Intelligence, Psychology, Neuroscience, and the Philosophy of Mind. The book should also be of interest for researchers in these fields, who will find in it analyses of certain crucial issues in both the earlier and more recent history of their disciplines, as well as interesting overall insights into the current debate on the nature of mind.
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  16. Roberto Cordeschi (1992). A Few Words on Representation and Meaning. Comments on H.A. Simon's Paper on Scientific Discovery. International Studies in the Philosophy of Science 6 (1):19 – 21.
    My aim here is to raise a few questions concerning the problem of representation in scientific discovery computer programs. Representation, as Simon says in his paper, "imposes constraints upon the phenomena that allow the mechanisms to be inferred from the data". The issue is obviously barely outlined by Simon in his paper, while it is addressed in detail in the book by Langley, Simon, Bradshaw and Zytkow (1987), to which I shall refer in this note. Nevertheless, their analysis would appear (...)
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  17. Roberto Cordeschi & Marcello Frixione (2007). Computationalism Under Attack. In M. Marraffa, M. De Caro & F. Ferretti (eds.), Cartographies of the Mind: Philosophy and Psychology in Intersection. Springer.
    Since the early eighties, computationalism in the study of the mind has been “under attack” by several critics of the so-called “classic” or “symbolic” approaches in AI and cognitive science. Computationalism was generically identified with such approaches. For example, it was identified with both Allen Newell and Herbert Simon’s Physical Symbol System Hypothesis and Jerry Fodor’s theory of Language of Thought, usually without taking into account the fact ,that such approaches are very different as to their methods and aims. Zenon (...)
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  18. Craig DeLancey (2001). Passionate Engines: What Emotions Reveal About the Mind and Artificial Intelligence. Oxford University Press.
    The emotions have been one of the most fertile areas of study in psychology, neuroscience, and other cognitive disciplines. Yet as influential as the work in those fields is, it has not yet made its way to the desks of philosophers who study the nature of mind. Passionate Engines unites the two for the first time, providing both a survey of what emotions can tell us about the mind, and an argument for how work in the cognitive disciplines can help (...)
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  19. Daniel C. Dennett (1978). AI as Philosophy and as Psychology. In Martin Ringle (ed.), Philosophical Perspectives on Artificial Intelligence. Humanities Press.
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  20. Hubert L. Dreyfus (2012). A History of First Step Fallacies. Minds and Machines 22 (2):87-99.
    In the 1960s, without realizing it, AI researchers were hard at work finding the features, rules, and representations needed for turning rationalist philosophy into a research program, and by so doing AI researchers condemned their enterprise to failure. About the same time, a logician, Yehoshua Bar-Hillel, pointed out that AI optimism was based on what he called the “first step fallacy”. First step thinking has the idea of a successful last step built in. Limited early success, however, is not a (...)
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  21. Federico Faroldi (2011). Don Ross Et Al. (Eds.), Distributed Cognition and the Will. Minds and Machines 21 (1):115-118.
  22. James H. Fetzer (1989). Language and Mentality: Computational, Representational, and Dispositional Conceptions. Behaviorism 17:21-39.
  23. James H. Fetzer (ed.) (1988). Aspects of AI. D.
  24. Luciano Floridi (2008). Artificial Intelligence's New Frontier: Artificial Companions and the Fourth Revolution. Metaphilosophy 39 (4-5):651-655.
    Abstract: In this article I argue that the best way to understand the information turn is in terms of a fourth revolution in the long process of reassessing humanity's fundamental nature and role in the universe. We are not immobile, at the centre of the universe (Copernicus); we are not unnaturally distinct and different from the rest of the animal world (Darwin); and we are far from being entirely transparent to ourselves (Freud). We are now slowly accepting the idea that (...)
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  25. Joseph S. Fulda (2012). Implications of a Logical Paradox for Computer-Dispensed Justice Reconsidered: Some Key Differences Between Minds and Machines. [REVIEW] Artificial Intelligence and Law 20 (3):321-333.
    We argued [Since this argument appeared in other journals, I am reprising it here, almost verbatim.] (Fulda in J Law Info Sci 2:230–232, 1991/AI & Soc 8(4):357–359, 1994) that the paradox of the preface suggests a reason why machines cannot, will not, and should not be allowed to judge criminal cases. The argument merely shows that they cannot now and will not soon or easily be so allowed. The author, in fact, now believes that when—and only when—they are ready they (...)
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  26. C. Glymour (1988). AI is Philosophy. In James H. Fetzer (ed.), Aspects of AI. D.
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  27. Michael Harré & Allan Snyder (2012). Intuitive Expertise and Perceptual Templates. Minds and Machines 22 (3):167-182.
    We provide the first demonstration of an artificial neural network encoding the perceptual templates that form an important component of the high level strategic understanding developed by experts. Experts have a highly refined sense of knowing where to look, what information is important and what information to ignore. The conclusions these experts reach are of a higher quality and typically made in a shorter amount of time than those of non-experts. Understanding the manifestation of such abilities in terms of both (...)
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  28. Rom Harre (1990). Vigotsky and Artificial Intelligence: What Could Cognitive Psychology Possibly Be About? Midwest Studies in Philosophy 15 (1):389-399.
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  29. Rom Harre (1988). Wittgenstein and Artificial Intelligence. Philosophical Psychology 1 (1):105 – 115.
    Recent studies of Wittgenstein's later writing have made clear that they stand as a defence of two main ideas: that scepticism about the possibility of interpersonal discussions about our subjective feelings is misplaced and, as a seemingly startling corollary; that a mind state account of most 'mental activities' is incoherent. This leads to a great emphasis on skills and practices which, a fortiori, are definable only relationally, by reference to targets. In this paper I try to show that the 'computer' (...)
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  30. David H. Helman (1987). Realism and Antirealism in Artificial Intelligence. British Journal for the Philosophy of Science 38 (1):19-26.
    In the philosophy of mind, the controversy between realists and antirealists often concerns the logical form of sentences embedded in attitude reports. Antirealists believe that such sentences refer to psychological states; realists believe that they refer to situations or states of the world. In this essay, it is shown how these two modes of semantic representation are associated with different approaches to the computational modeling of cognitive processes. I put forward a normative account of methodology in artificial intelligence that reconciles (...)
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  31. André Kukla (1994). Medium AI and Experimental Science. Philosophical Psychology 7 (4):493-5012.
    It has been claimed that a great deal of AI research is an attempt to discover the empirical laws describing a new type of entity in the world—the artificial computing system. I call this enterprise 'medium AI', since it is in some respects stronger than Searle's 'weak AI', and in other respects weaker than 'strong AI'. Bruce Buchanan, among others, conceives of medium AI as an empirical science entirely on a par with psychology or chemistry. I argue that medium AI (...)
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  32. André Kukla (1989). Is AI an Empirical Science? Analysis 49 (March):56-60.
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  33. Nicholas Kushmerick (1997). Software Agents and Their Bodies. Minds and Machines 7 (2):227-247.
    Within artificial intelligence and the philosophy of mind,there is considerable disagreement over the relationship between anagent's body and its capacity for intelligent behavior. Some treatthe body as peripheral and tangential to intelligence; others arguethat embodiment and intelligence are inextricably linked. Softwareagents–-computer programs that interact with software environmentssuch as the Internet–-provide an ideal context in which to studythis tension. I develop a computational framework for analyzingembodiment. The framework generalizes the notion of a body beyondmerely having a physical presence. My analysis sheds (...)
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  34. Shane Legg & Marcus Hutter (2007). Universal Intelligence: A Definition of Machine Intelligence. [REVIEW] Minds and Machines 17 (4):391-444.
    A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: we take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. (...)
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  35. Klaus Mainzer (2005). The Embodied Mind: On Computational, Evolutionary, and Philosophical Interpretations of Cognition. Synthesis Philosophica 2 (40):389-406.
  36. John McCarthy, What is Artificial Intelligence?
  37. Marvin L. Minsky, From Pain to Suffering.
    “Great pain urges all animals, and has urged them during endless generations, to make the most violent and diversified efforts to escape from the cause of suffering. Even when a limb or other separate part of the body is hurt, we often see a tendency to shake it, as if to shake off the cause, though this may obviously be impossible.” —Charles Darwin[1].
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  38. E. T. Mueller (1990). Daydreaming in Humans and Machines: A Computer Model of the Stream of Thought. Ablex.
    Chapter Introduction The field of artificial intelligence is concerned with the construction of computer systems which exhibit intelligent behavior in order ...
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  39. H. Nakashima (1999). AI as Complex Information Processing. Minds and Machines 9 (1):57-80.
    In this article, I present a software architecture for intelligent agents. The essence of AI is complex information processing. It is impossible, in principle, to process complex information as a whole. We need some partial processing strategy that is still somehow connected to the whole. We also need flexible processing that can adapt to changes in the environment. One of the candidates for both of these is situated reasoning, which makes use of the fact that an agent is in a (...)
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  40. María G. Navarro (2011). Collective Challenges for the Realisation of a Collective Intelligence. Analytic Teaching and Philosophical Praxis 32 (1):40-47.
    Understanding Information and Communication Technologies through the networks in which people get con¬nected, communicate and co-operate has been a constant feature in the work of researchers who have not dissociated their view of the meaning of technologies from new social movements. This paper maintains that Information and Communication Technologies are not only networks that people join individually, but they also act as social technologies. Their improvement depends both on the diversity of their functions (social, political, cognitive, etc.) and the flexibility (...)
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  41. Neil Nilsson (1991). Logic and Artificial Intelligence. Artificial Intelligence 47:31-56.
  42. Derek Partridge & Y. Wilks (eds.) (1990). The Foundations of Artificial Intelligence: A Sourcebook. Cambridge University Press.
    This outstanding collection is designed to address the fundamental issues and principles underlying the task of Artificial Intelligence.
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  43. Donald Perlis (2010). Bica and Beyond: How Biology and Anomalies Together Contribute to Flexible Cognition. International Journal of Machine Consciousness 2 (02):261-271.
  44. Hilary Putnam (1987). Computational Psychology and Interpretation Theory. In Artificial Intelligence. St Martin's Press.
  45. William J. Rapaport (1991). Predication, Fiction, and Artificial Intelligence. Topoi 10 (1):79-111.
    This paper describes the SNePS knowledge-representation and reasoning system. SNePS is an intensional, propositional, semantic-network processing system used for research in AI. We look at how predication is represented in such a system when it is used for cognitive modeling and natural-language understanding and generation. In particular, we discuss issues in the representation of fictional entities and the representation of propositions from <span class='Hi'>fiction</span>, using SNePS. We briefly survey four philosophical ontological theories of <span class='Hi'>fiction</span> and sketch an epistemological theory (...)
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  46. Martin Ringle (ed.) (1979). Philosophical Perspectives in Artificial Intelligence. Humanities Press.
  47. D. J. Saab & U. V. Riss (eds.) (2010). Logic and Abstraction as Capabilities of the Mind: Reconceptualizations of Computational Approaches to the Mind. IGI.
    In this chapter we will investigate the nature of abstraction in detail, its entwinement with logical thinking, and the general role it plays for the mind. We find that non-logical capabilities are not only important for input processing, but also for output processing. Human beings jointly use analytic and embodied capacities for thinking and acting, where analytic thinking mirrors reflection and logic, and where abstraction is the form in which embodied thinking is revealed to us. We will follow the philosophical (...)
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  48. Matthias Scheutz (ed.) (2002). Computationalism: New Directions. MIT Press.
    A new computationalist view of the mind that takes into account real-world issues of embodiment, interaction, physical implementation, and semantics.
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  49. Aaron Sloman (2002). The Irrelevance of Turing Machines to AI. In Matthias Scheutz (ed.), Computationalism: New Directions. MIT Press.
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  50. Lawrence B. Solum (1992). Legal Personhood for Artificial Intelligences. North Carolina Law Review 70:1231.
    Could an artificial intelligence become a legal person? As of today, this question is only theoretical. No existing computer program currently possesses the sort of capacities that would justify serious judicial inquiry into the question of legal personhood. The question is nonetheless of some interest. Cognitive science begins with the assumption that the nature of human intelligence is computational, and therefore, that the human mind can, in principle, be modelled as a program that runs on a computer. Artificial intelligence (AI) (...)
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