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  1. Nicholas Agar (2012). On the Irrationality of Mind-Uploading: A Rely to Neil Levy. [REVIEW] AI and Society 27 (4):431-436.
    In a paper in this journal, Neil Levy challenges Nicholas Agar’s argument for the irrationality of mind-uploading. Mind-uploading is a futuristic process that involves scanning brains and recording relevant information which is then transferred into a computer. Its advocates suppose that mind-uploading transfers both human minds and identities from biological brains into computers. According to Agar’s original argument, mind-uploading is prudentially irrational. Success relies on the soundness of the program of Strong AI—the view that it may someday be possible to (...)
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  2. Varol Akman (2000). Introduction to the Special Issue on Philosophical Foundations of Artificial Intelligence. Journal of Experimental and Theoretical Artificial Intelligence 12 (3):247-250.
    This is the guest editor's introduction to a JETAI special issue on philosophical foundations of AI.
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  3. Mario Alai (2004). A.I., Scientific Discovery and Realism. Minds and Machines 14 (1):21-42.
    Epistemologists have debated at length whether scientific discovery is a rational and logical process. If it is, according to the Artificial Intelligence hypothesis, it should be possible to write computer programs able to discover laws or theories; and if such programs were written, this would definitely prove the existence of a logic of discovery. Attempts in this direction, however, have been unsuccessful: the programs written by Simon's group, indeed, infer famous laws of physics and chemistry; but having found no new (...)
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  4. Ronald J. Allen (2001). Artificial Intelligence and the Evidentiary Process: The Challenges of Formalism and Computation. [REVIEW] Artificial Intelligence and Law 9 (2-3):99-114.
    The tension between rule and judgment is well known with respect to the meaning of substantive legal commands. The same conflict is present in fact finding. The law penetrates to virtually all aspects of human affairs; irtually any interaction can generate a legal conflict. Accurate fact finding about such disputes is a necessary condition for the appropriate application of substantive legal commands. Without accuracy in fact finding, the law is unpredictable, and thus individuals cannot efficiently accommodate their affairs to its (...)
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  5. Richard Alterman (2000). Rethinking Autonomy. Minds and Machines 10 (1):15-30.
    This paper explores the assumption of autonomy. Several arguments are presented against the assumption of runtime autonomy as a principle of design for artificial intelligence systems. The arguments vary from being theoretical, to practical, and to analytic. The latter parts of the paper focus on one strategy for building non-autonomous systems (the practice view). One critical theme is that intelligence is not located in the system alone, it emerges from a history of interactions among user, builder, and designer over a (...)
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  6. Michael Anderson, Projects.
    Description: The massive redeployment hypothesis (MRH) is a theory about the functional organization of the human cortex, offering a middle course between strict localization on the one hand, and holism on the other. Central to MRH is the claim that cognitive evolution proceeded in a way analogous to component reuse in software engineering, whereby existing components—originally developed to serve some specific purpose—were used for new purposes and combined to support new capacities, without disrupting their participation in existing programs.
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  7. Michael Anderson, Reviews. [REVIEW]
    Embodied cognition (EC) is growing up, and How the Body Shapes the Mind is both a sign of, and substantive contributor to, this ongoing development. Born in or about 1991 (the year of publication of seminal works by Brooks, Dreyfus, and Varela, Thompson & Rosch), EC is only now emerging from a tumultuous but exciting childhood marked in particular by the size and breadth of the extended family hoping to have some impact on its early education and upbringing. As family (...)
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  8. Michael Anderson, The Metacognitive Loop I: Enhancing Reinforcement Learning with Metacognitive Monitoring and Control for Improved Perturbation Tolerance||.
    Maintaining adequate performance in dynamic and uncertain settings has been a perennial stumbling block for intelligent systems. Nevertheless, any system intended for real-world deployment must be able to accommodate unexpected change—that is, it must be perturbation tolerant. We have found that metacognitive monitoring and control—the ability of a system to self-monitor its own decision-making processes and ongoing performance, and to make targeted changes to its beliefs and action-determining components—can play an important role in helping intelligent systems cope with the perturbations (...)
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  9. Marcus Anthony (2008). The Case for Integrated Intelligence. World Futures 64 (4):233 – 253.
    In this article I develop a case for a theory of intelligence incorporating transpersonal dimensions, namely integrated intelligence. Some recent expanded theories of intelligence move into concepts like creativity, wisdom, and emotional intelligence. Yet they remain embedded within mainstream intelligence theory and its reductionist and materialist presuppositions. Although various theorists in consciousness theory have developed transpersonal models that are beginning to be discussed in some mainstream circles, mainstream intelligence theory is yet to address the broader implications of this. Recent changes (...)
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  10. Michael J. Apter (1970). The Computer Simulation Of Behaviour. Hutchinson.
  11. Kevin D. Ashley (1992). Case-Based Reasoning and its Implications for Legal Expert Systems. Artificial Intelligence and Law 1 (2-3):113-208.
    Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for these components.In (...)
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  12. Denis L. Baggi (2000). The Intelligence Left in AI. AI and Society 14 (3-4):348-378.
    In its forty years of existence, Artificial Intelligence has suffered both from the exaggerated claims of those who saw it as the definitive solution of an ancestral dream — that of constructing an intelligent machine-and from its detractors, who described it as the latest fad worthy of quacks. Yet AI is still alive, well and blossoming, and has left a legacy of tools and applications almost unequalled by any other field-probably because, as the heir of Renaissance thought, it represents a (...)
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  13. Dana Ballard (1991). Animate Vision. Artificial Intelligence 48:57-86.
  14. Ian G. Barbour (1999). Neuroscience, Artificial Intelligence, and Human Nature: Theological and Philosophical Reflections. In Neuroscience and the Person: Scientific Perspectives on Divine Action. Notre Dame: University Notre Dame Press. 361-398.
  15. Ian G. Barbour (1999). Neuroscience and the Person: Scientific Perspectives on Divine Action. Notre Dame: University Notre Dame Press.
  16. Eric B. Baum (2004). What Is Thought? Cambridge MA: Bradford Book/MIT Press.
    In What Is Thought? Eric Baum proposes a computational explanation of thought.
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  17. Anthony F. Beavers (2002). Phenomenology and Artificial Intelligence. Metaphilosophy 33 (1-2):70-82.
    In CyberPhilosophy: The Intersection of Philosophy and Computing, edited by James H. Moor and Terrell Ward Bynum (Oxford, UK: Blackwell, 2002), 66-77. Also in Metaphilosophy 33.1/2 (2002): 70-82.
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  18. John Beloff (1989). The Rhine Legacy. Philosophical Psychology 2 (2):231-239.
    Abstract An attempt is made to examine the main principles that underlay the ?Rhinean? school of parapsychology. Five such principles are discussed: (1) that psi can best be assessed using quantitative measures and forced?choice tests; (2) that psi is a function of the unconscious with the implication that objective performance alone is important, not the state of mind of the subject; (3) that psi ability is, to some degree, present in everyone; (4) that only those problems deserve attention for which (...)
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  19. T. Bench-Capon (1995). Book Review. [REVIEW] Artificial Intelligence and Law 3 (3):273-274.
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  20. Trevor Bench-Capon (1997). Argument in Artificial Intelligence and Law. Artificial Intelligence and Law 5 (4):249-261.
    In this paper I shall discuss the notion of argument, and the importanceof argument in AI and Law. I shall distinguish four areas where argument hasbeen applied: in modelling legal reasoning based on cases; in thepresentation and explanation of results from a rule based legal informationsystem; in the resolution of normative conflict and problems ofnon-monotonicity; and as a basis for dialogue games to support the modellingof the process of argument. The study of argument is held to offer prospectsof real progress (...)
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  21. F. Bergadano (1993). Machine Learning and the Foundations of Inductive Inference. Minds and Machines 3 (1):31-51.
    The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by (...)
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  22. Donald H. Berman (1992). Book Review. [REVIEW] Artificial Intelligence and Law 1 (2-3):237-243.
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  23. John Bickle (2001). Book Symposium on John Horgan's the Undiscovered Mind: How the Human Brain Denies Replication, Medication and Explanation. [REVIEW] Brain and Mind 2 (2):213-213.
  24. Giuseppe Boccignone & Roberto Cordeschi (2012). Predictive Brains: Forethought and the Levels of Explanation. Frontiers in Psychology 3 (511).
    Is any unified theory of brain function possible? Following a line of thought dating back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gaining the “Grand Unified Theory of the Mind”—or “painting the big picture,” as (Edelman 2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental findings and (...)
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  25. Margaret Boden (1984). Animal Perception From an Artificial Intelligence Viewpoint. In Christopher Hookway (ed.), Minds, Machines, and Evolution: Philosophical Studies. Cambridge University Press.
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  26. Margaret A. Boden (ed.) (1990). The Philosophy of Artificial Intelligence. Oxford University Press.
    This interdisciplinary collection of classical and contemporary readings provides a clear and comprehensive guide to the many hotly-debated philosophical issues at the heart of artificial intelligence.
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  27. Margaret A. Boden (1989). Artificial Intelligence In Psychology: Interdisciplinary Essays. Cambridge: Mit Press.
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  28. Margaret A. Boden (1978). Artificial Intelligence and Piagetian Theory. Synthese 38 (July):389-414.
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  29. Margaret A. Boden (1973). How Artificial is Artificial Intelligence? [REVIEW] British Journal for the Philosophy of Science 24 (1):61-72.
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  30. Steffen Borge (2007). A Modal Defence of Strong AI. In Dermot Moran Stephen Voss (ed.), Epistemology. The Proceedings of the Twenty-First World Congress of Philosophy. Vol. 6. The Philosophical Society of Turkey. 127-131.
    John Searle has argued that the aim of strong AI of creating a thinking computer is misguided. Searle’s Chinese Room Argument purports to show that syntax does not suffice for semantics and that computer programs as such must fail to have intrinsic intentionality. But we are not mainly interested in the program itself but rather the implementation of the program in some material. It does not follow by necessity from the fact that computer programs are defined syntactically that the implementation (...)
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  31. Rainer P. Born (ed.) (1987). Artificial Intelligence: The Case Against. St Martin's Press.
  32. Tibor Bosse, Martijn C. Schut & Jan Treur (2009). Formal Analysis of Dynamics Within Philosophy of Mind by Computer Simulation. Minds and Machines 19 (4):543-555.
    Computer simulations can be useful tools to support philosophers in validating their theories, especially when these theories concern phenomena showing nontrivial dynamics. Such theories are usually informal, whilst for computer simulation a formally described model is needed. In this paper, a methodology is proposed to gradually formalise philosophical theories in terms of logically formalised dynamic properties. One outcome of this process is an executable logic-based temporal specification, which within a dedicated software environment can be used as a simulation model to (...)
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  33. Nick Bostrom (2011). A Patch for the Simulation Argument. Analysis 71 (1):54 - 61.
    This article reports on a newly discovered bug in the original simulation argument. Two different ways of patching the argument are proposed, each of which preserves the original conclusion.
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  34. Nick Bostrom, The Transhumanist FAQ.
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  35. Nick Bostrom (1998). How Long Before Superintelligence? International Journal of Futures Studies 2.
    _This paper outlines the case for believing that we will have superhuman artificial intelligence_ _within the first third of the next century. It looks at different estimates of the processing power of_ _the human brain; how long it will take until computer hardware achieve a similar performance;_ _ways of creating the software through bottom-up approaches like the one used by biological_ _brains; how difficult it will be for neuroscience figure out enough about how brains work to_ _make this approach work; (...)
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  36. P. Bouquet (ed.) (2001). Lecture Notes in Artificial Intelligence. Kluwer.
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  37. Danièle Bourcier (2006). Book Review. [REVIEW] Artificial Intelligence and Law 14 (3):241-246.
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  38. L. Karl Branting (1993). A Computational Model of Ratio Decidendi. Artificial Intelligence and Law 2 (1):1-31.
    This paper proposes a model ofratio decidendi as a justification structure consisting of a series of reasoning steps, some of which relate abstract predicates to other abstract predicates and some of which relate abstract predicates to specific facts. This model satisfies an important set of characteristics ofratio decidendi identified from the jurisprudential literature. In particular, the model shows how the theory under which a case is decided controls its precedential effect. By contrast, a purely exemplar-based model ofratio decidendi fails to (...)
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  39. L. Karl Branting (1993). Book Review. [REVIEW] Artificial Intelligence and Law 2 (3):233-238.
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  40. P. Brezillon & P. Bouquet (eds.) (1999). Lecture Notes in Artificial Intelligence. Springer.
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  41. 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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  42. Rodney A. Brooks, The Intelligent Room Project.
    At the MIT Arti cial Intelligence Laboratory we have been working on technologies for an Intelligent Room. Rather than pull people into the virtual world of the computer we are trying to pull the computer out into the real world of people. To do this we are combining robotics and vision technology with speech understanding systems, and agent based architectures to provide ready at hand computation and information services for people engaged in day to day activities, both on their own (...)
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  43. Joanna J. Bryson (2010). Why Robot Nannies Probably Won't Do Much Psychological Damage. Interaction Studies 11 (2):196-200.
  44. David J. Buller (1993). Confirmation and the Computational Paradigm (Or: Why Do You Think They Call Itartificial Intelligence?). [REVIEW] Minds and Machines 3 (2):155-181.
    The idea that human cognitive capacities are explainable by computational models is often conjoined with the idea that, while the states postulated by such models are in fact realized by brain states, there are no type-type correlations between the states postulated by computational models and brain states (a corollary of token physicalism). I argue that these ideas are not jointly tenable. I discuss the kinds of empirical evidence available to cognitive scientists for (dis)confirming computational models of cognition and argue that (...)
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  45. 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.
  46. Leslie Burkholder (ed.) (1992). Philosophy and the Computer. Westview Press.
  47. Graham Button, Jeff Coulter, John R. E. Lee & Wes Sharrock (2000). Re-Entering the Chinese Room. Minds and Machines 10 (1):149-152.
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  48. Graham Button, Jeff Coulter, John R. E. Lee & Wes Sharrock (1995). Computers, Minds, and Conduct. Polity Press.
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  49. T. W. Bynum & J. Moor (eds.) (1998). The Digital Phoenix. Cambridge: Blackwell.
    This important book, which results from a series of presentations at American Philosophical Association conferences, explores the major ways in which computers ...
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  50. Roger Caldwell (1997). Dan Dennett & the Conscious Robot. Philosophy Now 18:16-18.
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