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  1. Microfunctionalism: Connectionism and the Scientific Explanation of Mental States.Andy Clark - 1989 - In Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing. Cambridge: MIT Press.
    This is an amended version of material that first appeared in A. Clark, Microcognition: Philosophy, Cognitive Science, and Parallel Distributed Processing (MIT Press, Cambridge, MA, 1989), Ch. 1, 2, and 6. It appears in German translation in Metzinger,T (Ed) DAS LEIB-SEELE-PROBLEM IN DER ZWEITEN HELFTE DES 20 JAHRHUNDERTS (Frankfurt am Main: Suhrkamp. 1999).
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  • A computational approach to George Boole's discovery of mathematical logic.Luis de Ledesma, Aurora Pérez, Daniel Borrajo & Luis M. Laita - 1997 - Artificial Intelligence 91 (2):281-307.
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  • A few words on representation and meaning. Comments on H.A. Simon's paper on scientific discovery.Roberto Cordeschi - 1992 - 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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  • An analytical comparison of some rule-learning programs.Alan Bundy, Bernard Silver & Dave Plummer - 1985 - Artificial Intelligence 27 (2):137-181.
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  • From animals to animats: Proceedings of the First International Conference on Simulation of Adaptive Behavior.Matthew Brand, Peter Prokopowicz & Clark Elliott - 1995 - Artificial Intelligence 73 (1-2):307-322.
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  • A formal ontology for a generalized inventive design methodology.Cecilia Zanni-Merk, François de Bertrand de Beuvron, François Rousselot & Wei Yan - 2013 - Applied ontology 8 (4):231-273.
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  • Human-aligned artificial intelligence is a multiobjective problem.Peter Vamplew, Richard Dazeley, Cameron Foale, Sally Firmin & Jane Mummery - 2018 - Ethics and Information Technology 20 (1):27-40.
    As the capabilities of artificial intelligence systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility paradigm provides insufficient (...)
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  • Epistemology and cognition.Paul Thagard - 1991 - Erkenntnis 34 (1):117-123.
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  • Learning structures of visual patterns from single instances.Yoshinori Suganuma - 1991 - Artificial Intelligence 50 (1):1-36.
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  • Machine discovery.Herbert Simon - 1995 - Foundations of Science 1 (2):171-200.
    Human and machine discovery are gradual problem-solving processes of searching large problem spaces for incompletely defined goal objects. Research on problem solving has usually focused on search of an instance space (empirical exploration) and a hypothesis space (generation of theories). In scientific discovery, search must often extend to other spaces as well: spaces of possible problems, of new or improved scientific instruments, of new problem representations, of new concepts, and others. This paper focuses especially on the processes for finding new (...)
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  • Functional transformations in AI discovery systems.Wei-Min Shen - 1990 - Artificial Intelligence 41 (3):257-272.
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  • Two Ways of Analogy: Extending the Study of Analogies to Mathematical Domains.Dirk Schlimm - 2008 - Philosophy of Science 75 (2):178-200.
    The structure-mapping theory has become the de-facto standard account of analogies in cognitive science and philosophy of science. In this paper I propose a distinction between two kinds of domains and I show how the account of analogies based on structure-preserving mappings fails in certain (object-rich) domains, which are very common in mathematics, and how the axiomatic approach to analogies, which is based on a common linguistic description of the analogs in terms of laws or axioms, can be used successfully (...)
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  • MUSCADET: An automatic theorem proving system using knowledge and metaknowledge in mathematics.Dominique Pastre - 1989 - Artificial Intelligence 38 (3):257-318.
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  • Logic and artificial intelligence.Nils J. Nilsson - 1991 - Artificial Intelligence 47 (1-3):31-56.
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  • Why am and eurisko appear to work.Douglas B. Lenat & John Seely Brown - 1984 - Artificial Intelligence 23 (3):269-294.
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  • The Role of Evaluation-Driven Rejection in the Successful Exploration of a Conceptual Space of Stories.Carlos León & Pablo Gervás - 2010 - Minds and Machines 20 (4):615-634.
    Evaluation processes are a basic component of creativity. They guide not only the pure judgement about a new artefact but also the generation itself, as creators constantly evaluate their own work. This paper proposes a model for automatic story generation based on the evaluation of stories. A model of how quality in stories is evaluated is presented, and two possible implementations of the generation guided by this evaluation are shown: exhaustive space exploration and constrained exploration. A theoretical model and its (...)
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  • Theory formation by heuristic search.Douglas B. Lenat - 1983 - Artificial Intelligence 21 (1-2):31-59.
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  • Integrated Learning: Controlling Explanation.Michael Lebowitz - 1986 - Cognitive Science 10 (2):219-240.
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  • SOAR: An architecture for general intelligence.John E. Laird, Allen Newell & Paul S. Rosenbloom - 1987 - Artificial Intelligence 33 (1):1-64.
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  • Syntax-directed discovery in mathematics.David S. Henley - 1995 - Erkenntnis 43 (2):241 - 259.
    It is shown how mathematical discoveries such as De Moivre's theorem can result from patterns among the symbols of existing formulae and that significant mathematical analogies are often syntactic rather than semantic, for the good reason that mathematical proofs are always syntactic, in the sense of employing only formal operations on symbols. This radically extends the Lakatos approach to mathematical discovery by allowing proof-directed concepts to generate new theorems from scratch instead of just as evolutionary modifications to some existing theorem. (...)
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  • Pragmatic navigation: reactivity, heuristics, and search.Susan L. Epstein - 1998 - Artificial Intelligence 100 (1-2):275-322.
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  • For the Right Reasons: The FORR Architecture for Learning in a Skill Domain.Susan L. Epstein - 1994 - Cognitive Science 18 (3):479-511.
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  • A concept and its structures. Methodological analysis.Vladimir Kuznetsov (ed.) - 1997 - Institute of philosophy.
    The triplet model treats a concept as complex structure that expresses three kinds of information. The first is about entities subsumed under a concept,their properties and relations. The second is about means and ways of representing the first information in intelligent systems. The third is about linkage between the first and second ones and methods of its constructing. The application of triplet models to generalization and development of concept models in philosophy, logic, cognitive psychology, cognitive science, linguistics, artificial intelligence has (...)
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  • Consciousness and Common Sense: Metaphors of Mind.John A. Barnden - 1997 - In Sean O. Nuallain, Paul Mc Kevitt & Eoghan Mac Aogain (eds.), Two Sciences of Mind. John Benjamins. pp. 311-340.
    The science of the mind, and of consciousness in particular, needs carefully to consider people's common-sense views of the mind, not just what the mind really is. Such views are themselves an aspect of the nature of (conscious) mind, and therefore part of the object of study for a science of mind. Also, since the common-sense views allow broadly successful social interaction, it is reasonable to look to the common-sense views for some rough guidance as to the real nature of (...)
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  • Creativity refined: Bypassing the gatekeepers of appropriateness and value.Alan Dorin & Kevin Korb - unknown
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  • Project to build programs that understand.Eric B. Baum - 2009 - In B. Goertzel, P. Hitzler & M. Hutter (eds.), Proceedings of the Second Conference on Artificial General Intelligence. Atlantis Press. pp. 1--6.
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