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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. Patrick Allo (2012). Kees van Deemter: Not Exactly: In Praise of Vagueness. [REVIEW] Minds and Machines 22 (1):41-45.
  3. Mark H. Bickhard (2000). Motivation and Emotion: An Interactive Process Model. In Ralph D. Ellis & Natika Newton (eds.), The Caldron of Consciousness: Motivation, Affect and Self-Organization. John Benjamins. 161.
    In this chapter, I outline dynamic models of motivation and emotion. These turn out not to be autonomous subsystems, but, instead, are deeply integrated in the basic interactive dynamic character of living systems. Motivation is a crucial aspect of particular kinds of interactive systems -- systems for which representation is a sister aspect. Emotion is a special kind of partially reflective interaction process, and yields its own emergent motivational aspects. In addition, the overall model accounts for some of the crucial (...)
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  4. L. Birnbaum (1991). Rigor Mortis: A Response to Nilsson's 'Logic and Artificial Intelligence'. Artificial Intelligence 47:57-78.
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  5. Nick Bostrom (2012). The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. [REVIEW] Minds and Machines 22 (2):71-85.
    This paper discusses the relation between intelligence and motivation in artificial agents, developing and briefly arguing for two theses. The first, the orthogonality thesis, holds (with some caveats) that intelligence and final goals (purposes) are orthogonal axes along which possible artificial intellects can freely vary—more or less any level of intelligence could be combined with more or less any final goal. The second, the instrumental convergence thesis, holds that as long as they possess a sufficient level of intelligence, agents having (...)
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  6. 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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  7. Andy Clark (1987). The Kludge in the Machine. Mind and Language 2 (4):277-300.
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  8. Andy Clark (1986). A Biological Metaphor. Mind and Language 1 (1):45-64.
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  9. Giovanna Colombetti (2007). Enactive Appraisal. Phenomenology and the Cognitive Sciences 6 (4):527-546.
    Emotion theorists tend to separate “arousal” and other bodily events such as “actions” from the evaluative component of emotion known as “appraisal.” This separation, I argue, implies phenomenologically implausible accounts of emotion elicitation and personhood. As an alternative, I attempt a reconceptualization of the notion of appraisal within the so-called “enactive approach.” I argue that appraisal is constituted by arousal and action, and I show how this view relates to an embodied and affective notion of personhood.
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  10. Giovanna Colombetti & Evan Thompson (forthcoming). The Feeling Body: Towards an Enactive Approach to Emotion. In W. F. Overton, U. Mueller & J. Newman (eds.), Body in Mind, Mind in Body: Developmental Perspectives on Embodiment and Consciousness. Erlbaum.
    For many years emotion theory has been characterized by a dichotomy between the head and the body. In the golden years of cognitivism, during the nineteen-sixties and seventies, emotion theory focused on the cognitive antecedents of emotion, the so-called “appraisal processes.” Bodily events were seen largely as byproducts of cognition, and as too unspecific to contribute to the variety of emotion experience. Cognition was conceptualized as an abstract, intellectual, “heady” process separate from bodily events. Although current emotion theory has moved (...)
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  11. 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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  12. 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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  13. 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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  14. 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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  15. 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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  16. Lindley Darden (1982). Artificial Intelligence and Philosophy of Science: Reasoning by Analogy in Theory Construction. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982:147 - 165.
    This paper examines the hypothesis that analogies may play a role in the generation of new ideas that are built into new explanatory theories. Methods of theory construction by analogy, by failed analogy, and by modular components from several analogies are discussed. Two different analyses of analogy are contrasted: direct mapping (Mary Hesse) and shared abstraction (Michael Genesereth). The structure of Charles Darwin's theory of natural selection shows various analogical relations. Finally, an "abstraction for selection theories" is shown to be (...)
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  17. M. Dascal (1992). Why Does Language Matter to Artificial Intelligence? Minds and Machines 2 (2):145-174.
    Artificial intelligence, conceived either as an attempt to provide models of human cognition or as the development of programs able to perform intelligent tasks, is primarily interested in theuses of language. It should be concerned, therefore, withpragmatics. But its concern with pragmatics should not be restricted to the narrow, traditional conception of pragmatics as the theory of communication (or of the social uses of language). In addition to that, AI should take into account also the mental uses of language (in (...)
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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. Eric Dietrich (1994). AI and the Tyranny of Galen, or Why Evolutionary Psychology and Cognitive Ethology Are Important to Artificial Intelligence. Journal of Experimental and Theoretical Artificial Intelligence 6 (4):325-330.
    Concern over the nature of AI is, for the tastes many AI scientists, probably overdone. In this they are like all other scientists. Working scientists worry about experiments, data, and theories, not foundational issues such as what their work is really about or whether their discipline is methodologically healthy. However, most scientists aren’t in a field that is approximately fifty years old. Even relatively new fields such as nonlinear dynamics or branches of biochemistry are in fact advances in older established (...)
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  20. Hubert L. Dreyfus (2007). Why Heideggerian Ai Failed and How Fixing It Would Require Making It More Heideggerian. Philosophical Psychology 20 (2):247 – 268.
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  21. Hubert L. Dreyfus (1981). From Micro-Worlds to Knowledge: AI at an Impasse. In J. Haugel (ed.), Mind Design. MIT Press.
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  22. Hubert L. Dreyfus & Stuart E. Dreyfus (1988). Making a Mind Versus Modeling the Brain: AI at a Crossroads. Daedalus.
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  23. Ralph D. Ellis (ed.) (2000). The Caldron of Consciousness: Motivation, Affect and Self-Organization. John Benjamins.
  24. Jon Elster (1996). Rationality and the Emotions. Economic Journal 106:1386-97.
    In an earlier paper (Elster, 1989 a), I discussed the relation between rationality and social norms. Although I did mention the role of the emotions in sustaining social norms, I did not focus explicitly on the relation between rationality and the emotions. That relation is the main topic of the present paper, with social norms in a subsidiary part.
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  25. Federico Faroldi (2011). Don Ross Et Al. (Eds.), Distributed Cognition and the Will. Minds and Machines 21 (1):115-118.
  26. Norman Fenton, Martin Neil & David A. Lagnado (2013). A General Structure for Legal Arguments About Evidence Using Bayesian Networks. Cognitive Science 37 (1):61-102.
    A Bayesian network (BN) is a graphical model of uncertainty that is especially well suited to legal arguments. It enables us to visualize and model dependencies between different hypotheses and pieces of evidence and to calculate the revised probability beliefs about all uncertain factors when any piece of new evidence is presented. Although BNs have been widely discussed and recently used in the context of legal arguments, there is no systematic, repeatable method for modeling legal arguments as BNs. Hence, where (...)
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  27. P. A. Flach (ed.) (1991). Future Directions in Artificial Intelligence. New York: Elsevier Science.
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  28. Marcello Frixione & Antonio Lieto (2013). Dealing with Concepts: From Cognitive Psychology to Knowledge Representation. Frontiers of Psychological and Behevioural Science 2 (3):96-106.
    Concept representation is still an open problem in the field of ontology engineering and, more generally, of knowledge representation. In particular, the issue of representing “non classical” concepts, i.e. concepts that cannot be defined in terms of necessary and sufficient conditions, remains unresolved. In this paper we review empirical evidence from cognitive psychology, according to which concept representation is not a unitary phenomenon. On this basis, we sketch some proposals for concept representation, taking into account suggestions from psychological research. In (...)
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  29. Marcello Frixione & Antonio Lieto (2012). Representing Concepts in Formal Ontologies: Compositionality Vs. Typicality Effects&Quot;,. Logic and Logical Philosophy 21 ( Logic, Reasoning and Rationalit):391-414.
    The problem of concept representation is relevant for many sub-fields of cognitive research, including psychology and philosophy, as well as artificial intelligence. In particular, in recent years it has received a great deal of attention within the field of knowledge representation, due to its relevance for both knowledge engineering as well as ontology-based technologies. However, the notion of a concept itself turns out to be highly disputed and problematic. In our opinion, one of the causes of this state of affairs (...)
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  30. 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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  31. Joseph S. Fulda (2006). A Plea for Automated Language-to-Logical-Form Converters. RASK 24:87-102.
    This has been made available gratis by the publisher. -/- This piece gives the raison d'etre for the development of the converters mentioned in the title. Three reasons are given, one linguistic, one philosophical, and one practical. It is suggested that at least /two/ independent converters are needed. -/- This piece ties together the extended paper "Abstracts from Logical Form I/II," and the short piece providing the comprehensive theory alluded to in the abstract of that extended paper in "Pragmatics, Montague, (...)
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  32. Paul E. Griffiths & Andrea Scarantino (2005). Emotions in the Wild: The Situated Perspective on Emotion. In P. Robbins & Murat Aydede (eds.), The Cambridge Handbook of Situated Cognition. Cambridge University Press.
    Paul E Griffiths Biohumanities Project University of Queensland St Lucia 4072 Australia paul.griffiths@uq.edu.au.
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  33. Patrick Grüneberg & Kenji Suzuki (forthcoming). An Approach to Subjective Computing: A Robot That Learns From Interaction with Humans. Ieee Transactions on Autonomous Mental Development.
    We present an approach to subjective computing for the design of future robots that exhibit more adaptive and flexible behavior in terms of subjective intelligence. Instead of encapsulating subjectivity into higher order states, we show by means of a relational approach how subjective intelligence can be implemented in terms of the reciprocity of autonomous self-referentiality and direct world-coupling. Subjectivity concerns the relational arrangement of an agent’s cognitive space. This theoretical concept is narrowed down to the problem of coaching a reinforcement (...)
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  34. Patrick Grüneberg & Kenji Suzuki (2013). A Lesson From Subjective Computing: Autonomous Self-Referentiality and Social Interaction as Conditions for Subjectivity. AISB Proceedings 2012:18-28.
    In this paper, we model a relational notion of subjectivity by means of two experiments in subjective computing. The goal is to determine to what extent a cognitive and social robot can be regarded to act subjectively. The system was implemented as a reinforcement learning agent with a coaching function. To analyze the robotic agent we used the method of levels of abstraction in order to analyze the agent at four levels of abstraction. At one level the agent is described (...)
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  35. Robert F. Hadley (1991). The Many Uses of 'Belief' in AI. Minds and Machines 1 (1):55-74.
    Within AI and the cognitively related disciplines, there exist a multiplicity of uses of belief. On the face of it, these differing uses reflect differing views about the nature of an objective phenomenon called belief. In this paper I distinguish six distinct ways in which belief is used in AI. I shall argue that not all these uses reflect a difference of opinion about an objective feature of reality. Rather, in some cases, the differing uses reflect differing concerns with special (...)
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  36. J. Haugel (ed.) (1981). Mind Design. MIT Press.
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  37. John Haugeland (1979). Understanding Natural Language. Journal of Philosophy 76 (November):619-32.
  38. Peter Hucklenbroich (1988). Problems of Nomenclature and Classification in Medical Expert Systems. Theoretical Medicine and Bioethics 9 (2).
    Medical expert systems (MES) are knowledge-based computer programs that are designed for advising physicians on diagnostical and therapeutical decision-making. They use heuristic methods developed by Artificial Intelligence researchers in order to retrieve from large knowledge-bases information needed in the situation. Constructing the knowledge-base of a MES embraces the problem of explicating and fixing the conceptual, causal and epistemic relations between a lot of medical objects. There is a number of preconditions which any adequate representation of such knowledge must fulfil, among (...)
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  39. David Kirsh (1991). Foundations of AI: The Big Issues. Artificial Intelligence 47:3-30.
    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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  40. Alfred Kobsa (1987). What is Explained by AI Models. In Artificial Intelligence. St Martin's Press.
  41. Alfred Kobsa (1987). Artificial Intelligence. St Martin's Press.
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  42. Willem A. Labuschagne & Johannes Heidema (2005). Natural and Artificial Cognition: On the Proper Place of Reason. South African Journal of Philosophy 24 (2):137-149.
    We explore the psychological foundations of Logic and Artificial Intelligence, touching on representation, categorisation, heuristics, consciousness, and emotion. Specifically, we challenge Dennett's view of the brain as a syntactic engine that is limited to processing symbols according to their structural properties. We show that cognitive psychology and neurobiology support a dual-process model in which one form of cognition is essentially semantical and differs in important ways from the operation of a syntactic engine. The dual-process model illuminates two important events in (...)
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  43. David Makinson (1994). Handbook of Logic in Artificial Intelligence Nad Logic Programming, Vol. Iii. Clarendon Press.
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  44. David Marr (1977). Artificial Intelligence: A Personal View. Artificial Intelligence 9 (September):37-48.
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  45. John McCarthy, What is Artificial Intelligence?
  46. Drew McDermott (1987). A Critique of Pure Reason. Computational Intelligence 3:151-60.
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  47. Drew McDermott (1981). Artificial Intelligence Meets Natural Stupidity. In J. Haugel (ed.), Mind Design. MIT Press. 5-18.
  48. Michael Morreau & Sarit Kraus (1998). Syntactical Treatments of Propositional Attitudes. Artificial Intelligence 106 (1):161-177.
    Syntactical treatments of propositional attitudes are attractive to artificial intelligence researchers. But results of Montague (1974) and Thomason (1980) seem to show that syntactical treatments are not viable. They show that if representation languages are sufficiently expressive, then axiom schemes characterizing knowledge and belief give rise to paradox. Des Rivières and Levesque (1988) characterize a class of sentences within which these schemes can safely be instantiated. These sentences do not quantify over the propositional objects of knowledge and belief. We argue (...)
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  49. 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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  50. Neil Nilsson (1991). Logic and Artificial Intelligence. Artificial Intelligence 47:31-56.
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