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  1. added 2020-05-19
    Ontology and Cognitive Outcomes (Preprint).David Limbaugh, David Kasmier, Ronald Rudnicki, James Llinas & Barry Smith - 2020 - In arXiv.
    The intelligence community relies on human-machine-based analytic strategies that 1) access and integrate vast amounts of information from disparate sources, 2) continuously process this information, so that, 3) a maximally comprehensive understanding of world actors and their behaviors can be developed and updated. Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts.
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  2. added 2020-05-09
    AGI and the Knight-Darwin Law: Why Idealized AGI Reproduction Requires Collaboration.Samuel Alexander - forthcoming - In International Conference on Artificial General Intelligence. Springer.
    Can an AGI create a more intelligent AGI? Under idealized assumptions, for a certain theoretical type of intelligence, our answer is: “Not without outside help”. This is a paper on the mathematical structure of AGI populations when parent AGIs create child AGIs. We argue that such populations satisfy a certain biological law. Motivated by observations of sexual reproduction in seemingly-asexual species, the Knight-Darwin Law states that it is impossible for one organism to asexually produce another, which asexually produces another, and (...)
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  3. added 2020-03-13
    On a Cognitive Model of Semiosis.Piotr Konderak - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):129-144.
    What is the class of possible semiotic systems? What kinds of systems could count as such systems? The human mind is naturally considered the prototypical semiotic system. During years of research in semiotics the class has been broadened to include i.e. living systems like animals, or even plants. It is suggested in the literature on artificial intelligence that artificial agents are typical examples of symbol-processing entities. It also seems that semiotic processes are in fact cognitive processes. In consequence, it is (...)
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  4. added 2020-02-15
    The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - manuscript
    After generalizing the Archimedean property of real numbers in such a way as to make it adaptable to non-numeric structures, we demonstrate that the real numbers cannot be used to accurately measure non-Archimedean structures. We argue that, since an agent with Artificial General Intelligence (AGI) should have no problem engaging in tasks that inherently involve non-Archimedean rewards, and since traditional reinforcement learning rewards are real numbers, therefore traditional reinforcement learning cannot lead to AGI. We indicate two possible ways traditional reinforcement (...)
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  5. added 2019-10-28
    Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - forthcoming - Philosophy and Technology:1-24.
    Many of the computing systems programmed using Machine Learning are opaque: it is difficult to know why they do what they do or how they work. The Explainable AI research program aims to develop analytic techniques that render opaque computing systems transparent, but lacks a normative framework with which to evaluate these techniques’ explanatory contributions. The aim of the present discussion is to develop such a framework, paying particular attention to different stakeholders’ distinct explanatory requirements. Building on an analysis of (...)
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  6. added 2019-09-30
    Interprétabilité et explicabilité pour l’apprentissage machine : entre modèles descriptifs, modèles prédictifs et modèles causaux. Une nécessaire clarification épistémologique.Christophe Denis & Franck Varenne - 2019 - Actes de la Conférence Nationale En Intelligence Artificielle - CNIA 2019.
    Le déficit d’explicabilité des techniques d’apprentissage machine (AM) pose des problèmes opérationnels, juridiques et éthiques. Un des principaux objectifs de notre projet est de fournir des explications éthiques des sorties générées par une application fondée sur de l’AM, considérée comme une boîte noire. La première étape de ce projet, présentée dans cet article, consiste à montrer que la validation de ces boîtes noires diffère épistémologiquement de celle mise en place dans le cadre d’une modélisation mathématique et causale d’un phénomène physique. (...)
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  7. added 2019-06-14
    There is No General AI: Why Turing Machines Cannot Pass the Turing Test.Jobst Landgrebe & Barry Smith - 2020 - arXiv.
    Since 1950, when Alan Turing proposed what has since come to be called the Turing test, the ability of a machine to pass this test has established itself as the primary hallmark of general AI. To pass the test, a machine would have to be able to engage in dialogue in such a way that a human interrogator could not distinguish its behaviour from that of a human being. AI researchers have attempted to build machines that could meet this requirement, (...)
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  8. added 2019-06-13
    Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2019 - Synthese:arXiv:1901.02918v1.
    Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial (...)
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  9. added 2019-02-16
    In 30 Schritten zum Mond? Zukünftiger Fortschritt in der KI.Vincent C. Müller - 2018 - Medienkorrespondenz 20 (05.10.2018):5-15.
    Die Entwicklungen in der Künstlichen Intelligenz (KI) sind spannend. Aber wohin geht die Reise? Ich stelle eine Analyse vor, der zufolge exponentielles Wachstum von Rechengeschwindigkeit und Daten die entscheidenden Faktoren im bisherigen Fortschritt waren. Im Folgenden erläutere ich, unter welchen Annahmen dieses Wachstum auch weiterhin Fortschritt ermöglichen wird: 1) Intelligenz ist eindimensional und messbar, 2) Kognitionswissenschaft wird für KI nicht benötigt, 3) Berechnung (computation) ist hinreichend für Kognition, 4) Gegenwärtige Techniken und Architektur sind ausreichend skalierbar, 5) Technological Readiness Levels (TRL) (...)
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  10. added 2018-08-21
    The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI.Fernando Martínez-Plumed, Bao Sheng Loe, Peter Flach, Sean O. O. HEigeartaigh, Karina Vold & José Hernández-Orallo - 2018 - In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence Evolution of the contours of AI. pp. 5180-5187.
    We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI (...)
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  11. added 2018-01-02
    Neural-Symbolic Cognitive Reasoning.Artur D'Avila Garcez, Luis Lamb & Dov Gabbay - 2009 - New York: Springer.
    Humans are often extraordinary at performing practical reasoning. There are cases where the human computer, slow as it is, is faster than any artificial intelligence system. Are we faster because of the way we perceive knowledge as opposed to the way we represent it? -/- The authors address this question by presenting neural network models that integrate the two most fundamental phenomena of cognition: our ability to learn from experience, and our ability to reason from what has been learned. This (...)
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  12. added 2016-12-08
    An Expert System for Feeding Problems in Infants and Children.Samy S. Abu Naser & Mariam W. Alawar - 2016 - International Journal of Medicine Research 1 (2):79--82.
    A lot of infants have significant food-related problems, as well as spitting up, rejecting new foods, or not accepting to eat at specific times. These issues are frequently ordinary and are not a sign that the baby is unwell. According to the National Institutes of Health, 25% of generally developing infants and 35% of babies with neurodevelopmental disabilities are tormented by some sort of feeding problem. Some, for example rejecting to eat specific foods or being overly finicky, are momentary and (...)
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  13. added 2016-12-08
    Artificial Explanations: The Epistemological Interpretation of Explanation in AI.Andrés Páez - 2009 - Synthese 170 (1):131-146.
    In this paper I critically examine the notion of explanation used in Artificial Intelligence in general, and in the theory of belief revision in particular. I focus on two of the best known accounts in the literature: Pagnucco’s abductive expansion functions and Gärdenfors’ counterfactual analysis. I argue that both accounts are at odds with the way in which this notion has historically been understood in philosophy. They are also at odds with the explanatory strategies used in actual scientific practice. At (...)
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  14. added 2016-12-08
    Passionate Engines: What Emotions Reveal About the Mind and Artificial Intelligence.Craig DeLancey - 2001 - Oxford University Press USA.
    DeLancey shows that our understanding of emotion provides essential insight on key issues in philosophy of mind and artificial intelligence. He offers us a bold new approach to the study of the mind based on the latest scientific research and provides an accessible overview of the science of emotion.
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  15. added 2016-12-05
    Cognitive Behavioural Systems.Esposito Anna, Esposito Antonietta M., Hoffmann Rüdiger, Müller Vincent C. & Vinciarelli Alessandro (eds.) - 2012 - Springer.
    This book constitutes refereed proceedings of the COST 2102 International Training School on Cognitive Behavioural Systems held in Dresden, Germany, in February 2011. The 39 revised full papers presented were carefully reviewed and selected from various submissions. The volume presents new and original research results in the field of human-machine interaction inspired by cognitive behavioural human-human interaction features. The themes covered are on cognitive and computational social information processing, emotional and social believable Human-Computer Interaction (HCI) systems, behavioural and contextual analysis (...)
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  16. added 2016-09-23
    From Human to Artificial Cognition and Back: New Perspectives on Cognitively Inspired AI Systems.Antonio Lieto & Daniele Radicioni - 2016 - Cognitive Systems Research 39 (c):1-3.
    We overview the main historical and technological elements characterising the rise, the fall and the recent renaissance of the cognitive approaches to Artificial Intelligence and provide some insights and suggestions about the future directions and challenges that, in our opinion, this discipline needs to face in the next years.
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  17. added 2016-07-25
    La subjectivité artificielle : ébauche d'un projet de recherche.Jean-Jacques Pinto - manuscript
    Subjectivité artificielle: -/- •pléonasme, s'il est exact que la subjectivité humaine ne peut être qu'artificielle, cf infra subjiciel© -/- •terme proposé par l'auteur de l'A.L.S.© (Jean-Jacques Pinto) pour faire pendant à celui d'Intelligence artificielle -/- Subjiciel© : terme forgé (et déposé comme marque à l'I.N.P.I. en 1984) par l'auteur de l'A.L.S. : Jacques Pinto) : -/- 1. programmesubjectif "naturel", mais il se pourrait bien que la subjectivité humaine ne puisse être qu'artificielle : il n'y a pas de "nature humaine", seulement (...)
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  18. added 2016-03-23
    Framework of Consciousness From Semblance of Activity at Functionally LINKed Postsynaptic Membranes.Kunjumon Vadakkan - 2010 - Frontiers in Consciousness Research 1 (1):1-12.
    Consciousness is seen as a difficult “binding” problem. Binding, a process where different sensations evoked by an item are associated in the nervous system, can be viewed as a process similar to associative learning. Several reports that consciousness is associated with some form of memory imply that different forms of memories have a common feature contributing to consciousness. Based on a proposed synaptic mechanism capable of explaining different forms of memory, we developed a framework for consciousness. It is based on (...)
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  19. added 2016-03-11
    Why Build a Virtual Brain? Large-Scale Neural Simulations as Jump Start for Cognitive Computing.Matteo Colombo - 2016 - Journal of Experimental and Theoretical Artificial Intelligence.
    Despite the impressive amount of financial resources recently invested in carrying out large-scale brain simulations, it is controversial what the pay-offs are of pursuing this project. One idea is that from designing, building, and running a large-scale neural simulation, scientists acquire knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. It has been claimed that this knowledge may usher in a new era of neuromorphic, cognitive computing systems. This study elucidates (...)
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  20. added 2016-03-11
    Why Build a Virtual Brain? Large-Scale Neural Simulations as Test-Bed for Artificial Computing Systems.Matteo Colombo - 2015 - In D. C. Noelle, R. Dale, A. S. Warlaumont, J. Yoshimi, T. Matlock, C. D. Jennings & P. P. Maglio (eds.), Proceedings of the 37th Annual Conference of the Cognitive Science Society. Cognitive Science Society. pp. 429-434.
    Despite the impressive amount of financial resources invested in carrying out large-scale brain simulations, it is controversial what the payoffs are of pursuing this project. The present paper argues that in some cases, from designing, building, and running a large-scale neural simulation, scientists acquire useful knowledge about the computational performance of the simulating system, rather than about the neurobiological system represented in the simulation. What this means, why it is not a trivial lesson, and how it advances the literature on (...)
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  21. added 2015-11-07
    Simple or Complex Bodies? Trade-Offs in Exploiting Body Morphology for Control.Matej Hoffmann & Vincent C. Müller - 2017 - In Gordana Dodig-Crnkovic & Raffaela Giovagnoli (eds.), Representation of Reality: Humans, Other Living Organisms and Intelligent Machines. Berlin: Springer. pp. 335-345.
    Engineers fine-tune the design of robot bodies for control purposes, however, a methodology or set of tools is largely absent, and optimization of morphology (shape, material properties of robot bodies, etc.) is lagging behind the development of controllers. This has become even more prominent with the advent of compliant, deformable or ”soft” bodies. These carry substantial potential regarding their exploitation for control—sometimes referred to as ”morphological computation”. In this article, we briefly review different notions of computation by physical systems and (...)
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  22. added 2015-11-04
    Challenges for Artificial Cognitive Systems.Antoni Gomila & Vincent C. Müller - 2012 - Journal of Cognitive Science 13 (4):452-469.
    The declared goal of this paper is to fill this gap: “... cognitive systems research needs questions or challenges that define progress. The challenges are not (yet more) predictions of the future, but a guideline to what are the aims and what would constitute progress.” – the quotation being from the project description of EUCogII, the project for the European Network for Cognitive Systems within which this formulation of the ‘challenges’ was originally developed (http://www.eucognition.org). So, we stick out our neck (...)
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  23. added 2015-11-04
    Theory and Philosophy of AI (Minds and Machines, 22/2 - Special Volume).Vincent C. Müller (ed.) - 2012 - Springer.
    Invited papers from PT-AI 2011. - Vincent C. Müller: Introduction: Theory and Philosophy of Artificial Intelligence - Nick Bostrom: The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents - Hubert L. Dreyfus: A History of First Step Fallacies - Antoni Gomila, David Travieso and Lorena Lobo: Wherein is Human Cognition Systematic - J. Kevin O'Regan: How to Build a Robot that Is Conscious and Feels - Oron Shagrir: Computation, Implementation, Cognition.
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  24. added 2015-08-28
    Was Roboter nicht können. Die Roboterantwort als knapp misslungene Verteidigung der starken KI-These.Geert Keil - 1998 - In Andreas Engel & Peter Gold (eds.), Der Mensch in der Perspektive der Kognitionswissenschaften. Suhrkamp. pp. 98-131.
    Theoretiker der Künstlichen Intelligenz und deren Wegbegleiter in der Philosophie des Geistes haben auf unterschiedliche Weise auf Kritik am ursprünglichen Theorieziel der KI reagiert. Eine dieser Reaktionen ist die Zurücknahme dieses Theorieziels zugunsten der Verfolgung kleinerformatiger Projekte. Eine andere Reaktion ist die Propagierung konnektionistischer Systeme, die mit ihrer dezentralen Arbeitsweise die neuronalen Netze des menschlichen Gehirns besser simulieren sollen. Eine weitere ist die sogenannte robot reply. Die Roboterantwort besteht aus zwei Elementen. Sie enthält (a) das Zugeständnis, daß das Systemverhalten eines (...)
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  25. added 2015-07-29
    Knowledge Bases and Neural Network Synthesis.Todd R. Davies - 1991 - In Hozumi Tanaka (ed.), Artificial Intelligence in the Pacific Rim: Proceedings of the Pacific Rim International Conference on Artificial Intelligence. IOS Press. pp. 717-722.
    We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) representation, run, and the (...)
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  26. added 2015-07-29
    Determination, Uniformity, and Relevance: Normative Criteria for Generalization and Reasoning by Analogy.Todd R. Davies - 1988 - In David H. Helman (ed.), Analogical Reasoning. Kluwer Academic Publishers. pp. 227-250.
    This paper defines the form of prior knowledge that is required for sound inferences by analogy and single-instance generalizations, in both logical and probabilistic reasoning. In the logical case, the first order determination rule defined in Davies (1985) is shown to solve both the justification and non-redundancy problems for analogical inference. The statistical analogue of determination that is put forward is termed 'uniformity'. Based on the semantics of determination and uniformity, a third notion of "relevance" is defined, both logically and (...)
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  27. added 2015-07-29
    A Logical Approach to Reasoning by Analogy.Todd R. Davies & Stuart J. Russell - 1987 - In John P. McDermott (ed.), Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI'87). Morgan Kaufmann Publishers. pp. 264-270.
    We analyze the logical form of the domain knowledge that grounds analogical inferences and generalizations from a single instance. The form of the assumptions which justify analogies is given schematically as the "determination rule", so called because it expresses the relation of one set of variables determining the values of another set. The determination relation is a logical generalization of the different types of dependency relations defined in database theory. Specifically, we define determination as a relation between schemata of first (...)
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  28. added 2015-04-27
    Evaluating Artificial Models of Cognition.Marcin Miłkowski - 2015 - Studies in Logic, Grammar and Rhetoric 40 (1):43-62.
    Artificial models of cognition serve different purposes, and their use determines the way they should be evaluated. There are also models that do not represent any particular biological agents, and there is controversy as to how they should be assessed. At the same time, modelers do evaluate such models as better or worse. There is also a widespread tendency to call for publicly available standards of replicability and benchmarking for such models. In this paper, I argue that proper evaluation ofmodels (...)
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  29. added 2015-03-26
    The Impact of Argumentation on Artificial Intelligence.David Godden - 2006 - In F. H. van Eemeren, Peter Houtlosser & M. A. van Rees (eds.), Considering Pragma-Dialectics: A Festschrift for Frans H. L. Erlbaum Associates. pp. 287-299.
    In this chapter, we explore the development and importance of the connection between argumentation and artificial intelligence. Specifically, we show that the influence of argumentation on AI has occurred within a framework that is consistent with the basic approach of Pragma-Dialectics. While the pragma-dialectical approach is typically conceived of as applying primarily to argumentation occurring between human agents, we show that the basic features of this approach can consistently be applied in a virtual context, whereby the goal-directed activities of, and (...)
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  30. added 2014-08-29
    Applied Ontology: A New Discipline is Born.B. Smith - 1998 - Philosophy Today 12 (29):5-6.
    The discipline of applied ethics already has a certain familiarity in the Anglo-Saxon world, above all through the work of Peter Singer. Applied ethics uses the tools of moral philosophy to resolve practical problems of the sort which arise, for example, in the running of hospitals. In the University at Buffalo (New York) there was organized on April 24-25 1998 the world's first conference on a new, sister discipline, the discipline of applied ontology. Applied ontologists seek to apply ontological tools (...)
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  31. added 2014-03-15
    Enactive Appraisal.Giovanna Colombetti - 2007 - 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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  32. added 2014-03-10
    Decision Theory, Intelligent Planning and Counterfactuals.Michael John Shaffer - 2008 - Minds and Machines 19 (1):61-92.
    The ontology of decision theory has been subject to considerable debate in the past, and discussion of just how we ought to view decision problems has revealed more than one interesting problem, as well as suggested some novel modifications of classical decision theory. In this paper it will be argued that Bayesian, or evidential, decision-theoretic characterizations of decision situations fail to adequately account for knowledge concerning the causal connections between acts, states, and outcomes in decision situations, and so they are (...)
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  33. added 2014-03-09
    Why Heideggerian Ai Failed and How Fixing It Would Require Making It More Heideggerian.Hubert L. Dreyfus - 2007 - Philosophical Psychology 20 (2):247 – 268.
  34. added 2014-03-09
    Natural and Artificial Cognition: On the Proper Place of Reason.Willem A. Labuschagne & Johannes Heidema - 2005 - 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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  35. added 2014-03-07
    Bica and Beyond: How Biology and Anomalies Together Contribute to Flexible Cognition.Donald Perlis - 2010 - International Journal of Machine Consciousness 2 (2):261-271.
  36. added 2014-03-04
    Don Ross Et Al. (Eds.), Distributed Cognition and the Will. [REVIEW]Federico Faroldi - 2011 - Minds and Machines 21 (1):115-118.
  37. added 2014-02-22
    An Approach to Subjective Computing: A Robot That Learns From Interaction with Humans.Patrick Grüneberg & Kenji Suzuki - 2014 - Ieee Transactions on Autonomous Mental Development 6 (1):5-18.
    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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  38. added 2014-02-22
    A Lesson From Subjective Computing: Autonomous Self-Referentiality and Social Interaction as Conditions for Subjectivity.Patrick Grüneberg & Kenji Suzuki - 2013 - 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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  39. added 2013-08-28
    Searching in a Maze, in Search of Knowledge: Issues in Early Artificial Intelligence.Roberto Cordeschi - 2006 - In Lecture Notes In Computer Science, vol. 4155. Springer. pp. 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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  40. added 2013-08-01
    Dealing with Concepts: From Cognitive Psychology to Knowledge Representation.Marcello Frixione & Antonio Lieto - 2013 - 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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  41. added 2013-07-01
    Philosophical Assumptions in Artificial Intelligence: A Tentative Criticism of a Criticism.Roberto Cordeschi - 1989 - In Proceedings of the 5th Osterreichische Artificial-Intelligence-Tagung. Springer.
  42. added 2013-06-29
    Artificial Intelligence: A Tentative Criticism of a Criticism.Roberto Cordeschi - 1989 - In Proceedings of the 5th Osterreichische Artificial-Intelligence-Tagung. Springer.
  43. added 2013-06-27
    Brain, Mind and Computers.Roberto Cordeschi - 1991 - In P. Corsi (ed.), The Enchanted Loom: Chapters in the History of Neuroscience. Oxford University Press.
    In this chapter the early history of Computer Science, Cybernetics and Artificial Intelligence is sketched. More recent developments of AI and the philosophy of Cognitive Science are also discussed.
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  44. added 2013-06-26
    Artificial Intelligence and Evolutionary Theory: Herbert Simon's Unifying Framework.Roberto Cordeschi - 2011 - In C. Cellucci, E. Grosholz & E. Ippoliti (eds.), Logic and knowledge. Cambridge Scholars Press.
    A number of contributions are been given in recent years to illustrate Herbert Simon’s multidisciplinary approach to the study of behaviour. In this chapter, I give a brief picture of the origins of Simon’s bounded rationality in the framework of rising AI. I show then how seminal it was Simon’s insight on the unifying role of bounded rationality in different fields, from evolutionary theory to domains traditionally difficult for AI decision-making, such as those of real-life and real-world problems.
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  45. added 2013-06-26
    Steps Toward the Synthetic Method: Symbolic Information Processing and Self-Organizing Systems in Early Artificial Intelligence.Roberto Cordeschi - 2008 - In P. Husbands, O. Holland & M. Wheeler (eds.), The Mechanichal Mind in History. MIT Press.
    The year 1943 is customarily considered as the birth of cybernetics. Artificial Intelligence (AI) was officially born thirteen years later, in 1956. This chapter is about two theories on human cognitive processes developed in the context of cybernetics and early AI. The first theory is that of the cyberneticist Donald MacKay, in the framework of an original version of self-organizing systems; the second is that of Allen Newell and Herbert Simon (initially with the decisive support of Clifford Shaw) and is (...)
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  46. added 2013-06-26
    Computationalism Under Attack.Roberto Cordeschi & Marcello Frixione - 2007 - 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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  47. added 2013-06-26
    AI Turns Fifty: Revisiting its Origins.Roberto Cordeschi - 2007 - 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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  48. added 2013-06-26
    The Discovery of the Artificial: Behavior, Mind and Machines Before and Beyond Cybernetics.Roberto Cordeschi - 2002 - Kluwer Academic Publishers.
    Since the second half of the XXth century, researchers in cybernetics and AI, neural nets and connectionism, Artificial Life and new robotics have endeavoured to build different machines that could simulate functions of living organisms, such as adaptation and development, problem solving and learning. In this book these research programs are discussed, particularly as regard the epistemological issues of the behaviour modelling. One of the main novelty of this book consists of the fact that certain projects involving the building of (...)
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  49. added 2013-06-26
    The Role of Heuristics in Automated Theorem Proving.Roberto Cordeschi - 1996 - Mathware and Soft Computing 3:281-293.
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  50. added 2013-06-26
    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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