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  1. Artificial Intelligence and Meaning — Some Philosophical Aspects of Decision-Making.Pascal Acot, Sandrine Charles & Marie-Laure Delignette-Muller - 2000 - Acta Biotheoretica 48 (3-4):173-179.
  2. Kees van Deemter: Not Exactly: In Praise of Vagueness. [REVIEW]Patrick Allo - 2012 - Minds and Machines 22 (1):41-45.
  3. 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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  4. Motivation and Emotion: An Interactive Process Model.Mark H. Bickhard - 2000 - In Ralph D. Ellis & Natika Newton (eds.), The Caldron of Consciousness: Motivation, Affect and Self-Organization. John Benjamins. pp. 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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  5. Rigor Mortis: A Response to Nilsson's 'Logic and Artificial Intelligence'.L. Birnbaum - 1991 - Artificial Intelligence 47:57-78.
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  6. The Superintelligent Will: Motivation and Instrumental Rationality in Advanced Artificial Agents. [REVIEW]Nick Bostrom - 2012 - 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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  7. High-Level Perception, Representation, and Analogy:A Critique of Artificial Intelligence Methodology.David J. Chalmers, Robert M. French & Douglas R. Hofstadter - 1992 - 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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  8. The Kludge in the Machine.Andy Clark - 1987 - Mind and Language 2 (4):277-300.
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  9. A Biological Metaphor.Andy Clark - 1986 - Mind and Language 1 (1):45-64.
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  10. 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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  11. The Feeling Body: Towards an Enactive Approach to Emotion.Giovanna Colombetti & Evan Thompson - forthcoming - 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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  12. 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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  13. 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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  14. 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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  15. 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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  16. 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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  17. 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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  18. 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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  19. The Role of Heuristics in Automated Theorem Proving.Roberto Cordeschi - 1996 - Mathware and Soft Computing 3:281-293.
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  20. 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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  21. 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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  22. Artificial Intelligence: A Tentative Criticism of a Criticism.Roberto Cordeschi - 1989 - In Proceedings of the 5th Osterreichische Artificial-Intelligence-Tagung. Springer.
  23. Philosophical Assumptions in Artificial Intelligence: A Tentative Criticism of a Criticism.Roberto Cordeschi - 1989 - In Proceedings of the 5th Osterreichische Artificial-Intelligence-Tagung. Springer.
  24. 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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  25. Artificial Intelligence and Philosophy of Science: Reasoning by Analogy in Theory Construction.Lindley Darden - 1982 - 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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  26. Why Does Language Matter to Artificial Intelligence?M. Dascal - 1992 - 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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  27. 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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  28. 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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  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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  30. 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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  31. AI and the Tyranny of Galen, or Why Evolutionary Psychology and Cognitive Ethology Are Important to Artificial Intelligence.Eric Dietrich - 1994 - 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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  32. Why Heideggerian Ai Failed and How Fixing It Would Require Making It More Heideggerian.Hubert L. Dreyfus - 2007 - Philosophical Psychology 20 (2):247 – 268.
  33. From Micro-Worlds to Knowledge: AI at an Impasse.Hubert L. Dreyfus - 1981 - In J. Haugel (ed.), Mind Design. MIT Press.
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  34. Making a Mind Versus Modeling the Brain: AI at a Crossroads.Hubert L. Dreyfus & Stuart E. Dreyfus - 1988 - Daedalus.
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  35. The Caldron of Consciousness: Motivation, Affect and Self-Organization.Ralph D. Ellis (ed.) - 2000 - John Benjamins.
  36. Rationality and the Emotions.Jon Elster - 1996 - 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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  37. Don Ross Et Al. (Eds.), Distributed Cognition and the Will. [REVIEW]Federico Faroldi - 2011 - Minds and Machines 21 (1):115-118.
  38. A General Structure for Legal Arguments About Evidence Using Bayesian Networks.Norman Fenton, Martin Neil & David A. Lagnado - 2013 - 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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  39. Future Directions in Artificial Intelligence.P. A. Flach (ed.) - 1991 - New York: Elsevier Science.
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  40. 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. Representing Concepts in Formal Ontologies: Compositionality Vs. Typicality Effects".Marcello Frixione & Antonio Lieto - 2012 - 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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  42. Implications of a Logical Paradox for Computer-Dispensed Justice Reconsidered: Some Key Differences Between Minds and Machines.Joseph S. Fulda - 2012 - 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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  43. A Plea for Automated Language-to-Logical-Form Converters.Joseph S. Fulda - 2006 - 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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  44. 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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  45. 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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  46. The Pleadings Games: An Artificial Intelligence Model of Procedural Justice.Thomas F. Gordon - 1995 - Springer.
    The Pleadings Game is a major contribution to artificial intelligence and legal theory. The book draws on jurisprudence and moral philosophy to develop a formal model of argumentation called the pleadings game. From a technical perspective, the work can be viewed as an extension of recent argumentation-based approaches to non-monotonic logic: (1) the game is dialogical rather than mono-logical; (2) the validity and priority of defeasible rules is subject to debate; and (3) resource limitations are acknowledged by rules for fairly (...)
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  47. Emotions in the Wild: The Situated Perspective on Emotion.Paul E. Griffiths & Andrea Scarantino - 2005 - 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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  48. 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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  49. 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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  50. Gdzie jesteś, HAL?Jarek Gryz - 2013 - Przegląd Filozoficzny 22 (2):167-184.
    Sztuczna inteligencja pojawiła się jako dziedzina badawcza ponad 60 lat temu. Po spektakularnych sukcesach na początku jej istnienia oczekiwano pojawienia się maszyn myślących w ciągu kilku lat. Prognoza ta zupełnie się nie sprawdziła. Nie dość, że maszyny myślącej dotąd nie zbudowano, to nie ma zgodności wśród naukowców czym taka maszyna miałaby się charakteryzować ani nawet czy warto ją w ogóle budować. W artykule tym postaramy się prześledzić dyskusję metodologiczną towarzyszącą sztucznej inteligencji od początku jej istnienia i określić relację między sztuczną (...)
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