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  1. 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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  2. Short-Circuiting the Definition of Mathematical Knowledge for an Artificial General Intelligence.Samuel Alexander - forthcoming - Lecture Notes in Computer Science.
    We propose that, for the purpose of studying theoretical properties of the knowledge of an agent with Artificial General Intelligence (that is, the knowledge of an AGI), a pragmatic way to define such an agent’s knowledge (restricted to the language of Epistemic Arithmetic, or EA) is as follows. We declare an AGI to know an EA-statement φ if and only if that AGI would include φ in the resulting enumeration if that AGI were commanded: “Enumerate all the EA-sentences which you (...)
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  3. Computer Simulations, Machine Learning and the Laplacean Demon: Opacity in the Case of High Energy Physics.Florian J. Boge & Paul Grünke - forthcoming - In Andreas Kaminski, Michael Resch & Petra Gehring (eds.), The Science and Art of Simulation II.
    In this paper, we pursue three general aims: (I) We will define a notion of fundamental opacity and ask whether it can be found in High Energy Physics (HEP), given the involvement of machine learning (ML) and computer simulations (CS) therein. (II) We identify two kinds of non-fundamental, contingent opacity associated with CS and ML in HEP respectively, and ask whether, and if so how, they may be overcome. (III) We address the question of whether any kind of opacity, contingent (...)
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  4. Body in Mind, Mind in Body: Developmental Perspectives on Embodiment and Consciousness.W. F. Overton, U. Mueller & J. Newman (eds.) - forthcoming - Erlbaum.
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  5. 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. Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure.Samuel Alexander & Bill Hibbard - 2021 - Journal of Artificial General Intelligence 12 (1):1-25.
    In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard’s idea should actually be considered as two separate ideas: first, that the intelligence of such agents can be measured based on the growth rates of the runtimes of the competitors that they defeat; and second, one specific (somewhat arbitrary) method for measuring said growth rates. Whereas Hibbard’s intelligence measure is based on the latter growth-rate-measuring method, we survey other methods (...)
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  7. Making AI Meaningful Again.Jobst Landgrebe & Barry Smith - 2021 - Synthese 198 (March):2061-2081.
    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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  8. The Archimedean Trap: Why Traditional Reinforcement Learning Will Probably Not Yield AGI.Samuel Allen Alexander - 2020 - Journal of Artificial General Intelligence 11 (1):70-85.
    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 probably will not lead to AGI. We indicate two possible ways (...)
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  9. Shared Decision‐Making and Maternity Care in the Deep Learning Age: Acknowledging and Overcoming Inherited Defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2020 - Journal of Evaluation in Clinical Practice.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in (...)
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  10. Post-Turing Methodology: Breaking the Wall on the Way to Artificial General Intelligence.Albert Efimov - 2020 - Lecture Notes in Computer Science 12177.
    This article offers comprehensive criticism of the Turing test and develops quality criteria for new artificial general intelligence (AGI) assessment tests. It is shown that the prerequisites A. Turing drew upon when reducing personality and human consciousness to “suitable branches of thought” re-flected the engineering level of his time. In fact, the Turing “imitation game” employed only symbolic communication and ignored the physical world. This paper suggests that by restricting thinking ability to symbolic systems alone Turing unknowingly constructed “the wall” (...)
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  11. There is No General AI.Jobst Landgrebe & Barry Smith - 2020 - arXiv.
    The goal of creating Artificial General Intelligence (AGI) – or in other words of creating Turing machines (modern computers) that can behave in a way that mimics human intelligence – has occupied AI researchers ever since the idea of AI was first proposed. One common theme in these discussions is the thesis that the ability of a machine to conduct convincing dialogues with human beings can serve as at least a sufficient criterion of AGI. We argue that this very ability (...)
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  12. Ontology and Cognitive Outcomes.David Limbaugh, Jobst Landgrebe, David Kasmier, Ronald Rudnicki, James Llinas & Barry Smith - 2020 - Journal of Knowledge Structures and Systems 1 (1): 3-22.
    The term ‘intelligence’ as used in this paper refers to items of knowledge collected for the sake of assessing and maintaining national security. The intelligence community (IC) of the United States (US) is a community of organizations that collaborate in collecting and processing intelligence for the US. The IC 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 (...)
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  13. Cosa significano Paraconsistente, Indecifrabile, Casuale, Calcolabile e Incompleto? Una recensione di Godel's Way: sfrutta in un mondo indecidibile (Godel's Way: Exploits into an Undecidable World) di Gregory Chaitin, Francisco A Doria, Newton C.A. da Costa 160p (2012) (rivisto 2019).Michael Richard Starks - 2020 - In Benvenuti all'inferno sulla Terra: Bambini, Cambiamenti climatici, Bitcoin, Cartelli, Cina, Democrazia, Diversità, Disgenetica, Uguaglianza, Pirati Informatici, Diritti umani, Islam, Liberalismo, Prosperità, Web, Caos, Fame, Malattia, Violenza, Intellige. Las Vegas, NV, USA: Reality Press. pp. 163-176.
    Nel 'Godel's Way' tre eminenti scienziati discutono questioni come l'indecidibilità, l'incompletezza, la casualità, la computabilità e la paracoerenza. Affronto questi problemi dal punto di vista di Wittgensteinian che ci sono due questioni fondamentali che hanno soluzioni completamente diverse. Ci sono le questioni scientifiche o empiriche, che sono fatti sul mondo che devono essere studiati in modo osservante e filosofico su come il linguaggio può essere usato in modo intelligibilmente (che include alcune domande in matematica e logica), che devono essere decise (...)
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  14. Gli ominoidi o gli androidi distruggeranno la Terra? Una recensione di Come Creare una Mente (How to Create a Mind) di Ray Kurzweil (2012) (recensione rivista nel 2019).Michael Richard Starks - 2020 - In Benvenuti all'inferno sulla Terra: Bambini, Cambiamenti climatici, Bitcoin, Cartelli, Cina, Democrazia, Diversità, Disgenetica, Uguaglianza, Pirati Informatici, Diritti umani, Islam, Liberalismo, Prosperità, Web, Caos, Fame, Malattia, Violenza, Intellige. Las Vegas, NV, USA: Reality Press. pp. 150-162.
    Alcuni anni fa, ho raggiunto il punto in cui di solito posso dire dal titolo di un libro, o almeno dai titoli dei capitoli, quali tipi di errori filosofici saranno fatti e con quale frequenza. Nel caso di opere nominalmente scientifiche queste possono essere in gran parte limitate a determinati capitoli che sono filosofici o cercanodi trarre conclusioni generali sul significato o sul significato a lungoterminedell'opera. Normalmente però le questioni scientifiche di fatto sono generosamente intrecciate con incomprodellami filosofici su ciò (...)
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  15. Gli ominoidi o gli androidi distruggeranno la Terra? Una recensione di Come Creare una Mente (How to Create a Mind) di Ray Kurzweil (2012) (recensione rivista nel 2019).Michael Richard Starks - 2020 - In Benvenuti all'inferno sulla Terra: Bambini, Cambiamenti climatici, Bitcoin, Cartelli, Cina, Democrazia, Diversità, Disgenetica, Uguaglianza, Pirati Informatici, Diritti umani, Islam, Liberalismo, Prosperità, Web, Caos, Fame, Malattia, Violenza, Intellige. Las Vegas, NV, USA: Reality Press. pp. 150-162.
    Alcuni anni fa, ho raggiunto il punto in cui di solito posso dire dal titolo di un libro, o almeno dai titoli dei capitoli, quali tipi di errori filosofici saranno fatti e con quale frequenza. Nel caso di opere nominalmente scientifiche queste possono essere in gran parte limitate a determinati capitoli che sono filosofici o cercanodi trarre conclusioni generali sul significato o sul significato a lungoterminedell'opera. Normalmente però le questioni scientifiche di fatto sono generosamente intrecciate con incomprodellami filosofici su ciò (...)
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  16. कैसे सात Socipaths जो चीन शासन कर रहे हैं विश्व युद्ध तीन और तीन तरीके उन्हें रोकने के लिए How the Seven Sociopaths Who Rule China are Winning World War and Three and Three Ways to Stop Them (2019).Michael Richard Starks - 2020 - In पृथ्वी पर नर्क में आपका स्वागत है: शिशुओं, जलवायु परिवर्तन, बिटकॉइन, कार्टेल, चीन, लोकतंत्र, विविधता, समानता, हैकर्स, मानव अधिकार, इस्लाम, उदारवाद, समृद्धि, वेब, अराजकता, भुखमरी, बीमारी, हिंसा, कृत्रिम बुद्धिमत्ता, युद्ध. Las Vegas, NV , USA: Reality Press. pp. 389-396.
    पहली बात हमें ध्यान में रखना चाहिए कि जब यह कहना है कि चीन यह कहता है या चीन ऐसा करता है, तो हम चीनी लोगों की बात नहीं कर रहे हैं, लेकिन उन सोशियोपैथों की जो सीसीपी (चीनी कम्युनिस्ट पार्टी, अर्थात सात सेनेले सोसोपैथिक सीरियल किलर (एसएसएसएसके) का नियंत्रण करते हैं। सीपी या पोलितब्यूरो के 25 सदस्यों की टंडिंग समिति। मैं हाल ही में कुछ ठेठ वामपंथी नकली समाचार कार्यक्रमों को देखा (सुंदर बहुत ही तरह एक ही तरह से (...)
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  17. 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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  18. Chess, Artificial Intelligence, and Epistemic Opacity.Paul Grünke - 2019 - Információs Társadalom 19 (4):7--17.
    In 2017 AlphaZero, a neural network-based chess engine shook the chess world by convincingly beating Stockfish, the highest-rated chess engine. In this paper, I describe the technical differences between the two chess engines and based on that, I discuss the impact of the modeling choices on the respective epistemic opacities. I argue that the success of AlphaZero’s approach with neural networks and reinforcement learning is counterbalanced by an increase in the epistemic opacity of the resulting model.
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  19. Present Scenario of Fog Computing and Hopes for Future Research.G. KSoni, B. Hiren Bhatt & P. Dhaval Patel - 2019 - International Journal of Computer Sciences and Engineering 7 (9).
    According to the forecast that billions of devices will get connected to the Internet by 2020. All these devices will produce a huge amount of data that will have to be handled rapidly and in a feasible manner. It will become a challenge for real-time applications to handle this huge data while considering security issues as well as time constraints. The main highlights of cloud computing are on-demand service and scalability; therefore the data generated from IoT devices are generally handled (...)
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  20. 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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  21. 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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  22. A Study on Fog Computing Environment Mobility and Migration.R. J. Pedro - 2018 - 22nd International Conference Electronics 22.
    Cloud Computing paradigm has reached a high degree of popularity among all kinds of computer users, but it may not be suitable for mobile devices as they need computing power to be as close as possible to data sources in order to reduce delays. This paper focuses on achieving mathematical models for users moving around and proposes an overlay mobility model for Fog Data Centres based on traditional wireless mobility models aimed at better allocating edge computing resources to client demands. (...)
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  23. 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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  24. 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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  25. 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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  26. 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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  27. 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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  28. 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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  29. 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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  30. 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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  31. 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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  32. 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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  33. 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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  34. 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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  35. Kees van Deemter: Not Exactly: In Praise of Vagueness: Oxford University Press, Oxford, 2010, Xvi+341, $29.95, ISBN: 0-199-5459-01. [REVIEW]Patrick Allo - 2012 - Minds and Machines 22 (1):41-45.
  36. 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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  37. 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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  38. Representing Concepts in Formal Ontologies: Compositionality Vs. Typicality Effects".Marcello Frixione & Antonio Lieto - 2012 - Logic and Logical Philosophy 21 (4):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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  39. 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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  40. 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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  41. 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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  42. 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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  43. Don Ross Et Al. (Eds.), Distributed Cognition and the Will. [REVIEW]Federico Faroldi - 2011 - Minds and Machines 21 (1):115-118.
  44. Bica and Beyond: How Biology and Anomalies Together Contribute to Flexible Cognition.Donald Perlis - 2010 - International Journal of Machine Consciousness 2 (2):261-271.
  45. 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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  46. 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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  47. 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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  48. “All You Can Eat” Ontology-Building: Feeding Wikipedia to Cyc.Samuel Sarjant, Catherine Legg, Olena Medelyan & Michael Robinson - 2009 - IEEE/WIC/ACM International Conference on Web Intelligence (WI-09), 15 – 18 September 2009 Università Degli Studi di Milano Bicocca, Milano, Italy.
    In order to achieve genuine web intelligence, building some kind of large general machine-readable conceptual scheme (i.e. ontology) seems inescapable. Yet the past 20 years have shown that manual ontology-building is not practicable. The recent explosion of free user-supplied knowledge on the Web has led to great strides in automatic ontology building, but quality-control is still a major issue. Ideally one should automatically build onto an already intelligent base. We suggest that the long-running Cyc project is able to assist here. (...)
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  49. The Feeling Body: Towards an Enactive Approach to Emotion.Giovanna Colombetti & Evan Thompson - 2008 - In W. F. Overton, U. Müller & J. L. Newman (eds.), 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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  50. 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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