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  1. Explainable AI is Indispensable in Areas Where Liability is an Issue.Nelson Brochado - manuscript
    Certain animals and, in particular, humans have always been curious about the mysteries of the world. We have always shown interest in exploring the unknown, so that it becomes known. The necessity of discovery is likely inherent to our nature and it is possibly related to our limited time. Throughout the years, we have developed ways of communicating with each other and other animals. In particular, we have developed ways of saving and transferring information and knowledge. We have also developed (...)
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  2. 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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  3. AISC 17 Talk: The Explanatory Problems of Deep Learning in Artificial Intelligence and Computational Cognitive Science: Two Possible Research Agendas.Antonio Lieto - 2018 - In Proceedings of AISC 2017.
    Endowing artificial systems with explanatory capacities about the reasons guiding their decisions, represents a crucial challenge and research objective in the current fields of Artificial Intelligence (AI) and Computational Cognitive Science [Langley et al., 2017]. Current mainstream AI systems, in fact, despite the enormous progresses reached in specific tasks, mostly fail to provide a transparent account of the reasons determining their behavior (both in cases of a successful or unsuccessful output). This is due to the fact that the classical problem (...)
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  4. Empiricism Without Magic: Transformational Abstraction in Deep Convolutional Neural Networks.Cameron Buckner - 2018 - Synthese (12):1-34.
    In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often appealing to (...)
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  5. Philosophy and Theory of Artificial Intelligence, 3–4 October (Report on PT-AI 2011).Vincent C. Müller - 2011 - The Reasoner 5 (11):192-193.
    Report for "The Reasoner" on the conference "Philosophy and Theory of Artificial Intelligence", 3 & 4 October 2011, Thessaloniki, Anatolia College/ACT, http://www.pt-ai.org. --- Organization: Vincent C. Müller, Professor of Philosophy at ACT & James Martin Fellow, Oxford http://www.sophia.de --- Sponsors: EUCogII, Oxford-FutureTech, AAAI, ACM-SIGART, IACAP, ECCAI.
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  6. A Brief History of Connectionism and its Psychological Implications.S. F. Walker - 1990 - AI and Society 4 (1):17-38.
    Critics of the computational connectionism of the last decade suggest that it shares undesirable features with earlier empiricist or associationist approaches, and with behaviourist theories of learning. To assess the accuracy of this charge the works of earlier writers are examined for the presence of such features, and brief accounts of those found are given for Herbert Spencer, William James and the learning theorists Thorndike, Pavlov and Hull. The idea that cognition depends on associative connections among large networks of neurons (...)
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  7. The Connectionist Mind: A Study of Hayekian Psychology.Barry Smith - 1997 - In Stephen F. Frowen (ed.), Hayek: Economist and Social Philosopher: A Critical Retrospect. London: St. Martin's Press. pp. 9-29.
    In his book The Sensory Order, Hayek anticipates many of the central ideas behind what we now call the connectionist paradigm, and develops on this basis a theory of the workings of the human mind that extends the thinking of Hume and Mach. He shows that the idea of neural networks is can be applied not only in psychology and neurology but also in the sphere of economics. For the mind, from the perspective of The Sensory Order, is a dynamic, (...)
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Connectionism and Compositionality
  1. Higher-Level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind.Antonio Lieto, William G. Kennedy, Christian Lebiere, Oscar Romero, Niels Taatgen & Robert West - forthcoming - Procedia Computer Science.
    In his famous 1982 paper, Allen Newell [22, 23] introduced the notion of knowledge level to indicate a level of analysis, and prediction, of the rational behavior of a cognitive arti cial agent. This analysis concerns the investigation about the availability of the agent knowledge, in order to pursue its own goals, and is based on the so-called Rationality Principle (an assumption according to which "an agent will use the knowledge it has of its environment to achieve its goals" [22, (...)
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  2. 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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  3. Systematicity and Conceptual Pluralism.Fernando Martinez-Manrique - 2014 - In Paco Calvo John Symons (ed.), The Architecture of Cognition: Rethinking Fodor and Pylyshyn's Systematicity Challenge. MIT Press. pp. 305-334.
    The systematicity argument only challenges connectionism if systematicity is a general property of cognition. I examine this thesis in terms of properties of concepts. First, I propose that Evans's Generality Constraint only applies to attributions of belief. Then I defend a variety of conceptual pluralism, arguing that concepts share two fundamental properties related to centrality and belief-attribution, and contending that there are two kinds of concepts that differ in their compositional properties. Finally, I rely on Dual Systems Theory and on (...)
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Representation in Connectionism
  1. There’s Plenty of Boole at the Bottom: A Reversible CA Against Information Entropy.Francesco Berto, Jacopo Tagliabue & Gabriele Rossi - 2016 - Minds and Machines 26 (4):341-357.
    “There’s Plenty of Room at the Bottom”, said the title of Richard Feynman’s 1959 seminal conference at the California Institute of Technology. Fifty years on, nanotechnologies have led computer scientists to pay close attention to the links between physical reality and information processing. Not all the physical requirements of optimal computation are captured by traditional models—one still largely missing is reversibility. The dynamic laws of physics are reversible at microphysical level, distinct initial states of a system leading to distinct final (...)
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  2. Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation.Antonio Lieto, Antonio Chella & Marcello Frixione - 2017 - Biologically Inspired Cognitive Architectures 19:1-9.
    During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...)
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  3. 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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  4. Robot Pain.Simon van Rysewyk - 2014 - International Journal of Synthetic Emotions 4 (2):22-33.
    Functionalism of robot pain claims that what is definitive of robot pain is functional role, defined as the causal relations pain has to noxious stimuli, behavior and other subjective states. Here, I propose that the only way to theorize role-functionalism of robot pain is in terms of type-identity theory. I argue that what makes a state pain for a neuro-robot at a time is the functional role it has in the robot at the time, and this state is type identical (...)
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  5. The Waning of Materialism. Edited by R. Koons and G. Bealer. (OUP 2010). [REVIEW]David Yates - 2012 - Philosophical Quarterly 62 (247):420-422.
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  6. Bayesian Models and Simulations in Cognitive Science.Giuseppe Boccignone & Roberto Cordeschi - 2007 - Workshop Models and Simulations 2, Tillburg, NL.
    Bayesian models can be related to cognitive processes in a variety of ways that can be usefully understood in terms of Marr's distinction among three levels of explanation: computational, algorithmic and implementation. In this note, we discuss how an integrated probabilistic account of the different levels of explanation in cognitive science is resulting, at least for the current research practice, in a sort of unpredicted epistemological shift with respect to Marr's original proposal.
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The Connectionist/Classical Debate
  1. Review of The Emotion Machine by Marvin Minsky (2007).Michael Starks - 2016 - In Suicidal Utopian Delusions in the 21st Century: Philosophy, Human Nature and the Collapse of Civilization-- Articles and Reviews 2006-2017 2nd Edition Feb 2018. Michael Starks. pp. 627.
    Dullest book by a major scientist I have ever read. I suppose if you know almost nothing about cognition or AI research you might find this book useful. For anyone else it is a horrific bore. There are hundreds of books in cog sci, robotics, AI, evolutionary psychology and philosophy offering far more info and insight on cognition than this one. Minsky is a top rate senior scientist but it barely shows here. He has alot of good references but they (...)
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  2. 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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Subsymbolic Computation
  1. The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments.Antonio Lieto, Christian Lebiere & Alessandro Oltramari - 2017 - Cognitive Systems Research:1-42.
    In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build arti cial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the (...)
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  2. HeX and the Single Anthill: Playing Games with Aunt Hillary.J. M. Bishop, S. J. Nasuto, T. Tanay, E. B. Roesch & M. C. Spencer - 2015 - In Vincent Müller (ed.), Fundamental Issues of Artificial Intelligence. Springer. pp. 367-389.
    In a reflective and richly entertaining piece from 1979, Doug Hofstadter playfully imagined a conversation between ‘Achilles’ and an anthill (the eponymous ‘Aunt Hillary’), in which he famously explored many ideas and themes related to cognition and consciousness. For Hofstadter, the anthill is able to carry on a conversation because the ants that compose it play roughly the same role that neurons play in human languaging; unfortunately, Hofstadter’s work is notably short on detail suggesting how this magic might be achieved1. (...)
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  3. Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different Levels of Representation.Antonio Lieto, Antonio Chella & Marcello Frixione - 2017 - Biologically Inspired Cognitive Architectures 19:1-9.
    During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...)
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  4. Review of Fenstad's "Grammar, Geometry & Brain". [REVIEW]Erich Rast - 2014 - Studia Logica 102 (1):219-223.
    In this small book logician and mathematician Jens Erik Fenstad addresses some of the most important foundational questions of linguistics: What should a theory of meaning look like and how might we provide the missing link between meaning theory and our knowledge of how the brain works? The author’s answer is twofold. On the one hand, he suggests that logical semantics in the Montague tradition and other broadly conceived symbolic approaches do not suffice. On the other hand, he does not (...)
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  5. Is There a Future for AI Without Representation?Vincent C. Müller - 2007 - Minds and Machines 17 (1):101-115.
    This paper investigates the prospects of Rodney Brooks’ proposal for AI without representation. It turns out that the supposedly characteristic features of “new AI” (embodiment, situatedness, absence of reasoning, and absence of representation) are all present in conventional systems: “New AI” is just like old AI. Brooks proposal boils down to the architectural rejection of central control in intelligent agents—Which, however, turns out to be crucial. Some of more recent cognitive science suggests that we might do well to dispose of (...)
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Philosophy of Connectionism, Misc
  1. Advice Seeking Network Structures and the Learning Organization.Jarle Aarstad, Marcus Selart & Sigurd Troye - 2011 - Problems and Perspectives in Management 9 (2):44-51.
    Organizational learning can be described as a transfer of individuals’ cognitive mental models to shared mental models. Employees, seeking the same colleagues for advice, are structurally equivalent, and the aim of the paper is to study if the concept can act as a conduit for organizational learning. It is argued that the mimicking of colleagues’ advice seeking structures will induce structural equivalence and transfer the accuracy of individuals’ cognitive mental models to shared mental models. Taking a dyadic level of analysis (...)
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  2. Reduction and Levels of Explanation in Connectionism.John Sutton - 1995 - In P. Slezak, T. Caelli & R. Clark (eds.), Perspectives on cognitive science: theories, experiments, and foundations. Ablex. pp. 347-368.
    Recent work in the methodology of connectionist explanation has I'ocrrsccl on the notion of levels of explanation. Specific issucs in conncctionisrn hcrc intersect with rvider areas of debate in the philosophy of psychology and thc philosophy of science generally. The issues I raise in this chapter, then, are not unique to cognitive science; but they arise in new and important contexts when connectionism is taken seriously as a model of cognition. The general questions are the relation between levels and the (...)
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  3. Filling the Gaps: Hume and Connectionism on the Continued Existence of Unperceived Objects.Mark Collier - 1999 - Hume Studies 25 (1 and 2):155-170.
    In Book I, part iv, section 2 of the Treatise, "Of scepticism with regard to the senses," Hume presents two different answers to the question of how we come to believe in the continued existence of unperceived objects. He rejects his first answer shortly after its formulation, and the remainder of the section articulates an alternative account of the development of the belief. The account that Hume adopts, however, is susceptible to a number of insurmountable objections, which motivates a reassessment (...)
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