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  1. Cognitive penetration and informational encapsulation: Have we been failing the module?Sam Clarke - 2021 - Philosophical Studies 178 (8):2599-2620.
    Jerry Fodor deemed informational encapsulation ‘the essence’ of a system’s modularity and argued that human perceptual processing comprises modular systems, thus construed. Nowadays, his conclusion is widely challenged. Often, this is because experimental work is seen to somehow demonstrate the cognitive penetrability of perceptual processing, where this is assumed to conflict with the informational encapsulation of perceptual systems. Here, I deny the conflict, proposing that cognitive penetration need not have any straightforward bearing on the conjecture that perceptual processing is composed (...)
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  2. An Externalist Theory of Social Understanding: Interaction, Psychological Models, and the Frame Problem.Axel Seemann - 2021 - Review of Philosophy and Psychology:1-25.
    I put forward an externalist theory of social understanding. On this view, psychological sense making takes place in environments that contain both agent and interpreter. The spatial structure of such environments is social, in the sense that its occupants locate its objects by an exercise in triangulation relative to each of their standpoints. This triangulation is achieved in intersubjective interaction and gives rise to a triadic model of the social mind. This model can then be used to make sense of (...)
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  3. Equivalence of the Frame and Halting Problems.Eric Dietrich & Chris Fields - 2020 - Algorithms 13 (175):1-9.
    The open-domain Frame Problem is the problem of determining what features of an open task environment need to be updated following an action. Here we prove that the open-domain Frame Problem is equivalent to the Halting Problem and is therefore undecidable. We discuss two other open-domain problems closely related to the Frame Problem, the system identification problem and the symbol-grounding problem, and show that they are similarly undecidable. We then reformulate the Frame Problem as a quantum decision problem, and show (...)
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  4. Updating the Frame Problem for Artificial Intelligence Research.Lisa Miracchi - 2020 - Journal of Artificial Intelligence and Consciousness 7 (2):217-230.
    The Frame Problem is the problem of how one can design a machine to use information so as to behave competently, with respect to the kinds of tasks a genuinely intelligent agent can reliably, effectively perform. I will argue that the way the Frame Problem is standardly interpreted, and so the strategies considered for attempting to solve it, must be updated. We must replace overly simplistic and reductionist assumptions with more sophisticated and plausible ones. In particular, the standard interpretation assumes (...)
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  5. O "Frame Problem": a sensibilidade ao contexto como um desafio para teorias representacionais da mente.Carlos Barth - 2019 - Dissertation, Federal University of Minas Gerais
    Context sensitivity is one of the distinctive marks of human intelligence. Understanding the flexible way in which humans think and act in a potentially infinite number of circumstances, even though they’re only finite and limited beings, is a central challenge for the philosophy of mind and cognitive science, particularly in the case of those using representational theories. In this work, the frame problem, that is, the challenge of explaining how human cognition efficiently acknowledges what is relevant from what is not (...)
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  6. Why Emotions Do Not Solve the Frame Problem.Madeleine Ransom - 2016 - In Vincent C. Müller (ed.), Fundamental Issues of Artificial Intelligence. Cham: Springer. pp. 353-365.
    Attempts to engineer a generally intelligent artificial agent have yet to meet with success, largely due to the (intercontext) frame problem. Given that humans are able to solve this problem on a daily basis, one strategy for making progress in AI is to look for disanalogies between humans and computers that might account for the difference. It has become popular to appeal to the emotions as the means by which the frame problem is solved in human agents. The purpose of (...)
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  7. Analogical Cognition: Applications in Epistemology and the Philosophy of Mind and Language.Theodore Bach - 2012 - Philosophy Compass 7 (5):348-360.
    Analogical cognition refers to the ability to detect, process, and learn from relational similarities. The study of analogical and similarity cognition is widely considered one of the ‘success stories’ of cognitive science, exhibiting convergence across many disciplines on foundational questions. Given the centrality of analogy to mind and knowledge, it would benefit philosophers investigating topics in epistemology and the philosophies of mind and language to become familiar with empirical models of analogical cognition. The goal of this essay is to describe (...)
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  8. Context-switching and responsiveness to real relevance.Erik Rietveld - 2012 - In Julian Kiverstein & Michael Wheeler (eds.), Heidegger and Cognitive Science. Palgrave-Macmillan.
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  9. Why Dreyfus’ Frame Problem Argument Cannot Justify Anti-Representational AI.Nancy Salay - 2009 - In S. Ohlsson & R. Catrambone (ed.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.
    Hubert Dreyfus has argued recently that the frame problem, discussion of which has fallen out of favour in the AI community, is still a deal breaker for the majority of AI projects, despite the fact that the logical version of it has been solved. (Shanahan 1997, Thielscher 1998). Dreyfus thinks that the frame problem will disappear only once we abandon the Cartesian foundations from which it stems and adopt, instead, a thoroughly Heideggerian model of cognition, in particular one that does (...)
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  10. Robotlar ve planlama.Varol Akman & Erkan Tin - 1993 - Elektrik Mühendisliği 391:37-43.
    Planlama --- bir amaca ulaşmak üzere bir aksiyonlar bütünü tasarlamak --- yapay zekadaki en temel problemlerden biridir. Bu yazıda, robotikte planlama konusuna mantıkçı (logicist) yaklaşım ele alınmaktadır. [Planning --- devising a plan of action to reach a given goal --- is a fundamental problem in AI. This paper reviews the logicist approach to planning in robotics.].
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  11. Computing with causal theories.Erkan Tin & Varol Akman - 1992 - International Journal of Pattern Recognition and Artificial Intelligence 6 (4):699-730.
    Formalizing commonsense knowledge for reasoning about time has long been a central issue in AI. It has been recognized that the existing formalisms do not provide satisfactory solutions to some fundamental problems, viz. the frame problem. Moreover, it has turned out that the inferences drawn do not always coincide with those one had intended when one wrote the axioms. These issues call for a well-defined formalism and useful computational utilities for reasoning about time and change. Yoav Shoham of Stanford University (...)
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  12. Bruce D'Ambrosio, Qualitative Process Theory Using Linguistic Variables[REVIEW]Varol Akman - 1991 - ACM SIGART Bulletin 2 (2):25-27.
    Ken Forbus's Qualitative Process Theory (QPT) is a popular theory for reasoning about the physical aspects of the daily world. Qualitative Process Theory Using Linguistic Variables by Bruce D'Ambrosio (Springer-Verlag, New York, 1989) is an attempt to fill some gaps in QPT.
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