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  1. Why cognitive science is not formalized folk psychology.Martin Pickering & Nick Chater - 1995 - Minds and Machines 5 (3):309-337.
    It is often assumed that cognitive science is built upon folk psychology, and that challenges to folk psychology are therefore challenges to cognitive science itself. We argue that, in practice, cognitive science and folk psychology treat entirely non-overlapping domains: cognitive science considers aspects of mental life which do not depend on general knowledge, whereas folk psychology considers aspects of mental life which do depend on general knowledge. We back up our argument on theoretical grounds, and also illustrate the separation between (...)
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  • The knowledge level.Allen Newell - 1982 - Artificial Intelligence 18 (1):81-132.
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  • A situated view of representation and control.Stanley J. Rosenschein & Leslie Pack Kaelbling - 1995 - Artificial Intelligence 73 (1-2):149-73.
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  • The frame problem and theories of belief.Scott Hendricks - 2006 - Philosophical Studies 129 (2):317-33.
    The frame problem is the problem of how we selectively apply relevant knowledge to particular situations in order to generate practical solutions. Some philosophers have thought that the frame problem can be used to rule out, or argue in favor of, a particular theory of belief states. But this is a mistake. Sentential theories of belief are no better or worse off with respect to the frame problem than are alternative theories of belief, most notably, the “map” theory of belief.
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  • The frame problem: An AI fairy tale. [REVIEW]Kevin B. Korb - 1998 - Minds and Machines 8 (3):317-351.
    I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by (...)
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  • Framing the frame problem.Eric Lormand - 1990 - Synthese 82 (3):353-74.
    The frame problem is widely reputed among philosophers to be one of the deepest and most difficult problems of cognitive science. This paper discusses three recent attempts to display this problem: Dennett's problem of ignoring obviously irrelevant knowledge, Haugeland's problem of efficiently keeping track of salient side effects, and Fodor's problem of avoiding the use of kooky concepts. In a negative vein, it is argued that these problems bear nothing but a superficial similarity to the frame problem of AI, so (...)
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  • On the potential of non-classical constituency.W. F. G. Haselager - 1999 - Acta Analytica 144:23-42.
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  • Directions in Connectionist Research: Tractable Computations Without Syntactically Structured Representations.Jonathan Waskan & William Bechtel - 1997 - Metaphilosophy 28 (1‐2):31-62.
    Figure 1: A pr ototyp ical exa mple of a three-layer feed forward network, used by Plunkett and M archm an (1 991 ) to simulate learning the past-tense of En glish verbs. The inpu t units encode representations of the three phonemes of the present tense of the artificial words used in this simulation. Th e netwo rk is trained to produce a representation of the phonemes employed in the past tense form and the suffix (/d/, /ed/, or /t/) (...)
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  • Connectionism, systematicity, and the frame problem.W. F. G. Haselager & J. F. H. Van Rappard - 1998 - Minds and Machines 8 (2):161-179.
    This paper investigates connectionism's potential to solve the frame problem. The frame problem arises in the context of modelling the human ability to see the relevant consequences of events in a situation. It has been claimed to be unsolvable for classical cognitive science, but easily manageable for connectionism. We will focus on a representational approach to the frame problem which advocates the use of intrinsic representations. We argue that although connectionism's distributed representations may look promising from this perspective, doubts can (...)
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  • Form and content in semantics.Y. Wilks - 1990 - Synthese 82 (3):329-51.
    This paper continues a strain of intellectual complaint against the presumptions of certain kinds of formal semantics (the qualification is important) and their bad effects on those areas of artificial intelligence concerned with machine understanding of human language. After some discussion of the use of the term epistemology in artificial intelligence, the paper takes as a case study the various positions held by McDermott on these issues and concludes, reluctantly, that, although he has reversed himself on the issue, there was (...)
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  • Ascribing mental qualities to machines.John McCarthy - 1979 - In Martin Ringle (ed.), Philosophical Perspectives in Artificial Intelligence. Humanities Press.
    Ascribing mental qualities like beliefs, intentions and wants to a machine is sometimes correct if done conservatively and is sometimes necessary to express what is known about its state. We propose some new definitional tools for this: definitions relative to an approximate theory and second order structural definitions.
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  • The search hypothesis of emotions.Dylan Evans - 2002 - British Journal for the Philosophy of Science 53 (4):497-509.
    Many philosophers and psychologists now argue that emotions play a vital role in reasoning. This paper explores one particular way of elucidating how emotions help reason which may be dubbed ?the search hypothesis of emotion?. After outlining the search hypothesis of emotion and dispensing with a red herring that has marred previous statements of the hypothesis, I discuss two alternative readings of the search hypothesis. It is argued that the search hypothesis must be construed as an account of what emotions (...)
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  • Framing the Predictive Mind: Why We Should Think Again About Dreyfus.Jack Reynolds - 2024 - Phenomenology and the Cognitive Sciences.
    In this paper I return to Hubert Dreyfus’ old but influential critique of artificial intelligence, redirecting it towards contemporary predictive processing models of the mind (PP). I focus on Dreyfus’ arguments about the “frame problem” for artificial cognitive systems, and his contrasting account of embodied human skills and expertise. The frame problem presents as a prima facie problem for practical work in AI and robotics, but also for computational views of the mind in general, including for PP. Indeed, some of (...)
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  • After Turing: How Philosophy Migrated to the AI Lab.Lydia H. Liu - 2023 - Critical Inquiry 50 (1):2-30.
    What happens to philosophy when philosophical activities migrate to the AI lab? My article explores the philosophical work that has gone into the machine simulations of language and understanding after Alan Turing. The early experiments by AI practitioners such as Karen Spärck Jones, Richard Richens, Yorick Wilks, and others at the Cambridge Language Research Unit (CLRU) led to the creation of the machine interlingua, semantic networks, and other technological innovations central to the development of AI in the 1950s–1970s. I attempt (...)
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  • Constructing Condensed Memories in Functorial Time.Shanna Dobson & Chris Fields - manuscript
    If episodic memory is constructive, experienced time is also a construct. We develop an event-based formalism that replaces the traditional objective, agent-independent notion of time with a constructive, agent-dependent notion of time. We show how to make this agent-dependent time entropic and hence well-defined. We use sheaf-theoretic techniques to render agent-dependent time functorial and to construct episodic memories as sequences of observed and constructed events with well-defined limits that maximize the consistency of categorizations assigned to objects appearing in memories. We (...)
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  • Why Machines Will Never Rule the World: Artificial Intelligence without Fear by Jobst Landgrebe & Barry Smith (Book review). [REVIEW]Walid S. Saba - 2022 - Journal of Knowledge Structures and Systems 3 (4):38-41.
    Whether it was John Searle’s Chinese Room argument (Searle, 1980) or Roger Penrose’s argument of the non-computable nature of a mathematician’s insight – an argument that was based on Gödel’s Incompleteness theorem (Penrose, 1989), we have always had skeptics that questioned the possibility of realizing strong Artificial Intelligence (AI), or what has become known by Artificial General Intelligence (AGI). But this new book by Landgrebe and Smith (henceforth, L&S) is perhaps the strongest argument ever made against strong AI. It is (...)
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  • Attention: a descriptive taxonomy.Antonios Kaldas - 2022 - History and Philosophy of the Life Sciences 44 (4):1-27.
    The term attention has been used to mean so many different things that some have despaired of it being useful at all. This paper is devoted to bringing a modicum of order to the chaos through the time-honored device of categorization. The chief purpose of this paper is to introduce a comprehensive descriptive taxonomy of the nuanced ways the term attention may be employed. It is presented in table form, followed by elucidations and illustrations of each of its items. But (...)
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  • 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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  • Is Experience Stored in the Brain? A Current Model of Memory and the Temporal Metaphysic of Bergson.Stephen Robbins - 2021 - Axiomathes 31:15-43.
    In discussion on consciousness and the hard problem, there is an unquestioned background assumption, namely, our experience is stored in the brain. Yet Bergson (1896) argued that this very question, “Is experience stored in the brain?” is the critical issue in the problem of consciousness. His examination of then-current memory research led him, save for motor or procedural memory, to a “no” answer. Others, for example Sheldrake (2012), have continued this negative assessment of the research findings. So, has this assumption (...)
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  • Predictive processing and relevance realization: exploring convergent solutions to the frame problem.Brett P. Andersen, Mark Miller & John Vervaeke - forthcoming - Phenomenology and the Cognitive Sciences:1-22.
    The frame problem refers to the fact that organisms must be able to zero in on relevant aspects of the world and intelligently ignore the vast majority of the world that is irrelevant to their goals. In this paper we aim to point out the connection between two leading frameworks for thinking about how organisms achieve this. Predictive processing is a rapidly growing framework within cognitive science which suggests that organisms assign a high ‘weight’ to relevant aspects of the world, (...)
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  • Somatic Markers, Rhetoric, and Post-truth.José M. Muñoz - 2017 - Frontiers in Psychology 8.
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  • Why Dreyfus’ Frame Problem Argument Cannot Justify Anti-Representational AI.Nancy Salay - 2009 - In N. A. Taatgen & H. van Rijn (eds.), 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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  • Uncharted Aspects of Human Intelligence in Knowledge-Based “Intelligent” Systems.Ronaldo Vigo, Derek E. Zeigler & Jay Wimsatt - 2022 - Philosophies 7 (3):46.
    This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and concept learning, with (...)
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  • Analyzing generalized planning under nondeterminism.Vaishak Belle - 2022 - Artificial Intelligence 307 (C):103696.
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  • Hapoc 2013.Maarten Bullynck & Jean-Baptiste Joinet - unknown
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  • Knowledge-based programs as building blocks for planning.Jorge A. Baier & Sheila A. McIlraith - 2022 - Artificial Intelligence 303 (C):103634.
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  • Natural Deduction, Hybrid Systems and Modal Logics.Andrzej Indrzejczak - 2010 - Dordrecht, Netherland: Springer.
    This book provides a detailed exposition of one of the most practical and popular methods of proving theorems in logic, called Natural Deduction. It is presented both historically and systematically. Also some combinations with other known proof methods are explored. The initial part of the book deals with Classical Logic, whereas the rest is concerned with systems for several forms of Modal Logics, one of the most important branches of modern logic, which has wide applicability.
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  • Dynamic Formal Epistemology.Patrick Girard, Olivier Roy & Mathieu Marion (eds.) - 2010 - Berlin, Germany: Springer.
    This volume is a collation of original contributions from the key actors of a new trend in the contemporary theory of knowledge and belief, that we call “dynamic epistemology”. It brings the works of these researchers under a single umbrella by highlighting the coherence of their current themes, and by establishing connections between topics that, up until now, have been investigated independently. It also illustrates how the new analytical toolbox unveils questions about the theory of knowledge, belief, preference, action, and (...)
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  • Proving Cleanthes wrong.Laureano Luna - 2021 - Journal of Applied Logic 8 (3):707-736.
    Hume’s famous character Cleanthes claims that there is no difficulty in explaining the existence of causal chains with no first cause since in them each item is causally explained by its predecessor. Relying on logico-mathematical resources, we argue for two theses: (1) if the existence of Cleanthes’ chain can be explained at all, it must be explained by the fact that the causal law ruling it is in force, and (2) the fact that such a causal law is in force (...)
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  • Is attention both necessary and sufficient for consciousness?Antonios Kaldas - 2019 - Dissertation, Macquarie University
    Is attention both necessary and sufficient for consciousness? Call this central question of this treatise, “Q.” We commonly have the experience of consciously paying attention to something, but is it possible to be conscious of something you are not attending to, or to attend to something of which you are not conscious? Where might we find examples of these? This treatise is a quest to find an answer to Q in two parts. Part I reviews the foundations upon which the (...)
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  • Intencionalidad, conciencia y realidad.Julio César Armero San José - 2020 - Endoxa 46:193.
    Esa rareza que llamamos conciencia, lo más misterioso que hay en el universo, procedería de darse en ella la conjunción de dos cosas totalmente distintas: por un lado, el acto de referirse a algo y ese algo a lo cual se refiere. A esta forma de relación es a lo que tradicionalmente se ha denominado “intencionalidad” de la conciencia.
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  • Book: Cognitive Design for Artificial Minds.Antonio Lieto - 2021 - London, UK: Routledge, Taylor & Francis Ltd.
    Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...)
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  • Probabilistic reasoning about epistemic action narratives.Fabio Aurelio D'Asaro, Antonis Bikakis, Luke Dickens & Rob Miller - 2020 - Artificial Intelligence 287 (C):103352.
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  • Regression and progression in stochastic domains.Vaishak Belle & Hector J. Levesque - 2020 - Artificial Intelligence 281 (C):103247.
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  • Artificial systems with moral capacities? A research design and its implementation in a geriatric care system.Catrin Misselhorn - 2020 - Artificial Intelligence 278 (C):103179.
    The development of increasingly intelligent and autonomous technologies will eventually lead to these systems having to face morally problematic situations. This gave rise to the development of artificial morality, an emerging field in artificial intelligence which explores whether and how artificial systems can be furnished with moral capacities. This will have a deep impact on our lives. Yet, the methodological foundations of artificial morality are still sketchy and often far off from possible applications. One important area of application of artificial (...)
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  • Approximate verification of strategic abilities under imperfect information.Wojciech Jamroga, Michał Knapik, Damian Kurpiewski & Łukasz Mikulski - 2019 - Artificial Intelligence 277 (C):103172.
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  • Reasoning about discrete and continuous noisy sensors and effectors in dynamical systems.Vaishak Belle & Hector J. Levesque - 2018 - Artificial Intelligence 262 (C):189-221.
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  • Resolving distributed knowledge.Thomas Ågotnes & Yì N. Wáng - 2017 - Artificial Intelligence 252 (C):1-21.
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  • The scope and limits of simulation in automated reasoning.Ernest Davis & Gary Marcus - 2016 - Artificial Intelligence 233 (C):60-72.
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  • Semantical considerations on multiagent only knowing.Vaishak Belle & Gerhard Lakemeyer - 2015 - Artificial Intelligence 223 (C):1-26.
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  • Asynchronous knowledge with hidden actions in the situation calculus.Ryan F. Kelly & Adrian R. Pearce - 2015 - Artificial Intelligence 221 (C):1-35.
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  • How to progress a database III.Stavros Vassos & Hector J. Levesque - 2013 - Artificial Intelligence 195 (C):203-221.
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  • John McCarthy's legacy.Leora Morgenstern & Sheila A. McIlraith - 2011 - Artificial Intelligence 175 (1):1-24.
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  • Property persistence in the situation calculus.Ryan F. Kelly & Adrian R. Pearce - 2010 - Artificial Intelligence 174 (12-13):865-888.
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  • From systems to logic in the early development of nonmonotonic reasoning.Erik Sandewall - 2011 - Artificial Intelligence 175 (1):416-427.
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  • Semantics and complexity of recursive aggregates in answer set programming.Wolfgang Faber, Gerald Pfeifer & Nicola Leone - 2011 - Artificial Intelligence 175 (1):278-298.
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  • Non-Markovian control in the Situation Calculus.Alfredo Gabaldon - 2011 - Artificial Intelligence 175 (1):25-48.
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  • M odular- E and the role of elaboration tolerance in solving the qualification problem.Antonis Kakas, Loizos Michael & Rob Miller - 2011 - Artificial Intelligence 175 (1):49-78.
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  • A semantic characterization of a useful fragment of the situation calculus with knowledge.Gerhard Lakemeyer & Hector J. Levesque - 2011 - Artificial Intelligence 175 (1):142-164.
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  • The well-designed logical robot: Learning and experience from observations to the Situation Calculus.Fiora Pirri - 2011 - Artificial Intelligence 175 (1):378-415.
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