Results for 'knowledge representation and reasoning'

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  1. Logic in knowledge representation and reasoning: Central topics via readings.Luis M. Augusto - manuscript
    Logic has been a—disputed—ingredient in the emergence and development of the now very large field known as knowledge representation and reasoning. In this book (in progress), I select some central topics in this highly fruitful, albeit controversial, association (e.g., non-monotonic reasoning, implicit belief, logical omniscience, closed world assumption), identifying their sources and analyzing/explaining their elaboration in highly influential published work.
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  2. Paraconsistent Logics for Knowledge Representation and Reasoning: advances and perspectives.Walter A. Carnielli & Rafael Testa - 2020 - 18th International Workshop on Nonmonotonic Reasoning.
    This paper briefly outlines some advancements in paraconsistent logics for modelling knowledge representation and reasoning. Emphasis is given on the so-called Logics of Formal Inconsistency (LFIs), a class of paraconsistent logics that formally internalize the very concept(s) of consistency and inconsistency. A couple of specialized systems based on the LFIs will be reviewed, including belief revision and probabilistic reasoning. Potential applications of those systems in the AI area of KRR are tackled by illustrating some examples that (...)
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  3. Non classical concept representation and reasoning in formal ontologies.Antonio Lieto - 2012 - Dissertation, Università Degli Studi di Salerno
    Formal ontologies are nowadays widely considered a standard tool for knowledge representation and reasoning in the Semantic Web. In this context, they are expected to play an important role in helping automated processes to access information. Namely: they are expected to provide a formal structure able to explicate the relationships between different concepts/terms, thus allowing intelligent agents to interpret, correctly, the semantics of the web resources improving the performances of the search technologies. Here we take into account (...)
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  4. Dual PECCS: A Cognitive System for Conceptual Representation and Categorization.Antonio Lieto, Daniele Radicioni & Valentina Rho - 2017 - Journal of Experimental and Theoretical Artificial Intelligence 29 (2):433-452.
    In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on (...)
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  5. On the Diagrammatic and Mechanical Representation of Propositions and Reasonings.John Venn - 1880 - Philosophical Magazine 9 (59):1-18.
    Schemes of diagrammatic representation have been so familiarly introduced into logical treatises during the last century or so, that many readers, even of those who have made no professional study of logic, may be supposed to be acquainted with the general nature and object of such devices. Of these schemes one only, viz. that commonly called "Eulerian circles," has met with any general acceptance. A variety of others indeed have been proposed by ingenious and celebrated logicians, several of which (...)
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  6. 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) (...), run, and the results from run time and (eventually) from learning are decompiled to a symbolic description of the knowledge contained in the network. After inspecting this recovered knowledge, a designer would be able to modify the KB and go through the whole cycle of compiling, running, and decompiling again. The central question with which this project is concerned is, therefore, How do we go from a KB to an NN, and back again? We are investigating this question by building tools consisting of a repertoire of language/translation/network types, and trying them on problems in a variety of domains. (shrink)
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  7. Heterogeneous Proxytypes Extended: Integrating Theory-like Representations and Mechanisms with Prototypes and Exemplars.Antonio Lieto - 2018 - In Advances in Intelligent Systems and Computing, Springer. Springer.
    The paper introduces an extension of the proposal according to which conceptual representations in cognitive agents should be intended as heterogeneous proxytypes. The main contribution of this paper is in that it details how to reconcile, under a heterogeneous representational perspective, different theories of typicality about conceptual representation and reasoning. In particular, it provides a novel theoretical hypothesis - as well as a novel categorization algorithm called DELTA - showing how to integrate the representational and reasoning assumptions (...)
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  8. Perceptron Connectives in Knowledge Representation.Pietro Galliani, Guendalina Righetti, Daniele Porello, Oliver Kutz & Nicolas Toquard - 2020 - In Pietro Galliani, Guendalina Righetti, Daniele Porello, Oliver Kutz & Nicolas Toquard (eds.), Knowledge Engineering and Knowledge Management - 22nd International Conference, {EKAW} 2020, Bolzano, Italy, September 16-20, 2020, Proceedings. Lecture Notes in Computer Science 12387. pp. 183-193.
    We discuss the role of perceptron (or threshold) connectives in the context of Description Logic, and in particular their possible use as a bridge between statistical learning of models from data and logical reasoning over knowledge bases. We prove that such connectives can be added to the language of most forms of Description Logic without increasing the complexity of the corresponding inference problem. We show, with a practical example over the Gene Ontology, how even simple instances of perceptron (...)
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  9. Quasi‐Indexicals and Knowledge Reports.William J. Rapaport, Stuart C. Shapiro & Janyce M. Wiebe - 1997 - Cognitive Science 21 (1):63-107.
    We present a computational analysis of de re, de dicto, and de se belief and knowledge reports. Our analysis solves a problem first observed by Hector-Neri Castañeda, namely, that the simple rule -/- `(A knows that P) implies P' -/- apparently does not hold if P contains a quasi-indexical. We present a single rule, in the context of a knowledge-representation and reasoning system, that holds for all P, including those containing quasi-indexicals. In so doing, we explore (...)
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  10. A 3rd person Knowledge Level analysis of cognitive architectures: problems, challenges, and future directions.Antonio Lieto - 2021 - Unipa Invited Seminars.
    A 3rd person Knowledge Level analysis of cognitive architectures -/- Abstract I provide a knowledge level analysis of the main representational and reasoning problems affecting the cognitive architectures for what concerns this issue. In providing this analysis I will show, by considering some of the main cognitive architectures currently available (e.g. SOAR, ACT-R, CLARION), how one of the main problems of such architectures is represented by the fact that their knowledge representation and processing mechanisms are (...)
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  11. Possessing reasons: why the awareness-first approach is better than the knowledge-first approach.Paul Silva - 2021 - Synthese 199 (1-2):2925-2947.
    [Significantly updated in Chapter 6 of Awareness and the Substructure of Knowledge] In order for a reason to justify an action or attitude it must be one that is possessed by an agent. Knowledge-centric views of possession ground our possession of reasons, at least partially, either in our knowledge of them or in our being in a position to know them. On virtually all accounts, knowing P is some kind of non-accidental true belief that P. This entails (...)
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  12. Reasoning, Rules and Representation.Paul Robinson & Richard Samuels - 2018 - In Sorin Bangu (ed.), Naturalizing Logico-Mathematical Knowledge: Approaches From Psychology and Cognitive Science. New York: Routledge. pp. 30-51.
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  13. Models and minds.Stuart C. Shapiro & William J. Rapaport - 1991 - In Robert C. Cummins (ed.), Philosophy and AI: Essays at the Interface. Cambridge: MIT Press. pp. 215--259.
    Cognitive agents, whether human or computer, that engage in natural-language discourse and that have beliefs about the beliefs of other cognitive agents must be able to represent objects the way they believe them to be and the way they believe others believe them to be. They must be able to represent other cognitive agents both as objects of beliefs and as agents of beliefs. They must be able to represent their own beliefs, and they must be able to represent beliefs (...)
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  14. 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 (...)
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  15. Towards Knowledge-driven Distillation and Explanation of Black-box Models.Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello - 2021 - In Roberto Confalonieri, Guendalina Righetti, Pietro Galliani, Nicolas Toquard, Oliver Kutz & Daniele Porello (eds.), Proceedings of the Workshop on Data meets Applied Ontologies in Explainable {AI} {(DAO-XAI} 2021) part of Bratislava Knowledge September {(BAKS} 2021), Bratislava, Slovakia, September 18th to 19th, 2021. CEUR 2998.
    We introduce and discuss a knowledge-driven distillation approach to explaining black-box models by means of two kinds of interpretable models. The first is perceptron (or threshold) connectives, which enrich knowledge representation languages such as Description Logics with linear operators that serve as a bridge between statistical learning and logical reasoning. The second is Trepan Reloaded, an ap- proach that builds post-hoc explanations of black-box classifiers in the form of decision trees enhanced by domain knowledge. Our (...)
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  16. Cognitive and Computer Systems for Understanding Narrative Text.William J. Rapaport, Erwin M. Segal, Stuart C. Shapiro, David A. Zubin, Gail A. Bruder, Judith Felson Duchan & David M. Mark - manuscript
    This project continues our interdisciplinary research into computational and cognitive aspects of narrative comprehension. Our ultimate goal is the development of a computational theory of how humans understand narrative texts. The theory will be informed by joint research from the viewpoints of linguistics, cognitive psychology, the study of language acquisition, literary theory, geography, philosophy, and artificial intelligence. The linguists, literary theorists, and geographers in our group are developing theories of narrative language and spatial understanding that are being tested by the (...)
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  17. (1 other version)Logical foundations for belief representation.William J. Rapaport - 1986 - Cognitive Science 10 (4):371-422.
    This essay presents a philosophical and computational theory of the representation of de re, de dicto, nested, and quasi-indexical belief reports expressed in natural language. The propositional Semantic Network Processing System (SNePS) is used for representing and reasoning about these reports. In particular, quasi-indicators (indexical expressions occurring in intentional contexts and representing uses of indicators by another speaker) pose problems for natural-language representation and reasoning systems, because--unlike pure indicators--they cannot be replaced by coreferential NPs without changing (...)
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  18. Guilty Artificial Minds: Folk Attributions of Mens Rea and Culpability to Artificially Intelligent Agents.Michael T. Stuart & Markus Https://Orcidorg Kneer - 2021 - Proceedings of the ACM on Human-Computer Interaction 5 (CSCW2).
    While philosophers hold that it is patently absurd to blame robots or hold them morally responsible [1], a series of recent empirical studies suggest that people do ascribe blame to AI systems and robots in certain contexts [2]. This is disconcerting: Blame might be shifted from the owners, users or designers of AI systems to the systems themselves, leading to the diminished accountability of the responsible human agents [3]. In this paper, we explore one of the potential underlying reasons for (...)
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  19. From symbols to knowledge systems: A. Newell and H. A. Simon's contribution to symbolic AI.Luis M. Augusto - 2021 - Journal of Knowledge Structures and Systems 2 (1):29 - 62.
    A. Newell and H. A. Simon were two of the most influential scientists in the emerging field of artificial intelligence (AI) in the late 1950s through to the early 1990s. This paper reviews their crucial contribution to this field, namely to symbolic AI. This contribution was constituted mostly by their quest for the implementation of general intelligence and (commonsense) knowledge in artificial thinking or reasoning artifacts, a project they shared with many other scientists but that in their case (...)
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  20. Convention and Representation in Music.Hannah H. Kim - 2023 - Philosophers' Imprint 23 (1).
    In philosophy of music, formalists argue that pure instrumental music is unable to represent any content without the help of lyrics, titles, or dramatic context. In particular, they deny that music’s use of convention counts as a genuine case of representation because only intrinsic means of representing counts and conventions are extrinsic to the sound structures making up music. In this paper, I argue that convention should count as a way for music to genuinely represent content for two reasons. (...)
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  21. Basic knowledge and the normativity of knowledge: The awareness‐first solution.Paul Silva - 2022 - Philosophy and Phenomenological Research 104 (3):564-586.
    [Significantly updated in Chapter 7 of Awareness and the Substructure of Knowledge] Many have found it plausible that knowledge is a constitutively normative state, i.e. a state that is grounded in the possession of reasons. Many have also found it plausible that certain cases of proprioceptive knowledge, memorial knowledge, and self-evident knowledge are cases of knowledge that are not grounded in the possession of reasons. I refer to these as cases of basic knowledge. (...)
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  22. Epistemological Disjunctivism and its Representational Commitments.Craig French - 2019 - In Casey Doyle, Joseph Milburn & Duncan Pritchard (eds.), New Issues in Epistemological Disjunctivism. New York: Routledge.
    Orthodox epistemological disjunctivism involves the idea that paradigm cases of visual perceptual knowledge are based on visual perceptual states which are propositional, and hence representational. Given this, the orthodox version of epistemological disjunctivism takes on controversial representational commitments in the philosophy of perception. Must epistemological disjunctivism involve these commitments? I don’t think so. Here I argue that we can take epistemological disjunctivism in a new direction and develop a version of the view free of these representational commitments. The basic (...)
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  23. Kant and the Philosophy of Mind: Perception, Reason, and the Self.Andrew Stephenson & Anil Gomes (eds.) - 2017 - Oxford, United Kingdom: Oxford University Press.
    The essays in this volume explore those aspects of Kant’s writings which concern issues in the philosophy of mind. These issues are central to any understanding of Kant’s critical philosophy and they bear upon contemporary discussions in the philosophy of mind. Fourteen specially written essays address such questions as: What role does mental processing play in Kant’s account of intuition? What kinds of empirical models can be given of these operations? In what sense, and in what ways, are intuitions object-dependent? (...)
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  24. Automating Reasoning with Standpoint Logic via Nested Sequents.Tim Lyon & Lucía Gómez Álvarez - 2018 - In Michael Thielscher, Francesca Toni & Frank Wolter (eds.), Proceedings of the Sixteenth International Conference on Principles of Knowledge Representation and Reasoning (KR2018). pp. 257-266.
    Standpoint logic is a recently proposed formalism in the context of knowledge integration, which advocates a multi-perspective approach permitting reasoning with a selection of diverse and possibly conflicting standpoints rather than forcing their unification. In this paper, we introduce nested sequent calculi for propositional standpoint logics---proof systems that manipulate trees whose nodes are multisets of formulae---and show how to automate standpoint reasoning by means of non-deterministic proof-search algorithms. To obtain worst-case complexity-optimal proof-search, we introduce a novel technique (...)
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  25. In Defense of Contextual Vocabulary Acquisition: How to Do Things with Words in Context.William J. Rapaport - 2005 - In Anind Dey, Boicho Kokinov, David Leake & Roy Turner (eds.), Proceedings of the 5th International and Interdisciplinary Conference on Modeling and Using Context. Springer-Verlag Lecture Notes in Artificial Intelligence 3554. pp. 396--409.
    Contextual vocabulary acquisition (CVA) is the deliberate acquisition of a meaning for a word in a text by reasoning from context, where “context” includes: (1) the reader’s “internalization” of the surrounding text, i.e., the reader’s “mental model” of the word’s “textual context” (hereafter, “co-text” [3]) integrated with (2) the reader’s prior knowledge (PK), but it excludes (3) external sources such as dictionaries or people. CVA is what you do when you come across an unfamiliar word in your reading, (...)
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  26. Cognitive Agents with Commonsense.Antonio Lieto - 2021 - I-Cog Talks.
    Commonsense reasoning is a crucial human ability employed in everyday tasks. In this talk I provide a knowledge level analysis of the main representational and reasoning problems affecting the cognitive architectures for what concerns this issue. In providing this analysis I will show, by considering some of the main cognitive architectures currently available (e.g. SOAR, ACT-R, CLARION), how one of the main problems of such architectures is represented by the fact that their knowledge representation and (...)
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  27. Dretske on Introspection and Knowledge.Nathan Jun - 2015 - Rivista di Filosofia 106 (1):99-118.
    In Naturalizing the Mind, Fred Dretske articulates and defends a naturalistic theory of the mind which he calls «the Representation Thesis.» In brief, this thesis states that «(1) All mental facts are representational facts, and (2) All representational facts are facts about information functions.» From this it follows that introspective knowledge, the mind's direct knowledge of its own states, is a case of «displaced perception»-that is, knowledge of mental (i.e., representational) facts through an awareness of external (...)
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  28. Biomedical Terminologies and Ontologies: Enabling Biomedical Semantic Interoperability and Standards in Europe.Bernard de Bono, Mathias Brochhausen, Sybo Dijkstra, Dipak Kalra, Stephan Keifer & Barry Smith - 2009 - In Bernard de Bono, Mathias Brochhausen, Sybo Dijkstra, Dipak Kalra, Stephan Keifer & Barry Smith (eds.), European Large-Scale Action on Electronic Health.
    In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting (...)
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  29. Contextual Vocabulary Acquisition: A Computational Theory and Educational Curriculum.William J. Rapaport & Michael W. Kibby - 2002 - In Nagib Callaos, Ana Breda & Ma Yolanda Fernandez J. (eds.), Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics. International Institute of Informatics and Systemics.
    We discuss a research project that develops and applies algorithms for computational contextual vocabulary acquisition (CVA): learning the meaning of unknown words from context. We try to unify a disparate literature on the topic of CVA from psychology, first- and secondlanguage acquisition, and reading science, in order to help develop these algorithms: We use the knowledge gained from the computational CVA system to build an educational curriculum for enhancing students’ abilities to use CVA strategies in their reading of science (...)
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  30. (1 other version)Ontologies for the life sciences.Steffen Schulze-Kremer & Barry Smith - 2005 - In Schulze-Kremer Steffen & Smith Barry (eds.), Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics, vol. 4. Wiley.
    Where humans can manipulate and integrate the information they receive in subtle and ever changing ways from context to context, computers need structured and context-free background information of a sort which ontologies can help to provide. A domain ontology captures the stable, highly general and commonly accepted core knowledge for an application domain. The domain at issue here is that of the life sciences, in particular molecular biology and bioinformatics. Contemporary life science research includes components drawn from physics, chemistry, (...)
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  31. Environmental Representation of the Body.Adrian Cussins - 2012 - Review of Philosophy and Psychology 3 (1):15-32.
    Much recent cognitive neuroscientific work on body knowledge is representationalist: “body schema” and “body images”, for example, are cerebral representations of the body (de Vignemont 2009). A framework assumption is that representation of the body plays an important role in cognition. The question is whether this representationalist assumption is compatible with the variety of broadly situated or embodied approaches recently popular in the cognitive neurosciences: approaches in which cognition is taken to have a ‘direct’ relation to the body (...)
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  32. Do you see what I know? On reasons, perceptual evidence, and epistemic status.Clayton Littlejohn - 2020 - Philosophical Issues 30 (1):205-220.
    Our epistemology can shape the way we think about perception and experience. Speaking as an epistemologist, I should say that I don’t necessarily think that this is a good thing. If we think that we need perceptual evidence to have perceptual knowledge or perceptual justification, we will naturally feel some pressure to think of experience as a source of reasons or evidence. In trying to explain how experience can provide us with evidence, we run the risk of either adopting (...)
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  33. The Engineering Knowledge Research Program.Terry Bristol - 2018 - In Albrecht Fritzsche & Sascha Julian Oks (eds.), The Future of Engineering: Philosophical Foundations, Ethical Problems and Application Cases. Cham: Springer Verlag.
    The engineering knowledge research program is part of the larger effort to articulate a philosophy of engineering and an engineering worldview. Engineering knowledge requires a more comprehensive conceptual framework than scientific knowledge. Engineering is not ‘merely’ applied science. Kuhn and Popper established the limits of scientific knowledge. In parallel, the embrace of complementarity and uncertainty in the new physics undermined the scientific concept of observer-independent knowledge. The paradigm shift from the scientific framework to the broader (...)
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  34. (1 other version)Reflective Knowledge.Kristin Primus - 2021 - In Yitzhak Y. Melamed (ed.), Blackwell Companion to Spinoza. Hoboken, NJ: Blackwell. pp. 265-275.
    In this chapter, I first turn to Spinoza’s obscure “ideas of ideas” doctrine and his claim that “as soon as one knows something, one knows that one knows it, and simultaneously knows that one knows that one knows, and so on, to infinity” (E2p21s). On my view, Spinoza, like Descartes, holds that a given idea can be conceived either in terms of what it represents or as an act of thinking: E2p7 (where Spinoza presents his doctrine of the “parallelism” of (...)
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  35. Foundations for Knowledge-Based Decision Theories.Zeev Goldschmidt - 2024 - Australasian Journal of Philosophy 102 (4):939-958.
    Several philosophers have proposed Knowledge-Based Decision Theories (KDTs)—theories that require agents to maximize expected utility as yielded by utility and probability functions that depend on the agent’s knowledge. Proponents of KDTs argue that such theories are motivated by Knowledge-Reasons norms that require agents to act only on reasons that they know. However, no formal derivation of KDTs from Knowledge-Reasons norms has been suggested, and it is not clear how such norms justify the particular ways in which (...)
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  36. Heterogeneous Proxytypes as a Unifying Cognitive Framework for Conceptual Representation and Reasoning in Artificial Systems.Antonio Lieto - 2021 - In CARLA @FOIS Proceeding. Amsterdam, Netherlands: IOS Press.
    The paper presents the heterogeneous proxytypes hypothesis as a cognitively-inspired computational framework able to reconcile, in both natural and artificial systems, different theories of typicality about conceptual representation and reasoning that have been traditionally seen as incompatible. In particular, through the Dual PECCS system and its evolution, it shows how prototypes, exemplars and theory-theory like conceptual representations can be integrated in a cognitive artificial agent (thus extending its categorization capabilities) and, in addition, can provide useful insights in the (...)
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  37. The Ontology of Knowledge, logic, arithmetic, sets theory and geometry (issue 20220523).Jean-Louis Boucon - 2021 - Published.
    Despite the efforts undertaken to separate scientific reasoning and metaphysical considerations, despite the rigor of construction of mathematics, these are not, in their very foundations, independent of the modalities, of the laws of representation of the world. The OdC shows that the logical Facts Exist neither more nor less than the Facts of the world which are Facts of Knowledge. Mathematical facts are representation facts. The primary objective of this article is to integrate the subject into (...)
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  38. The Orbital Space Environment and Space Situational Awareness Domain Ontology – Towards an International Information System for Space Data.Robert J. Rovetto - 2016 Sept - In Proceedings of The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference.
    The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...)
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  39. Representation and Misrepresentation of Knowledge.Mikkel Gerken - forthcoming - Behavioral and Brain Sciences.
    I argue for three points: First, evidence of the primacy of knowledge representation is not evidence of primacy of knowledge. Second, knowledge-oriented mindreading research should also focus on misrepresentations and biased representations of knowledge. Third, knowledge-oriented mindreading research must confront the problem of the gold standard that arises when disagreement about knowledge complicates the interpretation of empirical findings.
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  40. The Dark Knowledge Problem: Why Public Justifications are Not Arguments.Sean Donahue - 2023 - Journal of Moral Philosophy 21 (3-4):298-332.
    According to the Public Justification Principle, legitimate laws must be justifiable to all reasonable citizens. Proponents of this principle assume that its satisfaction requires speakers to offer justifications that are representable as arguments that feature premises which reasonable listeners would accept. I develop the concept of dark knowledge to show that this assumption is false. Laws are often justified on the basis of premises that many reasonable listeners know, even though they would reject these premises on the basis of (...)
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  41. Knowledge, reasoning, and deliberation.Brian Kim - 2020 - Ratio 33 (1):14-26.
    Epistemologists have become increasingly interested in the practical role of knowledge. One prominent principle, which I call PREMISE, states that if you know that p, then you are justified in using p as a premise in your reasoning. In response, a number of critics have proposed a variety of counter-examples. In order to evaluate these problem cases, we need to consider the broader context in which this principle is situated by specifying in greater detail the types of activity (...)
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  42. The representation of context: Ideas from artificial intelligence.James Franklin - 2003 - Law, Probability and Risk 2:191-199.
    To move beyond vague platitudes about the importance of context in legal reasoning or natural language understanding, one must take account of ideas from artificial intelligence on how to represent context formally. Work on topics like prior probabilities, the theory-ladenness of observation, encyclopedic knowledge for disambiguation in language translation and pathology test diagnosis has produced a body of knowledge on how to represent context in artificial intelligence applications.
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  43. Taxonomy for Humans or Computers? Cognitive Pragmatics for Big Data.Beckett Sterner & Nico M. Franz - 2017 - Biological Theory 12 (2):99-111.
    Criticism of big data has focused on showing that more is not necessarily better, in the sense that data may lose their value when taken out of context and aggregated together. The next step is to incorporate an awareness of pitfalls for aggregation into the design of data infrastructure and institutions. A common strategy minimizes aggregation errors by increasing the precision of our conventions for identifying and classifying data. As a counterpoint, we argue that there are pragmatic trade-offs between precision (...)
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  44. Pictorial Representation And Moral Knowledge.Katerina Bantinaki - 2004 - Postgraduate Journal of Aesthetics 1 (2):69-76.
    The idea that pictorial art can have cognitive value, that it can enhance our understanding of the world and of our own selves, has had many advocates in art theory and philosophical aesthetics alike. It has also been argued, however, that the power of pictorial representation to convey or enhance knowledge, in particular knowledge with moral content, is not generalized across the medium.
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  45. An Ontological Architecture for Orbital Debris Data.Robert J. Rovetto - 2015 - Earth Science Informatics 9 (1):67-82.
    The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris and broader SSA domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain (...)
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  46. Architecture and Deconstruction. The Case of Peter Eisenman and Bernard Tschumi.Cezary Wąs - 2015 - Dissertation, University of Wrocław
    Architecture and Deconstruction Case of Peter Eisenman and Bernard Tschumi -/- Introduction Towards deconstruction in architecture Intensive relations between philosophical deconstruction and architecture, which were present in the late 1980s and early 1990s, belong to the past and therefore may be described from a greater than before distance. Within these relations three basic variations can be distinguished: the first one, in which philosophy of deconstruction deals with architectural terms but does not interfere with real architecture, the second one, in which (...)
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  47. Knowledge and reasonableness.Krista Lawlor - 2020 - Synthese 199:1435-1451.
    The notion of relevance plays a role in many accounts of knowledge and knowledge ascription. Although use of the notion is well-motivated, theorists struggle to codify relevance. A reasonable person standard of relevance addresses this codification problem, and provides an objective and flexible standard of relevance; however, treating relevance as reasonableness seems to allow practical factors to determine whether one has knowledge or not—so-called “pragmatic encroachment.” I argue that a fuller understanding of reasonableness and of the role (...)
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  48. Multimodal Abduction in Knowledge Development.L. Magnani - 2009 - Preworkshop Proceedings, IJCAI2009International Workshop on Abductive and Inductive Knowledge Development (Pasadena, CA, USA, July 12, 2009).
    From the perspective of distributed cognition I will stress how abduction is essentially multimodal, in that both data and hypotheses can have a full range of verbal and sensory representations, involving words, sights, images, smells, etc., but also kinesthetic – related to the ability to sense the position and location and orientation and movement of the body and its parts – and motor experiences and other feelings such as pain, and thus all sensory modalities. The presence of kinesthetic and motor (...)
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  49. Design Knowledge Representation: An Ontological Perspective.Emilio M. Sanfilippo, Claudio Masolo & Daniele Porello - 2015 - In Emilio M. Sanfilippo, Claudio Masolo & Daniele Porello (eds.), Proceedings of the 1st Workshop on Artificial Intelligence and Design, {A} workshop of the {XIV} International Conference of the Italian Association for Artificial Intelligence (AI*IA 2015), Ferrara, Italy, September 22, 2015. pp. 41-54.
    We present a preliminary high-level formal theory, grounded on knowledge representation techniques and foundational ontologies, for the uniform and integrated representation of the different kinds of (quali- tative and quantitative) knowledge involved in the designing process. We discuss the conceptual nature of engineering design by individuating and analyzing the involved notions. These notions are then formally charac- terized by extending the DOLCE foundational ontology. Our ultimate purpose is twofold: (i) to contribute to foundational issues of design; (...)
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  50. Causal Language and the Structure of Force in Newton’s System of the World.Hylarie Kochiras - 2013 - Hopos: The Journal of the International Society for the History of Philosophy of Science 3 (2):210-235.
    Although Newton carefully eschews questions about gravity’s causal basis in the published Principia, the original version of his masterwork’s third book contains some intriguing causal language. “These forces,” he writes, “arise from the universal nature of matter.” Such remarks seem to assert knowledge of gravity’s cause, even that matter is capable of robust and distant action. Some commentators defend that interpretation of the text—a text whose proper interpretation is important since Newton’s reasons for suppressing it strongly suggest that he (...)
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