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- Didier Dubois, Petr Hájek & Henri Prade (2000). Knowledge-Driven Versus Data-Driven Logics. Journal of Logic, Language and Information 9 (1):65--89.The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data-driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting forward fuzzy rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data-driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data-driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data-driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize.Reading list | Discuss | Edit | Categorize |My bibliography
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In the context of technology development and systems engineering, knowledge is typically treated as a complex information structure. In this view, knowledge can be stored in highly sophisticated data systems and processed by explicitly intelligent, software-based technologies. This paper argues that the current emphasis upon knowledge as information (or even data) is based upon a form of rationalism which is inappropriate for any comprehensive treatment of knowledge in the context of human-centred systems thinking. A human-centred perspective requires us to treat knowledge in human terms. The paper sets out the particular importance of tacit knowledge in this regard. It sets out two case studies which reveal the critical importance of a careful treatment of tacit knowledge for success in two complex, technology-driven projects.
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| | Other links: dx.doi.org | Scholar | At my library | More options ... Several justification logics have been created, starting with the logic LP, [1]. These can be thought of as explicit versions of modal logics, or of logics of knowledge or belief, in which the unanalyzed necessity (knowledge, belief) operator has been replaced with a family of explicit justification terms. We begin by sketching the basics of justification logics and their relations with modal logics. Then we move to new material. Modal logics come in various strengths. For their corresponding justification logics, differing strength is reflected in different vocabularies. What we show here is that for justification logics corresponding to modal logics extending T, various familiar extensions are actually conservative with respect to each other. Our method of proof is very simple, and general enough to handle several justification logics not directly corresponding to distinct modal logics. Our methods do not, however, allow us to prove comparable results for justification logics corresponding to modal logics that do not extend T. That is, we are able to handle explicit logics of knowledge, but not explicit logics of belief. This remains open.
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| | Scholar | More options ... The early, largely automatic stages of human visual processing involve things like feature detectors (e.g., edge detectors) that do not involve our concepts or beliefs. These stages are called data-driven or bottom up aspects of perceptual information processing. But in the later stages of processing perception often is affected by our concepts, beliefs, and expectations. Such processes are said to be hypothesis-driven or expectation-driven; they are also known as..
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| | Scholar | More options ... Fuzzy logic has become an important tool for a number of different applications ranging from the control of engineering systems to artificial intelligence. In this concise introduction, the author presents a succinct guide to the basic ideas of fuzzy logic, fuzzy sets, fuzzy relations, and fuzzy reasoning, and shows how they may be applied. The book culminates in a chapter which describes fuzzy logic control: the design of intelligent control systems using fuzzy if-then rules which make use of human knowledge and experience to behave in a manner similar to a human controller. Throughout, the level of mathematical knowledge required is kept basic and the concepts are illustrated with numerous diagrams to aid in comprehension. As a result, all those curious to know more about fuzzy concepts and their real-world application will find this a good place to start.
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| | Scholar | At my library | More options ... In this paper, I identify the source of the differences between classical logic and many-valued logics (including fuzzy logics) with respect to the set of valid formulas and the set of inferences sanctioned. In the course of doing so, we find the conditions that are individually necessary and jointly sufficient for any many-valued semantics (again including fuzzy logics) to validate exactly the classically valid formulas, while sanctioning exactly the same set of inferences as classical logic. This in turn shows, contrary to what has sometimes been claimed, that at least one class of infinite-valued semantics is axiomatizable.
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| | Other links: jstor.org | Scholar | At my library | More options ... Proof Theory of Modal Logic is devoted to a thorough study of proof systems for modal logics, that is, logics of necessity, possibility, knowledge, belief, time, computations etc. It contains many new technical results and presentations of novel proof procedures. The volume is of immense importance for the interdisciplinary fields of logic, knowledge representation, and automated deduction.
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| | Scholar | At my library | More options ... Whenever knowledge of the possible interpretation or conceptualization of some- thing helps in perceiving that thing, we say the processing is conceptually driven. That is, the process starts with conceptualization of what might be present and then looks for confirming evidence, biasing the processing mechanisms to give the expected result... Conceptually driven processing and data-driven processing almost always occur together, with each direction of processing contributing something to the total analysis. (Lindsay and Norman 1977, p. 13).
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| | Scholar | More options ... Fuzzy logics are in most cases based on an ad-hoc decision about the interpretation of the conjunction. If they are useful or not can typically be found out only by testing them with example data. Why we should use a specific fuzzy logic can in general not be made plausible. Since the difficulties arise from the use of additional, unmotivated structure with which the set of truth values is endowed, the only way to base fuzzy logics on firm ground is the development of alternative semantics to all of whose components we can associate a meaning. In this paper, we present one possible approach to justify ex post Łukasiewicz Logic as well as Basic Logic. The notion of ambiguity is central. Our framework consists of a Boolean or a Heyting algebra, respectively, endowed with an equivalence relation expressing ambiguity. The quotient set bears naturally the structure of an MV- or a BL-algebra, respectively, and thus can be used to interpret propositions of the mentioned logics.
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| | Other links: jstor.org | Scholar | At my library | More options ... In the last few decades many formal systems of fuzzy logics have been developed. Since the main differences between fuzzy and classical logics lie at the propositional level, the fuzzy predicate logics have developed more slowly (compared to the propositional ones). In this text we aim to promote interest in fuzzy predicate logics by contributing to the model theory of fuzzy predicate logics. First, we generalize the completeness theorem, then we use it to get results on conservative extensions of theories and on witnessed models.
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| | Other links: jstor.org dx.doi.org | Scholar | At my library | More options ... This short paper about fuzzy set-based approximate reasoning first emphasizes the three main semantics for fuzzy sets: similarity, preference and uncertainty. The difference between truth-functional many-valued logics of vague or gradual propositions and non fully compositional calculi such as possibilistic logic (which handles uncertainty) or similarity logics is stressed. Then, potentials of fuzzy set-based reasoning methods are briefly outlined for various kinds of approximate reasoning: deductive reasoning about flexible constraints, reasoning under uncertainty and inconsistency, hypothetical reasoning, exception-tolerant plausible reasoning using generic knowledge, interpolative reasoning, and abductive reasoning (under uncertainty). Open problems are listed in the conclusion.
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| | Scholar | At my library | More options ... Discussion of Didier Dubois , Petr Hájek & Henri Prade, Knowledge-driven versus data-driven logics
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