A Textbook of Belief Dynamics

Hansson, Sven Ove

doi:10.1007/978-94-007-0814-3

Cited by 304 publications

(119 citation statements)

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“…Base belief change, for the same logical framework than AGM, was characterized by Hansson (see [11], [12]). The results for contraction and revision were improved in [13] (by Hansson and Wassermann): for contraction ( [13,Theorem 3.8]) it is shown that finitarity and monotony suffice, while for revision ([13,Theorem 3.17]) their proof depends on a further condition, Noncontravention: for all sentences ϕ, if ¬ϕ ∈ Cn S (T ∪ {ϕ}), then ¬ϕ ∈ Cn S (T ).…”

Section: Preliminaries On Theory and Base Belief Changementioning

confidence: 88%

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Base Belief Change for Finitary Monotonic Logics

Pardo

Dellunde

Godo

2010

Current Topics in Artificial Intelligence

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Abstract. We slightly improve on characterization results already in the literature for base revision. We show that in order to axiomatically characterize revision operators in a logic the only conditions this logic is required to satisfy are: finitarity and monotonicity. A characterization of limiting cases of revision operators, full meet and maxichoice, is also offered. In the second part of the paper, as a particular case, we focus on the class of graded fuzzy logics and distinguish two types of bases, naturally arising in that context, exhibiting different behavior.

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Section: Preliminaries On Theory and Base Belief Changementioning

confidence: 88%

“…The original framework of Alchourrón, Gärdenfors and Makinson (AGM) [1] deals with belief change operators on deductively closed theories. This framework was generalized by Hansson [11,12] to deal with bases, i.e. arbitrary set of formulas, the original requirement of logical closure being dropped.…”

Section: Introductionmentioning

confidence: 99%

Base Belief Change for Finitary Monotonic Logics

Pardo

Dellunde

Godo

2010

Current Topics in Artificial Intelligence

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“…AGM-style belief revision (AGM-BR; for an overview, see Hansson 1999), typically aims at coherence optimization between a given set of beliefs and new information in as conservative a way as possible, implicitly taking that new information as true, whatever distance its adherents take to matters of truth. However, as far as aiming at truth approximation at all, AGM-BR seems to be primarily aiming at the truth about the actual world, actual truth approximation, in short.…”

Section: Introductionmentioning

confidence: 99%

Basic and Refined Nomic Truth Approximation by Evidence-Guided Belief Revision in AGM-Terms

Kuipers

2011

Erkenn

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Straightforward theory revision, taking into account as effectively as possible the established nomic possibilities and, on their basis induced empirical laws, is conducive for (unstratified) nomic truth approximation. The question this paper asks is: is it possible to reconstruct the relevant theory revision steps, on the basis of incoming evidence, in AGM-terms? A positive answer will be given in two rounds, first for the case in which the initial theory is compatible with the established empirical laws, then for the case in which it is incompatible with at least one such a law.

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“…Learning new concepts and their words is not simply "additional knowledge" or learning a definition. Concept learning requires making ever more refined discriminations of ideas, actions, feelings, and objects; it necessitates "assimilating" (Piaget 1952), "integrating" (Kintsch 1988), "consolidating" (Hansson 1999), or "connecting" (Storkel 2009) the newly learned concept with prior knowledge, which might include inference, belief revision, or reorganizing existing cognitive schemata.…”

Section: Sternberg Et Almentioning

confidence: 99%

“…Both ways of integrating text and prior knowledge are components of reading comprehension. "Belief revision" is the subfield of AI, knowledge representation, and philosophy that studies this (Alchourrón et al 1985, Martins & Shapiro 1988, Martins 1991, Gärdenfors 1992, Hansson 1999, Johnson 2006). The combination of the reader's prior knowledge and internalized (co-)text produces an updated, mental knowledge-base that is a "belief-revised integration" of the inputs.…”

Section: The Proper Definition Of 'Context'mentioning

confidence: 99%

Contextual vocabulary acquisition: from algorithm to curriculum

Rapaport

Kibby

IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH

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Deliberate contextual vocabulary acquisition (CVA) is a reader's ability to figure out a (not the) meaning for an unknown word from its "context", without external sources of help such as dictionaries or people. The appropriate context for such CVA is the "belief-revised integration" of the reader's prior knowledge with the reader's "internalization" of the text. We discuss unwarranted assumptions behind some classic objections to CVA, and present and defend a computational theory of CVA that we have adapted to a new classroom curriculum designed to help students use CVA to improve their reading comprehension. Running head: Contextual Vocabulary Acquisition Keywords:Artificial Intelligence, Education, Linguistics, Philosophy; Instruction, Language acquisition, Language understanding, Learning, Reasoning, Semantics; Knowledge representation, Logic, Symbolic computational modeling. 2 A Computational Theory of Contextual Vocabulary AcquisitionReaders often don't-or don't want to-look up unfamiliar words "in the dictionary". Sometimes, they don't realize that a word is unfamiliar, reading past it as if it weren't there: Even "skilled readers" skip "about one-third of the words" (Brysbaert et al. 2005), and words that are highly likely to be chosen in a cloze task are skipped more than others (Rayner & Well 1996); paradoxically, "children may misread or ignore unfamiliar words without jeopardizing comprehension" (Bowey & Muller 2005, citing Share 1999. Other times, readers realize that they don't understand a word but are too lazy (or embarrassed) to do anything about it: Perhaps-discouraged by previous, unsuccessful attempts to look words up-they hope that the word isn't important and won't be used again.Or they might be curious as to what the unfamiliar word might mean, i.e., what the author had in mind when using it. If no one is around to ask, or if the only people around don't know the word, then the reader can look it up in "the" dictionary. But what if no dictionary is handy? Or the only one doesn't contain the word? Or it does, but the definition seems inappropriate for the context, or is unhelpful? With the exception of learner's dictionaries designed primarily for ESL readers, dictionaries are often difficult to use and their definitions difficult to interpret (Miller 1985(Miller , 1986Miller & Gildea 1985;Rapaport & Kibby 2007).Alternatively, readers can "figure it out" from the "context". We call this "contextual vocabulary acquisition" (CVA). Just as giving a person a fish feeds them for a day, but teaching them to fish feeds them for a lifetime, so giving a reader a definition tells them one word's meaning, but teaching them CVA enables them to become better readers. We make four principal claims: (1) If "[t]exts ... are ... the soil in which word-meaning understandings are grown" (Wieland 2008), then the reader's prior knowledge is the water enabling that growth. (2) A procedure for successful CVA can be expressed in terms so precise that it can be programmed into a computer and (3) taught to h...

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A Textbook of Belief Dynamics

Cited by 304 publications

References 0 publications

Base Belief Change for Finitary Monotonic Logics

Base Belief Change for Finitary Monotonic Logics

Basic and Refined Nomic Truth Approximation by Evidence-Guided Belief Revision in AGM-Terms

Contextual vocabulary acquisition: from algorithm to curriculum

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