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Profile: Nina Gierasimczuk (University of Groningen)
  1. Nina Gierasimczuk, Muddy Children, Generalized Quantifiers and Internal Complexity.
    This paper generalizes Muddy Children puzzle to account for a large class of possible public announcements with various quantifiers. We identify conditions for solvability of the extended puzzle, with its classical version as a particular case. The characterization suggests a novel way of modeling multi-agent epistemic reasoning. The framework is based on the concept of number triangle. The advantage of our approach over more general formalizations in epistemic logics, like Dynamic Epistemic Logic, is that it gives models of linear size (...)
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  2. Nina Gierasimczuk, Han L. J. Van der Maas & Maartje E. J. Raijmakers (2013). An Analytic Tableaux Model for Deductive Mastermind Empirically Tested with a Massively Used Online Learning System. Journal of Logic, Language and Information 22 (3):297-314.
    The paper is concerned with the psychological relevance of a logical model for deductive reasoning. We propose a new way to analyze logical reasoning in a deductive version of the Mastermind game implemented within a popular Dutch online educational learning system (Math Garden). Our main goal is to derive predictions about the difficulty of Deductive Mastermind tasks. By means of a logical analysis we derive the number of steps needed for solving these tasks (a proxy for working memory load). Our (...)
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  3. Cédric Dégremont & Nina Gierasimczuk (2011). Finite Identification From the Viewpoint of Epistemic Update. Information And Computation 209 (3):383-396.
    Formal learning theory constitutes an attempt to describe and explain the phenomenon of learning, in particular of language acquisition. The considerations in this domain are also applicable in philosophy of science, where it can be interpreted as a description of the process of scientific inquiry. The theory focuses on various properties of the process of hypothesis change over time. Treating conjectures as informational states, we link the process of conjecture-change to epistemic update. We reconstruct and analyze the temporal aspect of (...)
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  4. Nina Gierasimczuk & Jakub Szymanik (2011). A Note on a Generalization of the Muddy Children Puzzle. In K. Apt (ed.), Proceeding of the 13th Conference on Theoretical Aspects of Rationality and Knowledge. ACM.
    We study a generalization of the Muddy Children puzzle by allowing public announcements with arbitrary generalized quantifiers. We propose a new concise logical modeling of the puzzle based on the number triangle representation of quantifi ers. Our general aim is to discuss the possibility of epistemic modeling that is cut for specifi c informational dynamics. Moreover, we show that the puzzle is solvable for any number of agents if and only if the quanti fier in the announcement is positively active (...)
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  5. Nina Gierasimczuk & Jakub Szymanik (2011). Invariance Properties of Quantifiers and Multiagent Information Exchange. In M. Kanazawa (ed.), Proceedings of the 12th Meeting on Mathematics of Language, Lecture Notes in Artificial Intelligence 6878. Springer.
    The paper presents two case studies of multi-agent information exchange involving generalized quantifiers. We focus on scenarios in which agents successfully converge to knowledge on the basis of the information about the knowledge of others, so-called Muddy Children puzzle and Top Hat puzzle. We investigate the relationship between certain invariance properties of quantifiers and the successful convergence to knowledge in such situations. We generalize the scenarios to account for public announcements with arbitrary quantifiers. We show that the Muddy Children puzzle (...)
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  6. Nina Gierasimczuk (2009). Bridging Learning Theory and Dynamic Epistemic Logic. Synthese 169 (2):371-384.
    This paper discusses the possibility of modelling inductive inference (Gold 1967) in dynamic epistemic logic (see e.g. van Ditmarsch et al. 2007). The general purpose is to propose a semantic basis for designing a modal logic for learning in the limit. First, we analyze a variety of epistemological notions involved in identification in the limit and match it with traditional epistemic and doxastic logic approaches. Then, we provide a comparison of learning by erasing (Lange et al. 1996) and iterated epistemic (...)
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  7. Nina Gierasimczuk & Jakub Szymanik (2009). Branching Quantification V. Two-Way Quantification. Journal of Semantics 26 (4):329-366.
    Next SectionWe discuss the thesis formulated by Hintikka (1973) that certain natural language sentences require non-linear quantification to express their meaning. We investigate sentences with combinations of quantifiers similar to Hintikka's examples and propose a novel alternative reading expressible by linear formulae. This interpretation is based on linguistic and logical observations. We report on our experiments showing that people tend to interpret sentences similar to Hintikka sentence in a way consistent with our interpretation.
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  8. Nina Gierasimczuk & Jakub Szymanik (2007). Hintikka's Thesis Revisited. Bulletin of Symbolic Logic 13:273.
    We discuss Hintikka’s Thesis [Hintikka 1973] that there exist natural language sentences which require non–linear quantification to express their logical form.
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