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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Cognitive modeling Plunkett, Kim (Mitarb.); VanLehn, Kurt (Mitarb.); Prince, Alan (Mitarb.). - Cambridge, Mass [u.a.] : MIT Press, 2002 BLLDB UB Frankfurt Linguistik Show details 2 Mind modelling : a cognitive science approach to reasoning, learning and discovery Cardoso, Amílcar (Mitarb.); Alpay, Laurence (Mitarb.); Schlieder, Christoph (Mitarb.). - Lengerich [u.a.] : Pabst, 1998 BLLDB UB Frankfurt Linguistik Show details 3 Collected Papers of the Soar/IFOR Project. Spring 1994 Johnson, W. L.; Jones, Randolph M.; Keirsey, David... In: DTIC AND NTIS (1994) BASE Show details 4 Acquisition of children's addition strategies: A model of impasse-free, knowledge-level learning Jones, Randolph M.; Vanlehn, Kurt. - : Kluwer Academic Publishers; Springer Science+Business Media, 1994 Abstract: When children learn to add, they count on their fingers, beginning with the simple Sum Strategy and gradually developing the more sophisticated and efficient Min strategy. The shift from Sum to Min provides an ideal domain for the study of naturally occurring discovery processes in cognitive skill acquisition. The Sum -to- Min transition poses a number of challenges for machine-learning systems that would model the phenomenon. First, in addition to the Sum and Min strategies, Siegler and Jenkins (1989) found that children exhibit two transitional strategies, but not a strategy proposed by an earlier model. Second, they found that children do not invent the Min strategy in response to impasses, or gaps in their knowledge. Rather, Min develops spontaneously and gradually replaces earlier strategies. Third, intricate structural differences between the Sum and Min strategies make it difficult, if not impossible, for standard, symbol-level machine-learning algorithms to model the transition. We present a computer model, called Gips , that meets these challenges. Gips combines a relatively simple algorithm for problem solving with a probabilistic learning algorithm that performs symbol-level and knowledge-level learning, both in the presence and absence of impasses. In addition, Gips makes psychologically plausible demands on local processing and memory. Most importantly, the system successfully models the shift from Sum to Min , as well as the two transitional strategies found by Siegler and Jenkins. ; Peer Reviewed ; http://deepblue.lib.umich.edu/bitstream/2027.42/46917/1/10994_2004_Article_BF00993172.pdf Keyword: Artificial Intelligence (Incl. Robotics); Automation and Robotics; Cognitive Simulation; Computer Science; Computing Methodologies; Engineering; Impasse-free Learning; Induction; Language Translation and Linguistics; Probabilistic Learning; Problem-solving Strategies; Science; Science (General); Simulation and Modeling URL: https://doi.org/10.1007/BF00993172 https://hdl.handle.net/2027.42/46917 BASE Hide details 5 A Model of the Self-Explanation Effect VanLehn, Kirt; Jones, Randolph M.; Chi, Michelene T. In: DTIC AND NTIS (1991) BASE Show details

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