@inproceedings{Taatgen_2006_Cognitive-Models,
        title = {How Cognitive Models can Inform the Design of Instructions},
        author = {Niels A. Taatgen and David Huss and John R. Anderson},
        pages = {304-309},
        series = {Proceedings of the seventh International Conference on cognitive modeling},
        year = 2006,
        abstract = {Instructions represented as lists of steps lead to inflexible and
brittle behavior in cognitive models, suggesting that list-style
instructions lead to poor learning in people as well. On the basis
of this assumption we designed an alternative operatorstyle
instruction that produces better learning in models. In an
experiment and model of interacting with a simulated Flight
Management System, a system that is notoriously hard to
learn on the basis of list-style instructions, we show that alternative
instructions produce significantly better and more robust
learning.
},
	biburl = {http://www.bibsonomy.org/bibtex/2cb8d6afb8976f785cedce6472e9db138/wnpxrz},
	keywords = {modeling cognition model cognitive}
}

@unpublished{Emond_2002_Cognitive-Modelling,
        title = {Cognitive Modeling and its Application for the Development of Socially Adept Technologies},
        author = {B. Emond and R.L. West},
        year = 2002,
        abstract = {In this paper we would like to propose that the
methodology of cognitive modeling can provide an
approach to the implementation of socially adept
technologies and the evaluation of its usability. We base
this approach on the assumption that representations play
an essential role in mediating social relations. Cognitive
representations and their causal link to the social and
physical environments have been recognized as an essential
element for understanding social relations. The challenge is
to port cognitive modeling methodology into the realm of
socially adept technologies. The first section briefly
presents the state of development of a modeling
environment aimed at supporting usability testing of sociotechnical
systems. The second section will give an
overview of an application intended to support social and
personal awareness in a web-based learning environment.
This application maps users identity, users behavior, and
shared information content into a model of human
memory.
},
	biburl = {http://www.bibsonomy.org/bibtex/2557704e0ea2c7c1ad42c1971d906da7d/wnpxrz},
	keywords = {model cognitive cognition modeling}
}

@article{yu2005,
        title = {{Reverse Engineering Goal Models from Legacy Code}},
        author = {Y. Yu and Y. Wang and J. Mylopoulos and S. Liaskos and A. Lapouchnian and JCS do Prado Leite},
        journal = {Requirements Engineering, 2005. Proceedings. 13th IEEE International Conference on},
        pages = {363--372},
        year = 2005,
        biburl = {http://www.bibsonomy.org/bibtex/242c5b8ab068c7b9adaea0334fc0fbd36/wnpxrz},
	keywords = {code model software goal reverseengineering}
}

@misc{si03flexible,
        title = {A Flexible Mixture Model for Collaborative Filtering},
        author = {L. Si and R. Jin},
        year = 2003,
        url = {citeseer.ist.psu.edu/si03flexible.html},
        description = {Flexible Mixture Model for Collaborative Filtering - Si, Jin (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/250caac59e67d472076003c36a44b1f15/wnpxrz},
	keywords = {imported model mixture collaborative filtering}
}

@article{citeulike:562662,
        title = {Operations for Learning with Graphical Models},
        author = {Wray L. Buntine},
        journal = {Journal of Artificial Intelligence Research},
        pages = {159--225},
        volume = 2,
        year = 1994,
        url = {http://citeseer.ist.psu.edu/6938.html},
        id = {562662},
	priority = {0},
	abstract = {This paper is a multidisciplinary review of empirical, statistical learning from a graphical
model perspective. Well-known examples of graphical models include Bayesian networks,
directed graphs representing a Markov chain, and undirected networks representing
a Markov field. These graphical models are extended to model data analysis and empirical
learning using the notation of plates. Graphical operations for simplifying and manipulating
a problem are provided including decomposition,...},
	biburl = {http://www.bibsonomy.org/bibtex/28952cf0d215116e038971f7c30d6d19d/wnpxrz},
	keywords = {bayesian toread ml learning model proj:o4p graphical}
}

@inproceedings{hollmen03:mixture,
        title = {Mixture Models and Frequent Sets: Combining Global
                  and Local Methods for 0--1 Data.},
        address = {San Fransisco},
        author = {Jaakko Hollm{\'e}n and Jouni K. Sepp{\"a}nen and Heikki Mannila},
        booktitle = {SIAM International Conference on Data Mining
                  (SDM'03)},
        month = {May},
        year = 2003,
        url = {citeseer.ist.psu.edu/698125.html},
        description = {Mixture Models and Frequent Sets: Combining Global and Local Methods for 0-1 Data. (ResearchIndex)},
	biburl = {http://www.bibsonomy.org/bibtex/220b9e565c0156cbc035e7e9f112558b2/wnpxrz},
	keywords = {model mixture data frequent set imported binary}
}