PyIBL is a Python implementation of a subset of Instance Based Learning Theory
(IBLT) (Cleotilde Gonzalez, Javier F. Lerch and Christian Lebiere (2003),
Instance-based learning in dynamic decision making, Cognitive Science, 27,
591-635. DOI: 10.1016/S0364-0213(03)00031-4). It is made and distributed by
the Dynamic Decision Making Laboratory of Carnegie Mellon University for
making computational cognitive models supporting research in how people make
decisions in dynamic environments.

PyIBL is copyright 2014-2015 by Carnegie Mellon University. It may be freely used,
and modified, but only for research purposes.

For further information, including the documentation and instructions for
downloading the latest version, see

http://pyibl.ddmlab.com

The PyIBL developers can be reached at pyibl@ddmlab.com.
