PyIBL is a Python implementation of a subset of Instance Based Learning Theory (IBLT). 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.
Typically PyIBL is used by creating an experimental framework in the Python programming language, which uses one or more PyIBL Agent objects. The framework then asks these agents to make decisions, and primaryrms the agents of the results of those decisions. The framework, for example, may be strictly algorithmic, may interact with human subjects, or may be embedded in a web site.
PyIBL is a library, or module, of Python code, useful for creating Python programs; it is not a stand alone application. Some knowledge of Python programming is essential for using it.
PyIBL is an ongoing project, and has been started with a small subset of IBLT. As it evolves it is expected that more and more of the IBLT will be encorporated into it. For example, PyIBL does not currently support similarity, spreading activation or deferred feedback, but those are planned for the near future. PyIBL is still sufficiently early in its development that future versions are likely to radically change some of the APIs exposed today, and some effort may be required to upgrade projects using the current version of PyIBL to a later version.
Implemented by: Don Morrison
SpeedyIBL is an implementation of Instance Based Learning (IBL) models, a Python library that allows to create single or multiple IBL agents with fast processing and response time without compromising the performance compared to the traditional implementation of IBL models. SpeedyIBL is made and distributed by the Dynamic Decision Making Laboratory of Carnegie Mellon University.
More details about installing and using SpeedyIBL to create IBL agents that can do a wide range of decision games such as Binary Choice, Insider Attack, Minimap, Ms. Pac-man, Fireman, and Cooperative Navigation can be found in Download/Documentation.
Implemented by: Nhat Phan and Ngoc Nguyen
Shiny-IBL is our most recent attempt to make an IBL model useful to researchers and students of behavioral science.
Shiny-IBL uses the R package Shiny for generating a web application written primarily in the R language. Shiny-IBL offers a complementary way to communicate the complexity of dynamics emerging from the simple IBL model of binary choice.
The main insight from Shiny-IBL is that cognitive modelers should go beyond the explanation of concepts that often need technical expertise and skills, and provide hands-on experiences to demonstrate and communicate the complex insights from their models without the need of additional skills. These interactive tools could also be research tools in their own right. Shiny-IBL could be used to discover a set of inputs that may produce model outputs shedding light to surprising aspects of human behavior, and to help researchers understand the reasons behind.
Implemented by: Jeffrey Chrabaszcz and Erin Bugbee
The Instance-Based Learning Tool (IBLTool) is an effort by Dynamic Decision Making Laboratory to formalize the theoretical approach to modeling. The goals are to have the Instance-Based Learning Theory be:
The tool is a graphical interface written in Visual Basic that uses sockets to communicate with various tasks.
System requirements:
Implemented by: Ripta Pasay and Varun Dutt
This is an R implementation of the IBL model for repeated choice tasks. This is made available for researchers who can then customize the code for other tasks and choice problems. This model is based on descriptions from Lejarraga, Dutt, & Gonzalez 2012 and in Gonzalez & Dutt, 2011.
Implemented by: Emmanouil Konstantinidis
This is a Matlab implementation of the IBL model for binary-choice which was developed and customized for running the problems in the Technion Prediction Tournament data set. This is made available for researchers who can then customize the code for other tasks and choice problems. This is the model presented in Lejarraga, Dutt, & Gonzalez 2012 and in Gonzalez & Dutt, 2011.
Implemented by: Varun Dutt
This is a simple verison of an Instance Based Learning (IBL) model implemented in Microsoft Excel. It is made and distributed by the Dynamic Decision Making Laboratory of Carnegie Mellon University to generate the generality of the learning process in multiple tasks. This model is presented in Lejarraga, Dutt, & Gonzalez, 2012.
Implemented by: Katja Mehlhorn