# LFO_Framework
The current tool is sited in "Towards a Framework for Testing Learning from Observation of State-based Agents".
Gunaratne, Esfandiari, Chan. (2017)

Initial Tool created by Santiago Ontanon - santi@cs.drexel.edu, www.cs.drexel.edu/~santi
Used in paper - 
Ontañón, S., Montaña, J. L., & Gonzalez, A. J. (2014). 
A Dynamic-Bayesian Network framework for modeling and evaluating learning from observation. 
Expert Systems with Applications, 41(11), 5212–5226. http://doi.org/10.1016/j.eswa.2014.02.049

The implementation of Temporal backtracking is from M. W. Floyd and B. Esfandiari, 
“Learning State-Based Behaviour using Temporally Related Cases,” vol 829, pp. 9–11. Proceedings of the Sixteenth UK Workshop on Case-Based Reasoning, Cambrdige, United Kingdom, December 13, 2011

The BNT tool used is from https://github.com/bayesnet/bnt

The initial tool, the BNET tool box, and the implementation of TB are encompassed in the LFOSimulator.jar file in lib

src contains the source code

results contain the results from the experiments

raw_data contains the raw_data for the robocup and vacuum cleaner domain

lib contains the libraries