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View Code? Open in Web Editor NEWA framework that focuses on using bayesian and Dynamic Bayesian Networks to perform Learning from observation on Discrete Domains
A framework that focuses on using bayesian and Dynamic Bayesian Networks to perform Learning from observation on Discrete Domains
# 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 - [email protected], 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
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