no code implementations • 5 Dec 2023 • Sergio A. Serrano, Jose Martinez-Carranza, L. Enrique Sucar
In this article, we study how to measure the similarity between cross-domain reinforcement learning tasks to select a source of knowledge that will improve the performance of the learning agent.
1 code implementation • 19 Mar 2021 • Sergio A. Serrano, Elizabeth Santiago, Jose Martinez-Carranza, Eduardo Morales, L. Enrique Sucar
The main goal in task planning is to build a sequence of actions that takes an agent from an initial state to a goal state.
no code implementations • 13 Oct 2020 • Mauricio Gonzalez-Soto, Ivan R. Feliciano-Avelino, L. Enrique Sucar, Hugo J. Escalante Balderas
We test our method over two different scenarios, and the experiments mainly confirm that our technique can learn a causal structure.
no code implementations • 21 Aug 2020 • Rafael Morales-Gamboa, L. Enrique Sucar
We present a general method for using a competences map, created by defining generalization/specialization and inclusion/part-of relationships between competences, in order to build an overlay student model in the form of a dynamic Bayesian network in which conditional probability distributions are defined per relationship type.
no code implementations • 26 Jul 2019 • Mauricio Gonzalez Soto, David Danks, Hugo J. Escalante Balderas, L. Enrique Sucar
Decision-making under uncertainty and causal thinking are fundamental aspects of intelligent reasoning.