Eduardo Morales is creator of the Pattern-Based First-OrderInductive Systems PAL and PAL-2, that can learn patterns in the form of Horn clauses from simple example descriptions and general purpose knowledge is described. The learning model is based on a constrained least general generalization algorithm to structure the hypothesis space and guide the learning process, and a pattern–based representation knowledge to constrain the construction of hypothesis. PAL can learn chess patterns which are beyond the learning capabilities of current inductive systems. The same pattern–based approach is used to learn qualitative models of simple dynamic systems and counterpoint rules for two–voice musical pieces [3] .
Eduardo F. Morales and Roberto Morales-Manzanares (1995). Learning Musical Rules. Proceedings of the IJCAI-96 Workshop: Artificial Intelligence and Music, pp. 81-85. Montreal, Canada
Julio H. Zaragoza, Eduardo F. Morales (2010). Relational Reinforcement Learning with Continuous Actions by Combining Behavioral Cloning and Locally Weighted Regression. Journal of Intelligent Systems and Applications, 2:69-79
a Mexican computer scientist and researcher at National Institute of Astrophysics, Optics and Electronics (Instituto Nacional de Astrofísica, Óptica y Electrónica, INAOE), San Andrés Cholula, Puebla. Before he was affiliated as professor at Monterrey Institute of Technology and Higher Education (ITESM) – Campus Morelos, and visiting professor at the University of New South Wales. He obtained a MSc. in computer science on Knowledge-based systems from University of Edinburgh under advisor Alan Bundy, and a Ph.D. in First-Order Induction of Patterns in Chess from Donald Michie's Turing Institute at University of Strathclyde, Glasgow [1], thesis advisor was Tim Niblett. His research interests include machine learning, relational reinforcement learning and robotics.
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PAL
Eduardo Morales is creator of the Pattern-Based First-Order Inductive Systems PAL and PAL-2, that can learn patterns in the form of Horn clauses from simple example descriptions and general purpose knowledge is described. The learning model is based on a constrained least general generalization algorithm to structure the hypothesis space and guide the learning process, and a pattern–based representation knowledge to constrain the construction of hypothesis. PAL can learn chess patterns which are beyond the learning capabilities of current inductive systems. The same pattern–based approach is used to learn qualitative models of simple dynamic systems and counterpoint rules for two–voice musical pieces [3] .Selected Publications
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