Melomics: A Case-Study of AI in Spain

By Quintana, Carlos Sanchez; Arcas, Francisco Moreno et al. | AI Magazine, Fall 2013 | Go to article overview

Melomics: A Case-Study of AI in Spain


Quintana, Carlos Sanchez, Arcas, Francisco Moreno, Molina, David Albarracin, Rodriguez, Jose David Fernandez, Vico, Francisco J., AI Magazine


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In Spain there are 74 universities, many of which have computer science departments that host AI-related research groups. AEPIA, the Spanish society for AI research, was founded in 1983 and has been vigorously promoting the advancement of AI since then. Along with several other societies and communities of interest, it promotes various periodic conferences and workshops. The Artificial Intelligence Research Institute (IIIA) of the Spanish National Research Council constitutes one of the flagships of local AI research. Ramon Lopez de Mantaras, IIIA's renowned director, was one of the pioneers of AI in Spain, and he also was the recipient of the prestigious AAAI Englemore Award in 2011. Other researchers that have reached an outstanding position, and lead important research groups in Spain, include Antonio Bahamonde (University of Oviedo), Federico Barber (Polytechnic University of Madrid), Vicent Botti (Polytechnic University of Valencia), and Amparo Vila (University of Granada). In this column, we describe a new class of computer composer (Ball 2012), which is being considered as a milestone in AI research, (1) currently developed at the Computer Science Department of the University of Malaga (UMA). This department, with more than one hundred faculty members, is organized in several research groups, three of which maintain active AI research lines.

Melomics is a new approach in artificial creativity (for a perspective on this discipline, see the 2009 fall issue of AI Magazine). More specifically, it focuses on algorithmic composition and aims at the full automation of the composition process of professional music. Before going into the details, and to better understand what is new in Melomics, it is worth mentioning that a wide range of AI techniques have been used for algorithmic composition in the past (like grammatical and knowledge-based systems, artificial neural networks, statistical machine learning, and evolutionary algorithms), as well as a wide collection of mappings from raw data to music notation. The first result goes back to the origins of AI: the Illiac Suite, a composition generated by computer (programmed by Hiller and Isaacson, late in 1956) as an experiment on the formal aspects of music composition. Since then, many researchers and artists have got notable results, as David Cope's Emily Howell algorithm or Kemal Ebcioglu's CHORAL expert system. Most strategies for computer composing have focused on imitating preexisting human styles, but Melomics' computer composers shy away from this trend, providing the system with knowledge about music composition (as a human learner is taught), which allows them to create their own styles.

Genetics, Embryology, and Evolution

To achieve these results, instead of traditional AI techniques, an approach based on evolutionary algorithms and indirect encoding has been followed. Since AI Magazine does not usually feature articles using these concepts, we will devote a few paragraphs to present them before going into further detail. See also Stanley and Miikkulainen's (2003) paper for a more extensive presentation of the concept of indirect encoding.

Biological evolution is one of the main mechanisms that has contributed to the diversity and complexity of living forms. In computer science, evolutionary algorithms represent a kind of heuristic methodology inspired by evolutionary biology. In these algorithms, a changing set of candidate solutions (a population of individuals) undergoes a repeated cycle of evaluation (by means of a fitness function), selection, and reproduction with variation (mutation and crossover). Evolutionary algorithms have been studied in depth over the last few decades, and they have been applied to many different problem domains.

However, classical evolutionary algorithms tend to show problems of scalability (the performance degrades significantly as the size of the problem increases) and solution structure (the solutions generated by the algorithm tend to be unstructured, hard to adapt, and fragile). …

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