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Melomics: a Case-Study of AI in Spain Research Groups in the Spanish Computer Sci - Ences

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Melomics: a Case-Study of AI in Spain Research Groups in the Spanish Computer Sci - Ences Column Worldwide AI Australia Spain n Traditionally focused on good old-fashioned Carlos Sánchez Quintana, Francisco Moreno Arcas, David Albar - AI and robotics, the Spanish AI community holds a vigorous computational intelligence racín Molina, Jose David Fernández Rodríguez, Francisco J. Vico substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many Melomics: A Case-Study of AI in Spain research groups in the Spanish computer sci - ences. It is no surprise, then, that these nature- grounded efforts start to emerge, enriching the In Spain there are 74 universities, many of which have computer sci - AI catalogue of research projects and publica - ence departments that host AI-related research groups. AEPIA, the tions and, eventually, leading to new directions Spanish society for AI research, was founded in 1983 and has been of basic or applied research. In this article, we vigorously promoting the advancement of AI since then. Along with review the contribution of Melomics in compu - several other societies and communities of interest, it promotes var - tational creativity. ious 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. Ramón López de Mántaras, IIIA’s renowned director, was one of the pioneers of AI in Spain, and he also was the recipient of the prestigious AAAI Engle - more Award in 2011. Other researchers that have reached an out - standing position, and lead important research groups in Spain, include Antonio Bahamonde (University of Oviedo), Federico Barber (Polytechnic University of Madrid), Vicent Botti (Polytechnic Uni - versity 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 Málaga (UMA). This department, with more than one hundred fac - ulty 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 perspec - tive 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 tech - niques have been used for algorithmic composition in the past (like grammatical and knowledge-based systems, artificial neural net - works, statistical machine learning, and evolutionary algorithms), as well as a wide collection of mappings from raw data to music nota - Copyright © 2013, Association for the Advancement of Artificial Intelligence. All rights reserved. ISSN 0738-4602 FALL 2013 99 Column tion. The first result goes back to the origins of AI: the organism) is known to play a key role in evolutionary Illiac Suite, a composition generated by computer processes. Unfortunately, when evolutionary algo - (programmed by Hiller and Isaacson, late in 1956) as rithms were first proposed, this role had been down - an experiment on the formal aspects of music com - played for decades. This, together with the limited position. Since then, many researchers and artists capacity of early computers, resulted in the wide - have got notable results, as David Cope’s Emily How - spread, implicit adoption of direct encoding in evo - ell algorithm or Kemal Ebcioglu’s CHORAL expert lutionary algorithms. system. Most strategies for computer composing As time passed, the importance of development in have focused on imitating preexisting human styles, the context of evolutionary biology became better but Melomics’ computer composers shy away from and better understood, and this eventually gave rise this trend, providing the system with knowledge to a specific branch of evolutionary thought: evolu - about music composition (as a human learner is tionary developmental biology (evo-devo for short). taught), which allows them to create their own styles. Developmental processes can be described as self- organized choreographies of precisely timed events, with cells dividing and arranging themselves into Genetics, Embryology, and Evolution layers of tissues that fold in complex shapes, resulting To achieve these results, instead of traditional AI in the formation of a multicellular organism from a techniques, an approach based on evolutionary algo - single zygote. In evo-devo, evolutionary changes are rithms and indirect encoding has been followed. interpreted as small mutations in the genome of Since AI Magazine does not usually feature articles organisms that modulate their developmental using these concepts, we will devote a few paragraphs processes in complex and orchestrated ways, result - to present them before going into further detail. See ing in altered forms and novel features. also Stanley and Miikkulainen’s (2003) paper for a As the field of evo-devo matured in the context of more extensive presentation of the concept of indi - evolutionary biology, it inspired an analogous field rect encoding. in the context of evolutionary computation: artificial Biological evolution is one of the main mecha - development. In opposition to the direct encodings nisms that has contributed to the diversity and com - of classical evolutionary algorithms, artificial devel - plexity of living forms. In computer science, evolu - opment uses indirect encodings, that is, formal tionary algorithms represent a kind of heuristic abstractions of developmental processes that define methodology inspired by evolutionary biology. In complex mappings between genotype and pheno - these algorithms, a changing set of candidate solu - type. Using an effective indirect encoding, a small tions (a population of individuals) undergoes a genotype can potentially specify a large and complex repeated cycle of evaluation (by means of a fitness phenotype, accounting for the scalability problem function), selection, and reproduction with variation previously mentioned. Additionally, a small change (mutation and crossover). Evolutionary algorithms in the genotype can potentially provoke a variety of have been studied in depth over the last few decades, coordinated changes in the phenotype, thus helping and they have been applied to many different prob - to mitigate the problem of solution fragility. lem domains. Because of these characteristics, evolutionary algo - However, classical evolutionary algorithms tend to rithms with well-engineered indirect encodings can show problems of scalability (the performance obtain complex solutions, and potentially generate degrades significantly as the size of the problem complex variations of these solutions. In some con - increases) and solution structure (the solutions gen - crete fields traditionally left to human expertise alone erated by the algorithm tend to be unstructured, hard (like industrial design, or the arts), these algorithms to adapt, and fragile). One of the most important fac - can perform in a truly disruptive way. They are cur - tors in these problems is the use of direct encodings: rently being used to a certain extent for automating classical evolutionary algorithms use genotypes (rep - tasks that demand creativity, proposing different resentations of solutions) that map to phenotypes variations to existing solutions, which evolve toward (the solutions themselves) in a straightforward way. desired design targets, resembling an automated form When an algorithm uses direct encoding, each part of brainstorming. of the solution is mapped to a part of the representa - Remarkable examples of real-life applications of tion. As a result, genotypes can grow too large for artificial development have appeared in recent years, evolutionary optimization to be practical, and differ - such as the set of antennas designed to fit the tech - ent parts of the genotype can evolve uncoordinated - nical requirements for the satellites of a NASA space ly, thus inducing the previously mentioned problems mission (Hornby, Lohn., and Linden 2011); the of scalability and fragility in the solutions. design of microstructured optical fibers (Manos, However, the use of direct encodings can be Large, and Poladian 2007); and the automatic gener - regarded as a historical artifact. In contemporary evo - ation of board games (Browne 2008). Our group has lutionary biology, development (the process that successfully developed and applied this paradigm of transforms a zygote into a full-fledged multicellular artificial development to two different domains in 100 AI MAGAZINE Column Figure 1. Examples of Scores Generated by Iamus These scores are considered by professional musicians good enough to have been written by an avant-garde composer (Berger 2013). (Name and dedication not decided by Iamus.) computational creativity: new techniques for auto - constraints and basic aesthetic principles. As evolu - matic character animation (Lobo, Fernández, and tion proceeds, genomes will undergo transforma - Vico 2012), and new systems for algorithmic compo - tions, making the corresponding music pieces more sition. and more complex, and better fitted to the require - ments. Artificial development provides very powerful Computer Composers ways of encoding music pieces. It means a very high Iamus is a computer composer specialized in
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