AI Methods in Algorithmic Composition: A Comprehensive Survey

Algorithmic composition is the partial or total automation of the process of music composition by using computers. Since the 1950s, different computational techniques related to Artificial Intelligence have been used for algorithmic composition, including grammatical representations, probabilistic methods, neural networks, symbolic rule-based systems, constraint programming and evolutionary algorithms. This survey aims to be a comprehensive account of research on algorithmic composition, presenting a thorough view of the field for researchers in Artificial Intelligence.This study was partially supported by a grant for the MELOMICS project (IPT-300000-2010-010) from the Spanish Ministerio de Ciencia e Innovación, and a grant for the CAUCE project (TSI-090302-2011-8) from the Spanish Ministerio de Industria, Turismo y Comercio. The first author was supported by a grant for the GENEX project (P09-TIC- 5123) from the Consejería de Innovación y Ciencia de Andalucía

Behavior finding: Morphogenetic Designs Shaped by Function

Evolution has shaped an incredible diversity of multicellular living organisms, whose complex forms are self-made through a robust developmental process. This fundamental combination of biological evolution and development has served as an inspiration for novel engineering design methodologies, with the goal to overcome the scalability problems suffered by classical top-down approaches. Top-down methodologies are based on the manual decomposition of the design into modular, independent subunits. In contrast, recent computational morphogenetic techniques have shown that they were able to automatically generate truly complex innovative designs. Algorithms based on evolutionary computation and artificial development have been proposed to automatically design both the structures, within certain constraints, and the controllers that optimize their function. However, the driving force of biological evolution does not resemble an enumeration of design requirements, but much rather relies on the interaction of organisms within the environment. Similarly, controllers do not evolve nor develop separately, but are woven into the organism’s morphology. In this chapter, we discuss evolutionary morphogenetic algorithms inspired by these important aspects of biological evolution. The proposed methodologies could contribute to the automation of processes that design “organic” structures, whose morphologies and controllers are intended to solve a functional problem. The performance of the algorithms is tested on a class of optimization problems that we call behavior-finding. These challenges are not explicitly based on morphology or controller constraints, but only on the solving abilities and efficacy of the design. Our results show that morphogenetic algorithms are well suited to behavior-finding

Complex and Diverse Morphologies Can Develop from a Minimal Genomic Model

While development plays a critical role in the emergence of diversity, its mechanical and chemical actions are considered to be inextricably correlated with genetic control. Since in most extant species the complex growth from zygote to adult organism is orchestrated by a complex gene regulatory network (GRN), the prevalent view is that the evolution of diverse morphologies must result from the evolution of diverse GRN topologies. By contrast, this work focuses on the unique e ect of developmental processes through an abstract model of self-regulated structure without genetic regulation|only modulation of initial conditions. Here, morphologies are generated by a simple evolutionary algorithm searching for the longest instances of unfolding dynamics based on tensegrity graphs. The usual regulatory function of the genome is taken over by physical constraints in the graphs, making morphological diversity a pure product of structural complexi cation. By highlighting the potential of structural development, our model is relevant to both "structuralist" biological models and bio-inspired systems engineering.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The Evolution of Controller-Free Molecular Motors from Spatial Constraints

Locomotion of robotic and virtual agents is a challenging task requiring the control of several degrees of freedom as well as the coordination of multiple subsystems. Traditionally, it is engineered by top-down design and finetuning of the agent’s morphology and controller. A relatively recent trend in fields such as evolutionary robotics, computer animation and artificial life has been the coevolution and mutual adaptation of the morphology and controller in computational agent models. However, the controller is generally modeled as a complex system, often a neural or gene regulatory network. In the present study, inspired by molecular biology and based on normal modal analysis, we formulate a behavior-finding framework for the design of bipedal agents that are able to walk along a filament and have no explicit control system. Instead, agents interact with their environment in a purely reactive way. A simple mutation operator, based on physical relaxation, is used to drive the evolutionary search. Results show that gait patterns can be evolutionarily engineered from the spatial interaction between precisely tuned morphologies and the environment.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Melomics: A Case-Study of AI in Spain

Traditionally focused on good old-fashioned AI and robotics, the Spanish AI community holds a vigorous computational intelligence substrate. Neuromorphic, evolutionary, or fuzzylike systems have been developed by many research groups in the Spanish computer sciences. It is no surprise, then, that these naturegrounded efforts start to emerge, enriching the AI catalogue of research projects and publications and, eventually, leading to new directions of basic or applied research. In this article, we review the contribution of Melomics in computational creativity.The work on Iamus was partially supported by grants IPT-300000-2010-010 from the Spanish Ministerio de Ciencia e Innovación and TSI-090302-2011-8 from the Spanish Ministerio de Industria, Turismo y Comercio. The first and fourth authors were supported by grant P09-TIC-5123 from the Consejería de Innovación y Ciencia, Junta de Andalucía

Just-In-Time eTraining Applied To Emergency Medical Services

While the applications of just-in-time training are more and more spread, the ubiquitous mobile technology has not found practical uses of this training strategy. As an original example of services for healthcare, we present in this work an application of eTraining that makes use of mobile telephones to transmit medical and on-site information content to emergency medical personnel that attend and emergency. The state-of-the-art in related technologies, overall architecture, and functioning of JITTER (for Just-In-Time Training for Emergency Responders) is described in this work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. This work has been funded by the FIT-350100-2006-400 PROFIT project of the Spanish Ministerio de Industria, Turismo y Comercio, American NSF grant DMI-0239180, NIEHS (National Institute for Environmental Health Sciences) grant 1R41ES014793-01, BanDeMar Networks, Inc., the healthcare company iSOFT Sanidad, S.A., and the CITIC Technology Centre

LTMaker: a tool for semiautomatic reconstruction of the embryonic lineage tree from 4D-microscopy

Studies of animal development using a 4Dmicroscopy system generate an immense amount of image data. In order to properly analyze the recorded embryogenesis, a computer-aided systematic process of categorization of cells from the image data should be accomplished. We present a software tool named LTMaker for the systematic semiautomatic identification of embryonic cells centers and also to determine the underlying linage tree. The program saves the generated data to a file so that further analysis of the embryo can be performed with external tools.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A review on machine learning approaches and trends in drug discovery

Abstract: Drug discovery aims at finding new compounds with specific chemical properties for the treatment of diseases. In the last years, the approach used in this search presents an important component in computer science with the skyrocketing of machine learning techniques due to its democratization. With the objectives set by the Precision Medicine initiative and the new challenges generated, it is necessary to establish robust, standard and reproducible computational methodologies to achieve the objectives set. Currently, predictive models based on Machine Learning have gained great importance in the step prior to preclinical studies. This stage manages to drastically reduce costs and research times in the discovery of new drugs. This review article focuses on how these new methodologies are being used in recent years of research. Analyzing the state of the art in this field will give us an idea of where cheminformatics will be developed in the short term, the limitations it presents and the positive results it has achieved. This review will focus mainly on the methods used to model the molecular data, as well as the biological problems addressed and the Machine Learning algorithms used for drug discovery in recent years.Instituto de Salud Carlos III; PI17/01826Instituto de Salud Carlos III; PI17/01561Xunta de Galicia; Ref. ED431D 2017/16Xunta de Galicia; Ref. ED431D 2017/23Xunta de Galicia; Ref. ED431C 2018/4

Design of a case management model for people with chronic disease (Heart Failure and COPD). Phase I: modeling and identification of the main components of the intervention through their actors: patients and professionals (DELTA-ICE-PRO Study

Background Chronic diseases account for nearly 60% of deaths around the world. The extent of this silent epidemic has not met determined responses in governments, policies or professionals in order to transform old Health Care Systems, configured for acute diseases. There is a large list of research about alternative models for people with chronic conditions, many of them with an advanced practice nurse as a key provider, as case management. But some methodological concerns raise, above all, the design of the intervention (intensity, frequency, components, etc). Methods/Design Objectives: General: To develop the first and second phases (theorization and modeling) for designing a multifaceted case-management intervention in people with chronic conditions (COPD and heart failure) and their caregivers. Specific aims: 1) To identify key events in people living with chronic disease and their relation with the Health Care System, from their point of view. 2) To know the coping mechanisms developed by patients and their caregivers along the story with the disease. 3) To know the information processing and its utilization in their interactions with health care providers. 4) To detect potential unmet needs and the ways deployed by patients and their caregivers to resolve them. 5) To obtain a description from patients and caregivers, about their itineraries along the Health Care System, in terms of continuity, accessibility and comprehensiveness of care. 6) To build up a list of promising case-management interventions in patients with Heart Failure and COPD with this information in order to frame it into theoretical models for its reproducibility and conceptualization. 7) To undergo this list to expert judgment to assess its feasibility and pertinence in the Andalusian Health Care. Design: Qualitative research with two phases: For the first five objectives, a qualitative technique with biographic stories will be developed and, for the remaining objectives, an expert consensus through Delphi technique, on the possible interventions yielded from the first phase. The study will be developed in the provinces of Almería, Málaga and Granada in the Southern Spain, from patients included in the Andalusian Health Care Service database with the diagnosis of COPD or Heart Failure, with the collaboration of case manager nurses and general practitioners for the assessment of their suitability to inclusion criteria. Patients and caregivers will be interviewed in their homes or their Health Centers, with their family or their case manager nurse as mediator. Discussion First of a series of studies intended to design a case-management service for people with heart failure and COPD, in the Andalusian Health Care System, where case management has been implemented since 2002. Accordingly with the steps of a theoretical model for complex interventions, in this study, theorization and intervention modeling phases will be developed.This research was carried out with the support of one research grant, awarded by the Regional Health Ministry of Andalusia (Exp. 0222/2008