Compositions created with constraint programming

This chapter surveys music constraint programming systems, and how composers have used them. The chapter motivates and explains how users of such systems describe intended musical results with constraints. This approach to algorithmic composition is similar to the way declarative and modular compositional rules have successfully been used in music theory for centuries as a device to describe composition techniques. In a systematic overview, this survey highlights the respective strengths of different approaches and systems from a composer's point of view, complementing other more technical surveys of this field. This text describes the music constraint systems PMC, Score-PMC, PWMC (and its successor Cluster Engine), Strasheela and Orchidée -- most are libraries of the composition systems PWGL or OpenMusic. These systems are shown in action by discussing the composition process of specific works by Jacopo Baboni-Schilingi, Magnus Lindberg, Örjan Sandred, Torsten Anders, Johannes Kretz and Jonathan Harvey

Compositions created with constraint programming

This chapter surveys music constraint programming systems, and how composers have used them. The chapter motivates and explains how users of such systems describe intended musical results with constraints. This approach to algorithmic composition is similar to the way declarative and modular compositional rules have successfully been used in music theory for centuries as a device to describe composition techniques. In a systematic overview, this survey highlights the respective strengths of different approaches and systems from a composer's point of view, complementing other more technical surveys of this field. This text describes the music constraint systems PMC, Score-PMC, PWMC (and its successor Cluster Engine), Strasheela and Orchidée -- most are libraries of the composition systems PWGL or OpenMusic. These systems are shown in action by discussing the composition process of specific works by Jacopo Baboni-Schilingi, Magnus Lindberg, Örjan Sandred, Torsten Anders, Johannes Kretz and Jonathan Harvey

Generative Design in Minecraft (GDMC), Settlement Generation Competition

This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio

Orchestrating Game Generation

The design process is often characterized by and realized through the iterative steps of evaluation and refinement. When the process is based on a single creative domain such as visual art or audio production, designers primarily take inspiration from work within their domain and refine it based on their own intuitions or feedback from an audience of experts from within the same domain. What happens, however, when the creative process involves more than one creative domain such as in a digital game? How should the different domains influence each other so that the final outcome achieves a harmonized and fruitful communication across domains? How can a computational process orchestrate the various computational creators of the corresponding domains so that the final game has the desired functional and aesthetic characteristics? To address these questions, this article identifies game facet orchestration as the central challenge for AI-based game generation, discusses its dimensions and reviews research in automated game generation that has aimed to tackle it. In particular, we identify the different creative facets of games, we propose how orchestration can be facilitated in a top-down or bottom-up fashion, we review indicative preliminary examples of orchestration, and we conclude by discussing the open questions and challenges ahead

Recomposing Beethoven with Music Neurotechnology

Musicians have an extraordinary opportunity today to develop new approaches to composition that would have been unthinkable a few years ago. Imagine if you could play a musical instrument with signals detected directly from your brain. Would it be possible to generate music representing brain activity? What would the music of our brains sound like? These are some of the questions addressed by research into Music Neurotechnology , which is an emerging field at the crossroads of music, technology and neuroscience. There has been a great number of very interesting initiatives within the last decade or so to sonify brainwaves, some of which might indeed be useful for creative musical purposes. Also, the burgeoning field of Brain-Computer Music Interfacing (BCMI) is developing powerful methods to generate music in real-time by means of brainwave signals some initiatives of which are even looking into harnessing the potential of biomedically uncertified low-cost equipment for BCMI applications. However, in this chapter we discuss an approach that goes beyond sonification of brainwaves and BCMI. We introduce algorithms that we have been developing to compose orchestral music off-line with fMRI brain scans. The chapter is concerned with the impact of Music Neurotechnology to the field of Computer-Aided Composition (CAC). As we are not concerned with real-time interaction here, we have an opportunity to take advantage of the fMRI brain scanning method. This method is deemed too cumbersome for real-time applications, but considerably more powerful and informative than EEG (electroencephalogram) scanning, which is the method used in sonification and BCMI research. The composition methods introduced below were developed in OpenMusic, originally to generate materials for two symphonies by Miranda: Symphony of Minds Listening (2013) and Corpus Callosum (2015). And they were subsequently used to compose Shockwaves (2015) a violin concertino for orchestra. The discussions in this chapter will be mostly in the context of Symphony of Minds Listening and Corpus Callosum . We begin the chapter by briefly introducing Miranda’s approach to composing with the aid of computers, focusing on using algorithmically generated materials. Then we introduce the compositions Symphony of Minds Listening and Corpus Callosum. Next, we focus on the technical details of collecting and handling fMRI data, followed by an overview of the OpenMusic patches that we developed for this project and an explanation of how ATO-MS was used to generate orchestrations based on fMRI information. The chapter ends with a brief concluding discussion and acknowledgements to contributors and sponsors

Management of generic and multi-platform workflows for exploiting heterogeneous environments on e-Science

Scientific Workflows (SWFs) are widely used to model applications in e-Science. In this programming model, scientific applications are described as a set of tasks that have dependencies among them. During the last decades, the execution of scientific workflows has been successfully performed in the available computing infrastructures (supercomputers, clusters and grids) using software programs called Workflow Management Systems (WMSs), which orchestrate the workload on top of these computing infrastructures. However, because each computing infrastructure has its own architecture and each scientific applications exploits efficiently one of these infrastructures, it is necessary to organize the way in which they are executed. WMSs need to get the most out of all the available computing and storage resources. Traditionally, scientific workflow applications have been extensively deployed in high-performance computing infrastructures (such as supercomputers and clusters) and grids. But, in the last years, the advent of cloud computing infrastructures has opened the door of using on-demand infrastructures to complement or even replace local infrastructures. However, new issues have arisen, such as the integration of hybrid resources or the compromise between infrastructure reutilization and elasticity, everything on the basis of cost-efficiency. The main contribution of this thesis is an ad-hoc solution for managing workflows exploiting the capabilities of cloud computing orchestrators to deploy resources on demand according to the workload and to combine heterogeneous cloud providers (such as on-premise clouds and public clouds) and traditional infrastructures (supercomputers and clusters) to minimize costs and response time. The thesis does not propose yet another WMS, but demonstrates the benefits of the integration of cloud orchestration when running complex workflows. The thesis shows several configuration experiments and multiple heterogeneous backends from a realistic comparative genomics workflow called Orthosearch, to migrate memory-intensive workload to public infrastructures while keeping other blocks of the experiment running locally. The running time and cost of the experiments is computed and best practices are suggested.Los flujos de trabajo científicos son comúnmente usados para modelar aplicaciones en e-Ciencia. En este modelo de programación, las aplicaciones científicas se describen como un conjunto de tareas que tienen dependencias entre ellas. Durante las últimas décadas, la ejecución de flujos de trabajo científicos se ha llevado a cabo con éxito en las infraestructuras de computación disponibles (supercomputadores, clústers y grids) haciendo uso de programas software llamados Gestores de Flujos de Trabajos, los cuales distribuyen la carga de trabajo en estas infraestructuras de computación. Sin embargo, debido a que cada infraestructura de computación posee su propia arquitectura y cada aplicación científica explota eficientemente una de estas infraestructuras, es necesario organizar la manera en que se ejecutan. Los Gestores de Flujos de Trabajo necesitan aprovechar el máximo todos los recursos de computación y almacenamiento disponibles. Habitualmente, las aplicaciones científicas de flujos de trabajos han sido ejecutadas en recursos de computación de altas prestaciones (tales como supercomputadores y clústers) y grids. Sin embargo, en los últimos años, la aparición de las infraestructuras de computación en la nube ha posibilitado el uso de infraestructuras bajo demanda para complementar o incluso reemplazar infraestructuras locales. No obstante, este hecho plantea nuevas cuestiones, tales como la integración de recursos híbridos o el compromiso entre la reutilización de la infraestructura y la elasticidad, todo ello teniendo en cuenta que sea eficiente en el coste. La principal contribución de esta tesis es una solución ad-hoc para gestionar flujos de trabajos explotando las capacidades de los orquestadores de recursos de computación en la nube para desplegar recursos bajo demando según la carga de trabajo y combinar proveedores de computación en la nube heterogéneos (privados y públicos) e infraestructuras tradicionales (supercomputadores y clústers) para minimizar el coste y el tiempo de respuesta. La tesis no propone otro gestor de flujos de trabajo más, sino que demuestra los beneficios de la integración de la orquestación de la computación en la nube cuando se ejecutan flujos de trabajo complejos. La tesis muestra experimentos con diferentes configuraciones y múltiples plataformas heterogéneas, haciendo uso de un flujo de trabajo real de genómica comparativa llamado Orthosearch, para traspasar cargas de trabajo intensivas de memoria a infraestructuras públicas mientras se mantienen otros bloques del experimento ejecutándose localmente. El tiempo de respuesta y el coste de los experimentos son calculados, además de sugerir buenas prácticas.Els fluxos de treball científics són comunament usats per a modelar aplicacions en e-Ciència. En aquest model de programació, les aplicacions científiques es descriuen com un conjunt de tasques que tenen dependències entre elles. Durant les últimes dècades, l'execució de fluxos de treball científics s'ha dut a terme amb èxit en les infraestructures de computació disponibles (supercomputadors, clústers i grids) fent ús de programari anomenat Gestors de Fluxos de Treballs, els quals distribueixen la càrrega de treball en aquestes infraestructures de computació. No obstant açò, a causa que cada infraestructura de computació posseeix la seua pròpia arquitectura i cada aplicació científica explota eficientment una d'aquestes infraestructures, és necessari organitzar la manera en què s'executen. Els Gestors de Fluxos de Treball necessiten aprofitar el màxim tots els recursos de computació i emmagatzematge disponibles. Habitualment, les aplicacions científiques de fluxos de treballs han sigut executades en recursos de computació d'altes prestacions (tals com supercomputadors i clústers) i grids. No obstant açò, en els últims anys, l'aparició de les infraestructures de computació en el núvol ha possibilitat l'ús d'infraestructures sota demanda per a complementar o fins i tot reemplaçar infraestructures locals. No obstant açò, aquest fet planteja noves qüestions, tals com la integració de recursos híbrids o el compromís entre la reutilització de la infraestructura i l'elasticitat, tot açò tenint en compte que siga eficient en el cost. La principal contribució d'aquesta tesi és una solució ad-hoc per a gestionar fluxos de treballs explotant les capacitats dels orquestadors de recursos de computació en el núvol per a desplegar recursos baix demande segons la càrrega de treball i combinar proveïdors de computació en el núvol heterogenis (privats i públics) i infraestructures tradicionals (supercomputadors i clústers) per a minimitzar el cost i el temps de resposta. La tesi no proposa un gestor de fluxos de treball més, sinó que demostra els beneficis de la integració de l'orquestració de la computació en el núvol quan s'executen fluxos de treball complexos. La tesi mostra experiments amb diferents configuracions i múltiples plataformes heterogènies, fent ús d'un flux de treball real de genòmica comparativa anomenat Orthosearch, per a traspassar càrregues de treball intensives de memòria a infraestructures públiques mentre es mantenen altres blocs de l'experiment executant-se localment. El temps de resposta i el cost dels experiments són calculats, a més de suggerir bones pràctiques.Carrión Collado, AA. (2017). Management of generic and multi-platform workflows for exploiting heterogeneous environments on e-Science [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86179TESI

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