2,368 research outputs found
A computational framework for aesthetical navigation in musical search space
Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper
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
Arbitrarily Scalable Environment Generators via Neural Cellular Automata
We study the problem of generating arbitrarily large environments to improve
the throughput of multi-robot systems. Prior work proposes Quality Diversity
(QD) algorithms as an effective method for optimizing the environments of
automated warehouses. However, these approaches optimize only relatively small
environments, falling short when it comes to replicating real-world warehouse
sizes. The challenge arises from the exponential increase in the search space
as the environment size increases. Additionally, the previous methods have only
been tested with up to 350 robots in simulations, while practical warehouses
could host thousands of robots. In this paper, instead of optimizing
environments, we propose to optimize Neural Cellular Automata (NCA) environment
generators via QD algorithms. We train a collection of NCA generators with QD
algorithms in small environments and then generate arbitrarily large
environments from the generators at test time. We show that NCA environment
generators maintain consistent, regularized patterns regardless of environment
size, significantly enhancing the scalability of multi-robot systems in two
different domains with up to 2,350 robots. Additionally, we demonstrate that
our method scales a single-agent reinforcement learning policy to arbitrarily
large environments with similar patterns. We include the source code at
\url{https://github.com/lunjohnzhang/warehouse_env_gen_nca_public}.Comment: Accepted to Advances in Neural Information Processing Systems
(NeurIPS), 202
The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications
The principle goal of computational mechanics is to define pattern and
structure so that the organization of complex systems can be detected and
quantified. Computational mechanics developed from efforts in the 1970s and
early 1980s to identify strange attractors as the mechanism driving weak fluid
turbulence via the method of reconstructing attractor geometry from measurement
time series and in the mid-1980s to estimate equations of motion directly from
complex time series. In providing a mathematical and operational definition of
structure it addressed weaknesses of these early approaches to discovering
patterns in natural systems.
Since then, computational mechanics has led to a range of results from
theoretical physics and nonlinear mathematics to diverse applications---from
closed-form analysis of Markov and non-Markov stochastic processes that are
ergodic or nonergodic and their measures of information and intrinsic
computation to complex materials and deterministic chaos and intelligence in
Maxwellian demons to quantum compression of classical processes and the
evolution of computation and language.
This brief review clarifies several misunderstandings and addresses concerns
recently raised regarding early works in the field (1980s). We show that
misguided evaluations of the contributions of computational mechanics are
groundless and stem from a lack of familiarity with its basic goals and from a
failure to consider its historical context. For all practical purposes, its
modern methods and results largely supersede the early works. This not only
renders recent criticism moot and shows the solid ground on which computational
mechanics stands but, most importantly, shows the significant progress achieved
over three decades and points to the many intriguing and outstanding challenges
in understanding the computational nature of complex dynamic systems.Comment: 11 pages, 123 citations;
http://csc.ucdavis.edu/~cmg/compmech/pubs/cmr.ht
Cellular automata for real-time generation of infinite cave levels
This paper presents a reliable and efficient approach to procedurally generating level maps based on the self-organization capabilities of cellular automata (CA). A simple CA-based algorithm is evaluated on an infinite cave game, generating playable and well-designed tunnel-based maps. The algorithm has very low computational cost, permitting realtime content generation, and the proposed map representation provides sufficient flexibility with respect to level design.peer-reviewe
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 ConsejerÃa de Innovación y Ciencia de AndalucÃa
Computational composition strategies in audiovisual laptop performance
We live in a cultural environment in which computer based musical performances have become ubiquitous. Particularly the use of laptops as instruments is a thriving practice in many genres and subcultures. The opportunity to command the most intricate level of control on the smallest of time scales in music composition and computer graphics introduces a number of complexities and dilemmas for the performer working with algorithms. Writing computer code to create audiovisuals offers abundant opportunities for discovering new ways of expression in live performance while simultaneously introducing challenges and presenting the user with difficult choices. There are a host of computational strategies that can be employed in live situations to assist the performer, including artificially intelligent performance agents who operate according to predefined algorithmic rules. This thesis describes four software systems for real time multimodal improvisation and composition in which a number of computational strategies for audiovisual laptop performances is explored and which were used in creation of a portfolio of accompanying audiovisual compositions
The Algorithmic Composition of Classical Music through Data Mining
The desire to teach a computer how to algorithmically compose music has been a topic in the world of computer science since the 1950’s, with roots of computer-less algorithmic composition dating back to Mozart himself. One limitation of algorithmically composing music has been the difficulty of eliminating the human intervention required to achieve a musically homogeneous composition. We attempt to remedy this issue by teaching a computer how the rules of composition differ between the six distinct eras of classical music by having it examine a dataset of musical scores, rather than explicitly telling the computer the formal rules of composition. To pursue this automated composition process, we examined the intersectionality of algorithmic composition with the machine learning concept of classification. Using a Naïve Bayes classifier, the computer classifies pieces of classical music into their respective era based upon a number of attributes. It then attempts to recreate each of the six classical styles using a technique inspired by cellular automata. The success of this process is twofold determined by feeding composition samples into a number of classifiers, as well as analysis by studied musicians. We concluded that there is potential for further hybridization of classification and composition techniques
Modelling Immunological Memory
Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves
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