Zobrazeno 1 - 10
of 109
pro vyhledávání: '"Colin G. Johnson"'
Publikováno v:
Complexity, Vol 2019 (2019)
One of the general aims of evolutionary art research is to build a computer system capable of creating interesting, beautiful, or creative results, including images, videos, animations, text, and performances. In this context, it is crucial to unders
Externí odkaz:
https://doaj.org/article/01b1dea8cd164a138e586625bc7bc85e
Autor:
Fabio Paolizzo, Colin G. Johnson
Publikováno v:
Journal of New Music Research. 49:115-125
Interactive music systems always exhibit an amount of autonomy in the creative process. The capacity to generate material that is primary, contextual and novel to the outcome is proposed here as the bare minimum for creative autonomy in these systems
Autor:
Colin G. Johnson
Publikováno v:
Expert Systems. 38
This paper explores a novel technique for learning the fitness function for search algorithms such as evolutionary strategies and hillclimbing. The aim of the new technique is to learn a fitness function (called a Learned Guidance Function) from a se
Autor:
Colin G. Johnson
Publikováno v:
Artificial Intelligence in Music, Sound, Art and Design ISBN: 9783030729134
EvoMUSART
EvoMUSART
This paper describes an ongoing project to create a “digital mirror” to my practice as a composer of contemporary classical music; that is, a system that takes descriptions (in code) of aspects of that practice, and reflects them back as computer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::41611d65a1d65a268c0c6a481d85bf9c
https://doi.org/10.1007/978-3-030-72914-1_28
https://doi.org/10.1007/978-3-030-72914-1_28
Publikováno v:
EMBC
This work demonstrates the effectiveness of Convolutional Neural Networks in the task of pose estimation from Electromyographical (EMG) data. The Ninapro DB5 dataset was used to train the model to predict the hand pose from EMG data. The models predi
Autor:
Valeria de Carvalho Santos, Fernando E. B. Otero, Fernando Santos Osório, Colin G. Johnson, Claudio Fabiano Motta Toledo
Publikováno v:
GECCO
In the path planning task for autonomous mobile robots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several path planning approaches for mobile robots in the literature. Ant Colony
Autor:
Colin G. Johnson
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030637989
SGAI Conf.
SGAI Conf.
Software development is a complex activity requiring intelligent action. This paper explores the use of an AI technique for one step in software development, viz. detecting the location of a fault in a program. A measure of program progress is propos
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1d3b8061128e49cdea437fbecc4fcd5c
https://doi.org/10.1007/978-3-030-63799-6_20
https://doi.org/10.1007/978-3-030-63799-6_20
Autor:
Colin G. Johnson
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030348847
SGAI Conf.
SGAI Conf.
This paper explores how Learned Guidance Functions (LGFs)— a pre-training method used to smooth search landscapes—can be used as a fitness function for evolutionary algorithms. A new form of LGF is introduced, based on deep neural network learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f1105b96ad8a377af5d740446a14426
https://kar.kent.ac.uk/78198/1/BCS_SGAI_2019.pdf
https://kar.kent.ac.uk/78198/1/BCS_SGAI_2019.pdf
Autor:
Emily P. Midgette, Brinda Sarathy, Matthew C. Whited, Jordan M. Ellis, Colin G. Johnson, Anup P. Sanghvi, Stacey B. Greenway, John Taylor Freeman
Publikováno v:
Journal of Cardiopulmonary Rehabilitation and Prevention. 39:E13-E15
Purpose To provide a prototypical patient narrative of the cardiac rehabilitation (CR) experience for providers and prospective patients using narrative analysis. Methods Qualitative interviews with 17 CR patients from a previous study regarding thei
Publikováno v:
Neurocomputing. 221:32-47
This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to pr