Explainable A.I.: The Promise of Genetic Programming Multi-run Subtree Encapsulation

Created by W.Langdon from gp-bibliography.bib Revision:1.5368

@InProceedings{Howard:2018:iCMLDEb,

• author = "Daniel Howard and Mark A. Edwards",
• title = "Explainable {A.I.}: The Promise of Genetic Programming Multi-run Subtree Encapsulation",
• booktitle = "2018 International Conference on Machine Learning and Data Engineering (iCMLDE)",
• year = "2018",
• pages = "158--159",
• address = "Sydney, Australia",
• month = "3-7 " # dec,
• organisation = "Western Sydney University",
• keywords = "genetic algorithms, genetic programming, Explainable Artificial Intelligence, AI, Evolutionary Computation, modularization, Subtree Encapsulation, Automatically Defined Functions, ADF, Software Evolution, white box, black box, expression simplification, Deep Learning, Artificial Neural Networks, Multirun Subtree Encapsulation, subtree database",
• URL = "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/icmlde_2018/Howard_2018_iCMLDEb.pdf",
• URL = "https://www.researchgate.net/publication/330475457_Evomorph_Morphological_Modularization_in_AI_for_Machine_Vision_Inspired_by_Embryology",
• DOI = "doi:10.1109/iCMLDE.2018.00037",
• size = "2 pages",
• abstract = "Deep Learning and other Artificial Neural Network based solutions are rarely transparent, and white-box solutions are often called for. This paper explains how Multirun Subtree Encapsulation can provide equivalent white box solutions to facilitate Explainable Artificial Intelligence.",
• notes = "Howard Science Ltd, Malvern, UK http://www.icmlde.net.au/IndustrialTrack.aspx Also known as \cite{8614020}",

}

Genetic Programming entries for Daniel Howard Mark A Edwards

Citations