{
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  "dcterms:identifier": [
    "tag:aim-pro.eu,2026:oer/b4c68e54f0ee",
    "https://arxiv.org/abs/2211.03219",
    "arxiv:2211.03219"
  ],
  "dcterms:title": [
    "B-SMART: A Reference Architecture for Artificially Intelligent Autonomic Smart Buildings"
  ],
  "dcterms:type": [
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    "preprint"
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  "dcterms:creator": [
    "Mikhail Genkin",
    "J. J. McArthur"
  ],
  "dcterms:description": [
    "The pervasive application of artificial intelligence and machine learning algorithms is transforming many industries and aspects of the human experience. One very important industry trend is the move to convert existing human dwellings to smart buildings, and to create new smart buildings. Smart buildings aim to mitigate climate change by reducing energy consumption and associated carbon emissions. To accomplish this, they leverage artificial intelligence, big data, and machine learning algorithms to learn and optimize system performance. These fields of research are currently very rapidly evolving and advancing, but there has been very little guidance to help engineers and architects working on smart buildings apply artificial intelligence algorithms and technologies in a systematic and effective manner. In this paper we present B-SMART: the first reference architecture for autonomic smart buildings. B-SMART facilitates the application of artificial intelligence techniques and technologies to smart buildings by decoupling conceptually distinct layers of functionality and organizing them into an autonomic control loop. We also present a case study illustrating how B-SMART can be applied to accelerate the introduction of artificial intelligence into an existing smart building."
  ],
  "dcterms:subject": [
    "cs.AI (arxiv)"
  ],
  "dcterms:language": [
    "en"
  ],
  "dcterms:license": [
    {
      "@id": "http://creativecommons.org/licenses/by-nc-sa/4.0/"
    }
  ],
  "dcterms:rights": [
    "CC-BY-NC-SA-4.0 — assessed as OPEN_NC by the harvester's licence gate. Conditions: attribution required, non-commercial use only, adaptations must carry the same licence. open but Non-Commercial — SME/commercial reuse needs care"
  ],
  "dcterms:accessRights": [
    "open"
  ],
  "dcterms:format": [
    "text/markdown"
  ],
  "dcterms:extent": [
    "33277 bytes (extracted text)"
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  "dcterms:issued": [
    "2022-11-06"
  ],
  "dcterms:provenance": [
    "Retrieved from arXiv on 2026-10-09 in response to the search string “(all:\"artificial intelligence\" OR all:\"machine learning\" OR all:\"generative AI\" OR all:\"deep learning\" OR all:\"reinforcement learning\" OR all:\"large language model\") AND (all:\"AI concepts\" OR all:\"types of AI\" OR all:\"AI fundamentals\" OR all:\"recognizing AI\" OR all:\"recognising AI\" OR all:\"general versus narrow AI\" OR all:\"narrow AI\" OR all:\"general AI\" OR all:\"machine intelligence\" OR all:\"AI strengths and weaknesses\" OR all:\"traditional software\" OR all:\"rule-based systems\" OR all:\"introduction to AI\" OR all:\"introduction to artificial intelligence\" OR all:\"artificial intelligence introduction\" OR all:\"AI primer\" OR all:\"foundations of artificial intelligence\" OR all:\"overview of AI\" OR all:\"understanding AI\" OR all:\"history of AI\" OR all:\"AI essentials\" OR all:\"AI terminology\" OR all:\"metaphors for AI\" OR all:\"AI fundamental concepts\" OR all:\"AI key concepts\" OR all:\"philosophy of AI\" OR all:\"critical AI literacy\")”. arXiv served the resource and is not asserted to be its publisher or author.",
    "Text extracted from latexml-html to Markdown by arxiv-html; the original is retained unchanged beside it."
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}