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Automated Planning for Ubiquitous Computing

Published:05 December 2016Publication History
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Abstract

The goal of ubiquitous computing is to create ambience in which one’s experiences and quality of life are improved by monitoring and assisting people using ubiquitous technologies and computation in coherence. The continuous advancements of involved technologies, such as wireless communications, mobile devices, and sensors, imply fast evolution of ubiquitous computing environments too. The complexity of these environments is reaching a point where traditional solutions simply no longer work. The environments are in need of computational techniques that can deal with the evolution and uncertainty of ubiquitous computing environments dynamically and automatically. Artificial Intelligence (AI) can contribute towards satisfying this future scenario in many ways, while numerous approaches inspired by work in the AI planning community have already been designed for ubiquitous computing. We devote this study to investigate the current progress of AI planning for ubiquitous computing by analysing those approaches. We rigorously search for and select relevant literature out of which we extract qualitative information. Using the extracted qualities, we derive a generic framework that consists of aspects important to planning for ubiquitous computing. The framework’s main purpose is to facilitate the understanding of those aspects, and classify the literature according to them. We then analyse the literature in a consolidated way, and identify future challenges of planning for ubiquitous computing.

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  1. Automated Planning for Ubiquitous Computing

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              cover image ACM Computing Surveys
              ACM Computing Surveys  Volume 49, Issue 4
              December 2017
              666 pages
              ISSN:0360-0300
              EISSN:1557-7341
              DOI:10.1145/3022634
              • Editor:
              • Sartaj Sahni
              Issue’s Table of Contents

              Copyright © 2016 ACM

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              Publication History

              • Published: 5 December 2016
              • Accepted: 1 October 2016
              • Revised: 1 September 2016
              • Received: 1 August 2015
              Published in csur Volume 49, Issue 4

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