Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
research-article
Open access

Computational sustainability: computing for a better world and a sustainable future

Published: 21 August 2019 Publication History
  • Get Citation Alerts
  • Abstract

    Computer and information scientists join forces with other fields to help solve societal and environmental challenges facing humanity, in pursuit of a sustainable future.

    References

    [1]
    Abdelrahman, H., Berkenkamp, F., Poland, J., and Krause, A. Bayesian optimization for maximum power point tracking in photovoltaic power plants. In Proceedings of the 2016 European Control Con. (Aalborg, Denmark, June 29--July 1, 2016), 2078--2083.
    [2]
    Albers, J.H., Dietterich, T., Hall, K., Katherine, L., and Taleghan, M. Simulator-defined Markov decision processes: A case study in managing bio-invasions. Artificial Intelligence and Conservation (2nd. ed.). F. Fang, M. Tambe, B. Dilkina, and A. Plumptre, (Eds.). Cambridge Univ. Press, 2018
    [3]
    Azimi, J., Fern, X., and Fern, A. Budgeted optimization with constrained experiments. J. Artif. Intell. Res. 56 (2016), 119--152.
    [4]
    Bai, J. et al. Phase mapper: Accelerating materials discovery with AI. AI Mag. 39, 1 (2018), 15--26.
    [5]
    Barrett, C., Garg, T., and McBride, L. Well-being dynamics and poverty traps. Annual Review of Resource Economics 8 (2016), 303--327.
    [6]
    Bernstein, G., McKenna, R., Sun, T., Sheldon, D., Hay, M., and Miklau, G. Differentially private learning of undirected graphical models using collective graphical models. In Proceedings of the 34<sup>th</sup> International Conference on Machine Learning, 2017, 478--487.
    [7]
    Chen, D., Xue, Y., and Gomes, C. End-to-end learning for the deep multivariate probit model. ICML (2018).
    [8]
    Coble, K., Mishra, A., Ferrell, S., and Griffin, T. Big data in agriculture: A challenge for the future. Applied Economic Perspectives and Policy 40, 1 (2018), 79--96.
    [9]
    Dilkina, B. et al. Trade-offs and efficiencies in optimal budget-constrained multi-species corridor networks. Conservation Biology 31, 1 (2017), 192--202.
    [10]
    Donti, P., Kolter, J.Z., and Amos, B. Task-based end-to-end model learning in stochastic optimization. In Proceedings of Advances in Neural Information Processing Systemsg Systems (Long Beach, CA, USA, Dec. 4--9, 2017), 5490--5500.
    [11]
    Ermon, S. et al. Learning large-scale dynamic discrete choice models of spatio-temporal preferences with application to migratory pastoralism in East Africa. In AAAI, 2015, 644--650.
    [12]
    Faghmous, J. and Kumar, V. A big data guide to understanding climate change: The case for theory-guided data science. Big Data 2, 3 (2014), 155--163.
    [13]
    Fang, F. et al. PAWS---A deployed game-theoretic application to combat poaching. AI Magazine 38, 1 (2017), 23--36.
    [14]
    Fang, F., Tambe, M., Dilkina, B., and Plumptre, A (eds.). Artificial Intelligence and Conservation. Cambridge University Press, 2018.
    [15]
    Fink, D. et al. Spatiotemporal exploratory models for broad-scale survey data. Ecological Applications 20, 8 (2010), 2131--2147.
    [16]
    Fisher, D.H. Recent advances in AI for computational sustainability. IEEE Intelligent Systems 31, 4 (2016), 56--61
    [17]
    Freund, D., Henderson, S.G., and Shmoys, D.B. Sharing Economy: Making Supply Meet Demand. Springer, 2018.
    [18]
    Gomes, C.P. Computational sustainability: Computational methods for a sustainable environment, economy, and society. The Bridge 39, 4 (2009), 5--13.
    [19]
    Grover, A. et al. Best arm identification in multi-armed bandits with delayed feedback. In Proceedings of the Inter Conf. Artificial Intelligence and Statistics (Playa Blanca, Lanzarote, Canary Islands, Spain, April 9--11, 2018), 833--842.
    [20]
    Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., and Ermon, S. Combining satellite imagery and machine learning to predict poverty. Science 353, 6301 (2016), 790--794.
    [21]
    Kelling, S. et al. Can observation skills of citizen scientists be estimated using species accumulation curves? PloS one 10, 10 (2015).
    [22]
    Khazaei, J. and Powell, W.B. SMART-Invest: A stochastic, dynamic planning for optimizing investments in wind, solar, and storage in the presence of fossil fuels. The case of the PJM electricity market. Energy Systems 9, 2 (2018), 277--303.
    [23]
    Kraus, S. Automated negotiation and decision-making in multiagent environments. ECCAI Advanced Course on Artificial Intelligence. Springer, 2001, 150--172.
    [24]
    Lässig, J., Kersting, K., and Morik, K (Eds.). Computational Sustainability. Studies in Computational Intelligence 645 (2016). Springer.
    [25]
    Phillips, S.J., Anderson, R.P., and Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecological Modeling 190, 3--4 (2006), 231--259.
    [26]
    Powell, W. A unified framework for stochastic optimization. European J. Operational Research 275, 3 (2019), 795--821.
    [27]
    Reynolds, M.D. et al. Dynamic conservation for migratory species. Science Advances 3, 8 (2017), e1700707.
    [28]
    Rockström, J. et al. Planetary boundaries: Exploring the safe operating space for humanity. Ecology and Society 14, 2 (2009).
    [29]
    Rudin, C. and Wagstaff, K. Machine learning for science and society. Machine Learning 95, 1 (2014), 1--9
    [30]
    Ruiz-Muñoz, J.F., You, Z., Raich, R., and Fern, X.Z. Dictionary learning for bioacoustics monitoring with applications to species classification. Signal Processing Systems 90, 2 (2018), 233--247.
    [31]
    Russell, S.J. et al. Letter to the editor: Research priorities for robust and beneficial artificial intelligence: An open letter. AI Magazine 36, 4 (2015).
    [32]
    Sheldon, D.R. and Dietterich, T.G. Collective graphical models. Advances in Neural Information Processing Systems, 2011, 1161--1169.
    [33]
    Sheldon, D.R. et al. Approximate Bayesian inference for reconstructing velocities of migrating birds from weather radar. In Proceedings of the 27<sup>th</sup> AAAI Conf. Artificial Intelligence. (Bellevue, WA, USA, July 14--18, 2013).
    [34]
    Sullivan, B.L. et al. The eBird enterprise: An integrated approach to development and application of citizen science. Biological Conservation 169 (2014), 31--40.
    [35]
    Tambe, M. and Rice, E (eds.). Artificial Intelligence and Social Work. Cambridge University Press, 2018.
    [36]
    Giesen, N., Hut, R., and Selker, J. The Trans-African Hydro-Meteorological Observatory (TAHMO). Wiley Interdisciplinary Reviews: Water 1, 4 (2014), 341--348.
    [37]
    Wahabzada, M., Mahlein, A.K., Bauckhage, C., Steiner, U., Oerke, E.C., and Kersting, K. Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants. Scientific Reports 6 (2016).
    [38]
    Wu, X. et al. Efficiently approximating the Pareto frontier: Hydropower dam placement in the Amazon basin. In AAAI (2018).
    [39]
    Xue, Y., Davies, I., Fink, D., Wood, C., and Gomes, C.P. Avicaching: A two stage game for bias reduction in citizen science. In Proceedings of the 2016 Intl. Conf. on Autonomous Agents & Multiagent Systems. 776--785.
    [40]
    Yadav, A. et al. Influence maximization in the field: The arduous journey from emerging to deployed application. In Proceedings of the 16<sup>th</sup> Conference on Autonomous Agents and MultiAgent Systems, 2017, 150--158.

    Cited By

    View all
    • (2024)ICT under Constraint: Exposing Tensions in Collaboratively Prioritising ICT Innovation for Climate TargetsACM Journal on Responsible Computing10.1145/36482341:2(1-21)Online publication date: 11-Mar-2024
    • (2024)Sustainability in Computing Education: A Systematic Literature ReviewACM Transactions on Computing Education10.1145/363906024:1(1-53)Online publication date: 5-Jan-2024
    • (2024)Exploring the association between engagement with location-based game features and getting inspired about environmental issues and natureProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642786(1-15)Online publication date: 11-May-2024
    • Show More Cited By

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image Communications of the ACM
    Communications of the ACM  Volume 62, Issue 9
    September 2019
    95 pages
    ISSN:0001-0782
    EISSN:1557-7317
    DOI:10.1145/3358415
    Issue’s Table of Contents
    This work is licensed under a Creative Commons Attribution International 4.0 License.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 August 2019
    Published in CACM Volume 62, Issue 9

    Permissions

    Request permissions for this article.

    Check for updates

    Qualifiers

    • Research-article
    • Popular
    • Refereed

    Funding Sources

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)5,477
    • Downloads (Last 6 weeks)246

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)ICT under Constraint: Exposing Tensions in Collaboratively Prioritising ICT Innovation for Climate TargetsACM Journal on Responsible Computing10.1145/36482341:2(1-21)Online publication date: 11-Mar-2024
    • (2024)Sustainability in Computing Education: A Systematic Literature ReviewACM Transactions on Computing Education10.1145/363906024:1(1-53)Online publication date: 5-Jan-2024
    • (2024)Exploring the association between engagement with location-based game features and getting inspired about environmental issues and natureProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642786(1-15)Online publication date: 11-May-2024
    • (2024)Advancing computational sustainability in higher educationNature Computational Science10.1038/s43588-024-00638-z4:6(382-383)Online publication date: 7-Jun-2024
    • (2024)The carbon emissions of writing and illustrating are lower for AI than for humansScientific Reports10.1038/s41598-024-54271-x14:1Online publication date: 14-Feb-2024
    • (2024)Scientists’ warning on technologyJournal of Cleaner Production10.1016/j.jclepro.2023.140074434(140074)Online publication date: Jan-2024
    • (2024)Strategies for Compressing the Pareto Frontier: Application to Strategic Planning of Hydropower in the Amazon BasinIntegration of Constraint Programming, Artificial Intelligence, and Operations Research10.1007/978-3-031-60599-4_9(141-157)Online publication date: 28-May-2024
    • (2023)Beyond Metrics: Navigating AI through Sustainable ParadigmsSustainability10.3390/su15241678915:24(16789)Online publication date: 13-Dec-2023
    • (2023)The Method for Identifying the Scope of Cyberattack Stages in Relation to Their Impact on Cyber-Sustainability Control over a SystemElectronics10.3390/electronics1203059112:3(591)Online publication date: 25-Jan-2023
    • (2023)User-centric democratization towards social value aligned medical AI servicesProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/702(6326-6334)Online publication date: 19-Aug-2023
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Digital Edition

    View this article in digital edition.

    Digital Edition

    Magazine Site

    View this article on the magazine site (external)

    Magazine Site

    Get Access

    Login options

    Full Access

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media