Dr.

Ilche Georgievski

Lead of Smart Energy Systems Research Area
Institute for Architecture of Application Systems

Contact

+49 711 685-88459

Universitätsstraße 38
 70569 Stuttgart
Germany
Room: 0.353

Office Hours

By arrangement

  1. article

    1. I. Georgievski and M. Aiello, “Automated Planning for Ubiquitous Computing.,” ACM Comput. Surv., vol. 49, no. 4, pp. 63:1-63:46, 2017.
    2. I. Georgievski, T. A. Nguyen, F. Nizamic, B. Setz, A. Lazovik, and M. Aiello, “Planning meets activity recognition: Service coordination for intelligent buildings.,” Pervasive and Mobile Computing, vol. 38, pp. 110–139, 2017.
    3. I. Georgievski and M. Aiello, “HTN planning: Overview, comparison, and beyond.,” Artif. Intell., vol. 222, pp. 124–156, 2015.
    4. I. Georgievski, V. Degeler, G. A. Pagani, T. A. Nguyen, A. Lazovik, and M. Aiello, “Optimizing energy costs for offices connected to the smart grid,” IEEE Transactions on Smart Grid, vol. 3, no. 4, pp. 2273–2285, 2012.

  2. inproceedings

    1. I. Georgievski, L. Fiorini, and M. Aiello, “Towards Service-Oriented and Intelligent Microgrids,” in International Conference on Applications of Intelligent Systems, 2020.
    2. I. Georgievski and M. Aiello, “Phantomisation in state-based HTN planning,” in International Conference on Advances in Signal Processing and Artificial Intelligence, 2019, pp. 39--44.
    3. I. Georgievski, P. Gupta, and M. Aiello, “Activity learning for intelligent buildings,” in IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, 2019.
    4. M. Kalksma, B. Setz, A. R. Pratama, I. Georgievski, and M. Aiello, “Mining Sequential Patterns for Appliance Usage Prediction.,” in SMARTGREENS, 2018, pp. 23–33.
    5. I. Georgievski, F. Nizamic, A. Lazovik, and M. Aiello, “Cloud Ready Applications Composed via HTN Planning.,” in SOCA, 2017, pp. 81–89.
    6. I. Georgievski, F. Nizamic, A. Lazovik, and A. Marco, “Cloud Ready Applications Composed via HTN Planning,” in IEEE International Conference on Service Oriented Computing and Applications, 2017.
    7. I. Georgievski and T. Bouman, “On the relationship between automation and occupants in smart buildings,” in International Conference on ICT for Sustainability, 2016, pp. 240--241.
    8. I. Georgievski and A. Lazovik, “Utility-based HTN planning,” in European Conference on Artificial Intelligence, 2014, pp. 1013–1014.
    9. I. Georgievski, T. A. Nguyen, and M. Aiello, “Combining activity recognition and AI planning for energy-saving offices,” in IEEE International Conference on Ubiquitous Intelligence and Computing, 2013, pp. 238–245.
    10. I. Georgievski, “Planning for coordination of devices in energy-smart envronments,” in Doctoral Consortium of the 23rd International Conference on Automated Planning and Scheduling, 2013.
    11. V. Degeler, I. Georgievski, A. Lazovik, and M. Aiello, “Concept mapping for faster QoS-aware Web service composition,” in IEEE Conference on Service Oriented Computing and Applications, 2010, pp. 1–4.

  3. mastersthesis

    1. I. Georgievski, “Service-oriented architecture and cloud computing convergence,” 2010.

  4. phdthesis

    1. I. Georgievski, “Coordinating services embedded everywhere via hierarchical planning,” University of Groningen, 2015.

  5. techreport

    1. I. Georgievski and M. Aiello, “An overview of hierarchical task network planning,” CoRR, abs/1403.7426, 2014.
    2. I. Georgievski, “Hierarchical planning definition language,” University of Groningen, JBI 2013-12-3, 2013.
    3. I. Georgievski, D. Degeler, G. A. Pagani, T. A. Nguyen, A. Lazovik, and M. Aiello, “Optimizing Offices for the Smart Grid,” University of Groningen, JBI 2011-12-01, 2011.
    4. I. Georgievski, A. Lazovik, and M. Aiello, “Phantomization in an HTN Planner,” CoRR, abs/1111.7025, 2011.

Summer 2020

Winter 2019/2020

Summer 2019

Winter 2018/2019

Summer 2018

MatchIT

We integrate cross-sectorial expertise on power distribution, control systems, building automation, computer science, and social and behavioral science to propose an interdisciplinary framework that uses innovative distributed control algorithms and an ICT platform coupled with intelligent automated techniques to improve demand-supply matching in a financially and psychologically way that is attractive and acceptable to end-users.

To the top of the page