This image shows Ilche Georgievski

Ilche Georgievski

Dr.

Lead of Smart Energy Systems Research Area
Institute for Architecture of Application Systems
[Photo: Ilche Georgievski]

Contact

+49 711 685 88459
Website

Universitätsstraße 38
70569 Stuttgart
Germany
Room: 0.353

Office Hours

By arrangement

  1. 2022

    1. Georgievski, I.: Towards Engineering AI Planning Functionalities as Services. In: Service-Oriented Computing -- ICSOC 2019 Workshops. Springer International Publishing (2022).
    2. Alnazer, E., Georgievski, I., Prakash, N., Aiello, M.: A Role for HTN Planning in Increasing Trust in Autonomous Driving. In: IEEE International Smart Cities Conference. pp. 1–7 (2022). https://doi.org/10.1109/ISC255366.2022.9922427.
    3. Georgievski, I.: Office Activity Recognition Using HTN Planning. In: IEEE International Conference on Signal Image Technology & Internet-Based Systems (2022).
    4. Alnazer, E., Georgievski, I., Aiello, M.: On Bringing HTN Domains Closer to Reality - The Case of Satellite and Rover Domains. In: International Conference on Automated Planning Systems (ICAPS) Workshop on Scheduling and Planning Applications (SPARK) (2022).
    5. Alnazer, E., Georgievski, I., Aiello, M.: Risk Awareness in HTN Planning. arXiv (2022).

  2. 2021

    1. Aiello, M., Fiorini, L., Georgievski, I.: Software Engineering Smart Energy Systems. In: Fathi, M., Zio, E., and Pardalos, P.M. (eds.) Handbook of Smart Energy Systems. pp. 1--21. Springer (2021). https://doi.org/10.1007/978-3-030-72322-4_21-1.
    2. Georgievski, I.: HTN Planning Domain for Deployment of Cloud Applications. In: 10th International Planning Competition: Planner and Domain Abstracts. pp. 34–46 (2021).
    3. Georgievski, I., Jeyakumar, I.H.J., Kale, S.: Designing a System based on Robotic Assistance For Privacy Awareness in Smart Environment. In: IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference. pp. 427--432 (2021). https://doi.org/10.1109/UEMCON53757.2021.9666672.
    4. Georgievski, I., Breitenbücher, U.: A Vision for Composing, Integrating, and Deploying AI Planning Functionalities. In: IEEE International Conference on Service-Oriented System Engineering. pp. 166--171 (2021).
    5. Graef, S., Georgievski, I.: Software Architecture for Next-Generation AI Planning Systems. arXiv (2021).

  3. 2020

    1. Georgievski, I., Fiorini, L., Aiello, M.: Towards Service-Oriented and Intelligent Microgrids. In: International Conference on Applications of Intelligent System. pp. 18:1--18:6 (2020). https://doi.org/10.1145/3378184.3378214.

  4. 2019

    1. Georgievski, I., Aiello, M.: Phantomisation in state-based HTN planning. In: International Conference on Advances in Signal Processing and Artificial Intelligence. pp. 39--44 (2019).
    2. Georgievski, I., Gupta, P., Aiello, M.: Activity learning for intelligent buildings. In: IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference. pp. 0916–0923 (2019). https://doi.org/10.1109/UEMCON47517.2019.8993060.

  5. 2018

    1. Kalksma, M., Setz, B., Pratama, A.R., Georgievski, I., Aiello, M.: Mining sequential patterns for appliance usage prediction. In: International Conference on Smart Cities and Green ICT Systems. pp. 23--33. SCITEPRESS (2018). https://doi.org/10.5220/0006669500230033.

  6. 2017

    1. Georgievski, I., Nguyen, T.A., Nizamic, F., Setz, B., Lazovik, A., Aiello, M.: Planning meets activity recognition: Service coordination for intelligent buildings. Pervasive and Mobile Computing. (2017). https://doi.org/10.1016/j.pmcj.2017.02.008.
    2. Georgievski, I., Nizamic, F., Lazovik, A., Aiello, M.: Cloud Ready Applications Composed via HTN Planning. In: 2017 IEEE 10th Conference on Service-Oriented Computing and Applications (SOCA). IEEE (2017). https://doi.org/10.1109/soca.2017.19.

  7. 2016

    1. Georgievski, I., Aiello, M.: Automated Planning for Ubiquitous Computing. ACM Computing Surveys. 49, 1--46 (2016). https://doi.org/10.1145/3004294.
    2. Aiello, M., Johnsen, E.B., Dustdar, S., Georgievski, I.: Preface. In: European Conference on Service-Oriented and Cloud Computing. pp. V–VI (2016). https://doi.org/10.1007/978-3-319-44482-6.

  8. 2015

    1. Georgievski, I., Aiello, M.: HTN planning: Overview, comparison, and beyond. Artificial Intelligence. 222, 124--156 (2015). https://doi.org/10.1016/j.artint.2015.02.002.
    2. Georgievski, I.: Coordinating services embedded everywhere via hierarchical planning, https://www.rug.nl/research/portal/files/23863757/Complete_thesis.pdf, (2015).

  9. 2013

    1. Georgievski, I., Nguyen, T.A., Aiello, M.: Combining Activity Recognition and AI Planning for Energy-Saving  Offices. In: IEEE International Conference on Ubiquitous Intelligence and Computing and IEEE International Conference on Autonomic and Trusted Computing. pp. 238--245. IEEE Computer Society (2013). https://doi.org/10.1109/UIC-ATC.2013.106.

  10. 2012

    1. Georgievski, I., Degeler, V., Pagani, G.A., Nguyen, T.A., Lazovik, A., Aiello, M.: Optimizing Energy Costs for Offices Connected to the Smart Grid. IEEE Transactions on Smart Grid. 3, 2273--2285 (2012). https://doi.org/10.1109/tsg.2012.2218666.

  11. 2010

    1. Degeler, V., Georgievski, I., Lazovik, A., Aiello, M.: Concept mapping for faster QoS-aware web service composition. In: IEEE International Conference on Service-Oriented Computing and Applications. pp. 1--4. IEEE Computer Society (2010). https://doi.org/10.1109/SOCA.2010.5707193.

Summer 2021

  • Smart Citites and Internet of Things, Exercise
  • Distributed Systems, Exercise (Herman Hollertih Zentrum)

Winter 2020/2021

  • AI Planning for Ubiquitous Computing, Organisation and Execution
  • Distributed Systems, Exercise (Herman Hollertih Zentrum)

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.

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