Artificial Intelligence

The research within the Artificial Intelligence group conduct research focused on all aspects of algorithms and methods in respect to Artificial Intelligence. This includes probabilistic and biologically inspired methods, big data, data mining, machine learning, search, information integration, and semantic web as well as applications in bioinformatics, engineering informatics, social informatics, and related areas.

The AI the research group is focusing on reasoning, knowledge, planning, learning, natural language processing, image analysis, prediction, perception and the ability to move and manipulate objects in addition to statistical methods, computational intelligence, and traditional symbolic AI.


  • Bernt Arild Bremdal, Professor
  • Asbjørn Danielsen, Associate Professor
  • Hans Olofsen, Associate Professor
  • Arild Steen, Assistant Professor
  • Børre Bang, Professor
  • Rune Dalmo, Associate Professor
  • Arne Lakså, Professor


  • Tatiana Kravetc
  • Kristoffer Tangrand
  • Andreas Dyrøy Jansson
  • Shayan Dadman

Recent publications:

  • B. Bremdal, K. Tangrand, A. Danielsen, E. Gramme: The E-Regio project: for a distributed local energy market. The Innovation Platform ISSUE 2,, p. 85-86

    Tangrand, Kristoffer and Bremdal, Bernt. (2020). Using Deep Learning Methods to Monitor Non-Observable States in a Building. Proceedings of the Northern Lights Deep Learning Workshop. 1. 6. 10.7557/18.5159.

    Tangrand, Kristoffer; Bremdal, Bernt Arild. The FlexNett Simulator. IOP Conference Series: Earth and Environmental Science (EES) 2019; Volum 352:012005. ISSN 1755-1307.p 1 - 9.

    Bremdal, Bernt Arild; Ilieva, Iliana. Micro Markets in Microgrids. John Wiley & Sons 2019 ISBN 9781119434542.s 97 - 164.

    Ilieva, Iliana; Bremdal, Bernt Arild; de la Nieta Lopez, A.A.S.; Simonsen, S.H.. Local energy markets as a solution for increased energy efficiency and flexibility. IOP Conference Series: Earth and Environmental Science (EES) 2019; Volum 352 (012036). ISSN 1755-1307.s

    Olivella-Rosell, Pol; Lloret-Gallego, Pau; Munne-Collado, Ingrid; Villafafila-Robles, Roberto; Sumper, Andreas; Ottesen, Stig Ødegaard; Rajasekharan, Jayaprakash; Bremdal, Bernt Arild. Local Flexibility Market Design for Aggregators Providing Multiple Flexibility Services at Distribution Network Level. Energies 2018; Volum 11 (4). ISSN 1996-1073.

    Jansson, Andreas Dyrøy; Bremdal, Bernt Arild. Genetic Algorithm for Adaptable Design using Crowdsourced Learning as Fitness Measure. IEEE conference proceedings 2018 ISBN 978-1-5386-7189-4.

    Asbjørn Danielsen: Increasing fall risk awareness using wearables: A fall risk awareness protocol. Journal of Biomedical Informatics, 2016 DOI: 10.1016/j.jbi.2016.08.016

    Danielsen, Asbjørn and Tørresen, Jim. Recognizing Bedside Events Using Thermal and Ultrasonic Readings. Sensors (Basel). 2017;17(6):1342. Published 2017 Jun 9. doi:10.3390/s17061342

    Danielsen, Asbjørn and Bremdal, Bernt. (2017). Predicting Bedside Falls using Current Context. 10.1109/SSCI.2017.8280988.

    Asbjørn Danielsen: Non-intrusive Bedside Event Recognition Using Infrared Array and Ultrasonic Sensor, Published in: Ubiquitous Computing and Ambient Intelligence, 2016


The Smart Charge Project has started

October 2019: The Interreg funded project has started. Smart Charge is a cooperation between the Computer Science and Computational Engineering Department of UiT and The Lapland University of Applied Sciences in Rovaniemi in Finland


Work at UiT Narvik hits international front pages

Technology developed at UiT Narvik reaches the front page of Innovation News Network in June.The Innovation News Network promotes work that was done in the E-Regio project together with Smart Innovation Norway in Halden and Skagerak, the energy company, in Porsgrunn, Norway. UiT was engaged due to its earlier work on multi-agent systems and simulations on energy markets.  This technology was further developed in the Interreg funded project, Smart Charge.  E-Regio created a chance for the AI group to test the technology beyond a simulated world and has been used to manage a local energy market at Skagerak soccer stadium. Here a giant facility for local energy production based on solar panels and 1000 kWh of storage has been built.  An agent represents a device such as a battery, PV panel or the grid.  Based on predictions made on future supply and consumption as well as storage level the agents engage in internal trade that seeks to improve local self-consumption and cut costs and improve profits for the players involved in this market.  Several R&D initiatives have explored similar concepts before, but with the Skagerak case UiT Narvik shows that it is possible to employ these type of agents in real-world settings too.


Master student has made a system that composes jazz

As a part of the creative AI research program at UiT Narvik Shayan Dadman has created a system that composes jazz music.


Kristoffer Tangrand publiserer ny artikkel rundt anvendelsen av LSTM og Stordata

Januar 2020: Kilmasensorer i bygg gir opphav til estimering av byggets bruk uten at personvernet utfordres.