Agnar Aamodt
Background and activities
Professor in Computer Science and Artificial Intelligence.
Affiliated with Norwegian Open AI Lab.
Research Area
Main research area is Artificial Intelligence, and in particular methods for data analysis and experience-based support of human problem solving and learning. In particular:
- Methods for experience capture and reuse of human experiences,
and in particular case-based reasoning in combination with generalization-based methods - Machine learning and data analysis
- Knowledge modelling and representation
- Cognitive Science
- Decision support systems
Application areas in focus include medicine and health, fish farming, petroleum technology, search and rescue.
Research Group: Data and Artificial Intelligence
Additional information: www.idi.ntnu.no/~agnar
Scientific, academic and artistic work
A selection of recent journal publications, artistic productions, books, including book and report excerpts. See all publications in the database
Journal publications
- (2021) Fault diagnosis under uncertain situations within a Bayesian knowledge-intensive CBR system. Progress in Artificial Intelligence.
- (2020) Downhole failures revealed through ontology engineering. Journal of Petroleum Science and Engineering. vol. 191 (107188).
- (2019) Learning similarity measures from data. Progress in Artificial Intelligence.
- (2019) A spatio-temporal recurrent network for salmon feeding action recognition from underwater videos in aquaculture. Computers and Electronics in Agriculture. vol. 167.
- (2018) Design of a clinician dashboard to facilitate co-decision making in the management of non-specific low back pain. Journal of Intelligent Information Systems.
- (2018) Case based reasoning as a model for cognitive artificial intelligence. Lecture Notes in Computer Science (LNCS). vol. 11156 LNAI.
- (2018) Bayesian-Supported Retrieval in BNCreek: A Knowledge-Intensive Case-Based Reasoning System. Lecture Notes in Computer Science (LNCS). vol. 11156 LNAI.
- (2017) Case-Based Reasoning and the Upswing of AI. Lecture Notes in Computer Science (LNCS). vol. 10339.
- (2017) Diversity of Exercise Plans using Evolutionary Inspired Adaptation. CEUR Workshop Proceedings. vol. 1917.
- (2017) A learning system based on lazy metareasoning. Progress in Artificial Intelligence. vol. 7 (2).
- (2017) Data driven case base construction for prediction of success of marine operations. CEUR Workshop Proceedings. vol. 2028.
- (2017) Diagnosing Root Causes and Generating Graphical Explanations by Integrating Temporal Causal Reasoning and CBR. CEUR Workshop Proceedings. vol. 1815.
- (2017) Evolutionary inspired adaptation of exercise plans for increasing solution variety. Lecture Notes in Computer Science (LNCS). vol. 10339 LNAI.
- (2016) Case Representation and Similarity Assessment in the SELFBACK Decision Support System. Lecture Notes in Computer Science (LNCS). vol. 9969 LNCS.
- (2016) Case representation and similarity assessment in the selfBACK decision support system. CEUR Workshop Proceedings. vol. 1670.
- (2015) Evidence-driven retrieval in textual CBR: Bridging the gap between retrieval and reuse. Lecture Notes in Computer Science (LNCS). vol. 9343.
- (2014) Case-based reasoning for improving traffic flow in urban intersections. Lecture Notes in Computer Science (LNCS). vol. 8765.
- (2014) Integrating human related errors with technical errors to determine causes behind offshore accidents. Safety Science. vol. 63.
- (2014) An overview of case-based reasoning applications in drilling engineering. Artificial Intelligence Review. vol. 41 (3).
- (2013) A real-time decision support system for high cost oil-well drilling operations. The AI Magazine. vol. 34 (1).