Agnar Aamodt
Background and activities
Professor in Computer Science and Artificial Intelligence.
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
- (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. vol. 11156 LNAI.
- (2018) Bayesian-Supported Retrieval in BNCreek: A Knowledge-Intensive Case-Based Reasoning System. Lecture Notes in Computer Science. vol. 11156 LNAI.
- (2017) Case-Based Reasoning and the Upswing of AI. Lecture Notes in Computer Science. 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. vol. 10339 LNAI.
- (2016) Case Representation and Similarity Assessment in the SELFBACK Decision Support System. Lecture Notes in Computer Science. 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. vol. 9343.
- (2014) Case-based reasoning for improving traffic flow in urban intersections. Lecture Notes in Computer Science. 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).
- (2013) Detection of Symptoms for Revealing Causes Leading to Drilling Failures. SPE Drilling & Completion. vol. 28 (2).
- (2013) Experience Transfer for Process Improvement. Engineering applications of artificial intelligence. vol. 26 (9).