inSiTUEVO

Partners: Northeastern University, University of Minnesota, University of Piraeus

Funding: FWF

Employed Technologies: PostgreSQL, PostGIS, Visual Paradigm, Hibernate, JBoss Drools, Java, R, Python

Web: https://sites.google.com/view/insituevo

Team: DI Dr. Andrea Salfinger


Inductive Situation Evolution Modeling

inSiTUEVO is a research project in the area of Cognitive Situation Management. Broadly speaking, Cognitive Situation Management involves the problem of how machines can not only perceive the individual elements of their observed environment, but understand the whole "big picture", i.e., encountered situations, by interrelating these individual perceptions. To do so, artifical agents require suitable situation models representing the event patterns of interest, against which they can compare their sensed observations.

inSiTUEVO aims at supporting the acquisition of these situation models, by developing novel Knowledge Discovery & Data Mining approaches to automatically induce symbolic situation evolution models from data.


Publications:

    2020

  1. Andrea Salfinger. Reinforcement Learning Meets Cognitive Situation Management: A Review of Recent Learning Approaches from the Cognitive Situation Management Perspective. 2020 IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA). Canada. May, 2020. PDF BibTeX
  2. Salfinger, Andrea. Deep Learning for Cognitive Load Monitoring: A Comparative Evaluation. Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers. New York, NY, USA,UbiComp-ISWC '20, pages: 462–467. Association for Computing Machinery, 2020. PDF BibTeX
  3. Andrea Salfinger, Lauro Snidaro. Towards Neural Situation Evolution Modeling: Learning a Distributed Representation for Predicting Complex Event Sequences. 2020 IEEE 23rd International Conference on Information Fusion (FUSION). pages: 1-8. 2020. PDF BibTeX

    4. 2019

  4. Salfinger, Andrea. Framing Situation Prediction as a Sequence Prediction Problem: A Situation Evolution Model Based on Continuous-Time Markov Chains. 22nd International Conference on Information Fusion (FUSION). July, 2019. PDF BibTeX
  5. Andrea Salfinger. Situation Mining: Event Pattern Mining for Situation Model Induction. IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019, Las Vegas, NV, USA, April 8-11, 2019. pages: 17--25. April, 2019. PDF BibTeX