In autonomous systems, self-awareness capabilities are useful to allow artificial agents to detect abnormal situations based on previous experiences. This paper presents a method that facilitates the incremental learning of new models by an agent. Available learned models can dynamically generate probabilistic predictions as well as evaluate their mismatch from current observations. Observed mismatches are grouped through an unsupervised learning strategy into different classes, each of them corresponding to a dynamic model in a given region of the state space. Such clusters define switching Dynamic Bayesian Networks (DBNs) employed for predicting future instances and detect anomalies. Inferences generated by several DBNs that use different sensorial data are compared quantitatively. For testing the proposed approach, it is considered the multi-sensorial data generated by a robot performing various tasks in a controlled environment and a real autonomous vehicle moving at a University Campus.
Incremental learning of abnormalities in autonomous systems
01.01.2019
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Incremental Motion Reshaping of Autonomous Dynamical Systems
TIBKAT | 2020
|Incremental Motion Reshaping of Autonomous Dynamical Systems
Springer Verlag | 2020
|Incremental guidance of functionality for autonomous and semi-autonomous systems and applications
Europäisches Patentamt | 2025
|