Learning new concepts is a difficult task for autonomous robots. These robots can adapt to changes in the situations. To adapt to a situation, they should be able to determine the usefulness of objects around them. The usefulness of objects is highly dependent on situational context, making pre-programming of adaptation behaviour to all possible situations difficult. Automatic learning of this information by the robot from its own observations during deployment is a more feasible approach. We encode the usefulness of objects in logical statements that symbolise regularities in observations of objects in a Markov Logic Network. This network forms the basis from which the usefulness of newly observed objects or situations can be inferred by a robot. Each logical statement in the network has an associated weight that indicates how strong the robot believes the statement is true. These weights can be adjusted over time based on new observations made by the robot, strengthening its beliefs or creating a new belief for an unknown object in a process called cumulative learning. We extend the Markov Logic Network framework with a cumulative learning algorithm called MLN-CLA. This algorithm contains several different knowledge updating strategies that handle conflicts. We demonstrate the ability of MLN-CLA to learn the usefulness of unknown objects over time. In addition, we demonstrate the abilities of MLN-CLA to incorporate new logical statements to better capture information about objects and situational contexts. Together, these two abilities enable robots to autonomously adapt to changing situations during deployment.

In the open world, machine learning (ML) models can encounter a multitude of unknown or novel classes. In a surveillance, safety, or security use case, unknown samples can pose potential threats that are hard to detect since those samples have never been trained on. At the same time, most of the unknowns that will be encountered by a surveillance ML model will be harmless. This results in too many unwanted alerts and manual analyses, of harmless unknowns that have been flagged.

Through this thesis, for the first time (to the best of our knowledge), a method is developed that can automatically assess the relevance of unknown classes, by modelling their image features as clusters (or distributions) and comparing them using statistical distance measures. Our use case lies in computer vision for military applications, where based on the user input, relevance is defined. We define road vehicles as relevant classes and use those for our training set. Our aim is to build a model that can successfully classify new unseen road vehicles as ‘relevant unknowns’, while also successfully classifying harmless unknown birds that are not part of the training set, as ‘irrelevant unknowns’. On the DomainNet data-set, we demonstrate that our novel method can very accurately determine the relevance of unknown classes at test time for both low and high-dimensional data, with AUC scores ranging from 0.99 to a perfect 1.00.