Belief revision with reinforcement learning for interactive object recognition

From a conceptual point of view, belief revision and learning are quite similar. Both methods change the belief state of an intelligent agent by processing incoming information. However, for learning, the focus in on the exploitation of data to extract and assimilate useful knowledge, whereas belief revision is more concerned with the adaption of prior beliefs to new information for the purpose of reasoning. In this paper, we propose a hybrid learning method called SPHINX that combines low-level, non-cognitive reinforcement learning with high-level epistemic belief revision, similar to human learning. The former represents knowledge in a sub-symbolic, numerical way, while the latter is based on symbolic, non-monotonic logics and allows reasoning. Beyond the theoretical appeal of linking methods of very different disciplines of artificial intelligence, we will illustrate the usefulness of our approach by employing SPHINX in the area of computer vision for object recognition tasks. The S...

• Type: Conference
• Year: 2008
• Where: ECAI
• Authors: Thomas Leopold, Gabriele Kern-Isberner, Gabriele Peters
• Keywords: Artificial Intelligence, Belief Revision, ECAI 2008, Epistemic Belief Revision, Method Called Sphinx