Authors:
Klaus Häming
and
Gabriele Peters
Affiliation:
FernUniversität in Hagen, Germany
Keyword(s):
Machine learning, Reinforcement learning, Belief revision, Object recognition.
Related
Ontology
Subjects/Areas/Topics:
Autonomous Agents
;
Image Processing
;
Informatics in Control, Automation and Robotics
;
Robotics and Automation
;
Vision, Recognition and Reconstruction
Abstract:
We propose a hybrid learning system which combines two different theories of learning, namely implicit and explicit learning. They are realized by the machine learning methods of reinforcement learning and belief revision, respectively. The resulting system can be regarded as an autonomous agent which is able to learn from past experiences as well as to acquire new knowledge from its environment. We apply this agent in an object recognition task, where it learns how to recognize a 3D object despite the fact that a very similar, alternative object exists. The agent scans the viewing sphere of an object and learns how to access such a view that allows for the discrimination. We present first experiments which indicate the general applicability of the proposed hybrid learning scheme to this object recognition tasks.