Knowledge Acquisition and Design Using Semantics and Perception: A Case Study for Autonomous Robots

The pervasive use of artificial intelligence and neural networks in several different research fields has noticeably improved multiple aspects of human life. The application of these techniques to machines has made them progressively more “intelligent” and able to solve tasks considered extremely complex for a human being. This technological evolution has deeply influenced the way we interact with machines. Purely symbolic artificial intelligence and techniques like ontologies, have also been successfully used in the past applied to robotics, but have also shown some limitations and failings in the knowledge construction task. In fact, the exhibited “intelligence” is rarely the result of a real autonomous decision, but it is rather hard-encoded in the machine. While a number of approaches have already been proposed in literature concerning knowledge acquisition from the surrounding environment, they are either exclusively based on low-level features or they involve solely high-level semantics-based attributes. Moreover, they often don’t use a general high-level knowledge base for grounding the acquired knowledge. In this contexts, the use of semantics technologies, such as ontologies, is mostly employed for action-oriented tasks. In this article we propose an extension of a novel approach for knowledge acquisition based on a general semantic knowledge-base and the fusion of semantics and visual information by means of neural networks and ontologies. The proposed approach has been implemented on a humanoid robotic platform and the experimental results are shown and discussed.