Engineering Background Knowledge for Social Robots

Social robots are embodied agents that continuously perform knowledge-intensive tasks involving several kinds of information coming from different heterogeneous sources. Providing a framework for engineering robots' knowledge raises several problems like identifying sources of information and modeling solutions suitable for robots' activities, integrating knowledge coming from different sources, evolving this knowledge with information learned during robots' activities, grounding perceptions on robots' knowledge, assessing robots' knowledge with respect humans' one and so on. In this thesis we investigated feasibility and benefits of engineering background knowledge of Social Robots with a framework based on Semantic Web technologies and Linked Data. This research has been supported and guided by a case study that provided a proof of concept through a prototype tested in a real socially assistive context.