Engineering Background Knowledge for Social Robots

Social robots are embodied agents that perform knowledge-intensive tasks involving several kinds of information from different heterogeneous sources. This book, Engineering Background Knowledge for Social Robots, introduces a component-based architecture for supporting the knowledge-intensive tasks performed by social robots. The design was based on the requirements of a real socially-assistive robotic application, and all the components contribute to and benefit from the knowledge base which is its cornerstone. The knowledge base is structured by a set of interconnected and modularized ontologies which model the information, and is initially populated with linguistic, ontological and factual knowledge retrieved from Linked Open Data. Access to the knowledge base is guaranteed by Lizard, a tool providing software components, with an API for accessing facts stored in the knowledge base in a programmatic and object-oriented way. The author introduces two methods for engineering the knowledge needed by robots, a novel method for automatically integrating knowledge from heterogeneous sources with a frame-driven approach, and a novel empirical method for assessing foundational distinctions over Linked Open Data entities from a common-sense perspective. These effectively enable the evolution of the robot s knowledge by automatically integrating information derived from heterogeneous sources and the generation of common-sense knowledge using Linked Open Data as an empirical basis. The feasibility and benefits of the architecture have been assessed through a prototype deployed in a real socially-assistive scenario, and the book presents two applications and the results of a qualitative and quantitative evaluation. Title Page Abstract Contents Introduction Goals of the Thesis Contributions of the Thesis An Ontology Network for Social Robots in Assistive Context Research Methodology Thesis Outline Background Social Robots The Semantic Web Ontologies Pattern-based Ontology Design Ontology Matching Linguistic Linked Open Data Resources Common Sense Knowledge An Ontology Network for Social Robots in Assistive Context Design Methodology Knowledge Areas Ontology Modules Discussion Providing Linked Open Data as Background Knowledge for Social Robots Framester: a Linguistic Data Hub Assessing Foundational Distinctions in Linked Open Data Discussion Accessing Background Knowledge using Lizard Requirements Architecture Ontology Bundle Discussion A Frame-based Approach for Integrating Ontologies Types of Semantic Heterogeneity Proposed Approach Discussion A Knowledge Base Centered Software Architecture for Social Robots Requirements of Software Architectures for Social Robots Robot Software Architecture Overview Components Architecture Prototype for a Real Social Assistive Scenario Discussion Conclusion and Future Work Research Questions Revisited Future Work Code Generated by Lizard Interface Jena Class Bean Class REST API Description Bibliography