Graph-based software knowledge: Storage and semantic querying of domain models for run-time adaptation

Abstract

Abstract-Software development for robots is a knowledgeintensive exercise. To capture this knowledge explicitly and formally in the form of various domain models, roboticists have recently employed model-driven engineering (MDE) approaches. However, these models are merely seen as a way to support humans during the robot's software design process. We argue that the robots themselves should be first-class consumers of this knowledge to autonomously adapt their software to the various and changing run-time requirements induced, for instance, by the robot's tasks or environment.Motivated by knowledge-enabled approaches, we address this problem by employing a graph-based knowledge representation that allows us not only to persistently store domain models, but also to formulate powerful queries for the sake of run time adaptation. We have evaluated our approach in an integrated, real-world system using the neo4j graph database and we report some lessons learned. Further, we show that the graph database imposes only little overhead on the system's overall performance.

On Managing Knowledge for MAPE-K Loops in Self-Adaptive Robotics Using a Graph-Based Runtime Model

“…To help a robot to unfold this behaviour, the knowledge representation must be able not only to manage information about the robot's actions and environment, but also to relate the semantics of these concepts to its internal components for decision making. For bridging the gap between the knowledge descriptions and the knowledge specifications about the implementation of the software solving tasks, Hochgeschwender et al [8] proposed a graph-based knowledge representation. This graph is the basic tool for storing, composing, and querying domain models.…”