An automated tool support for managing implicit requirements using Analogy-based Reasoning

Systems requirements are crucial to the proper functioning of a software and must be met for a project to be successful. Hence the need for its effective management. Implicit Requirements (IMRs) however are difficult to manage as a result of their nature-vague, unclear, and ambiguous amongst other characteristics. The process of requirement management is a continuous cycle as change in requirements and emergence of new requirements occur in a system. Hence the need for a tool/approach which identifies and manages requirements (implicit and explicit) effectively. However, most systems do not manage implicit requirements as a lot of attention is focused on explicit requirements. This research presents an approach for identification and management of IMRs using Analogy-based Reasoning in combination with two other core technologies (Ontology and Natural Language Processing). The approach is supported by a prototype tool, which was assessed by conducting a preliminary evaluation. The results indicate that the approach enables for early identification of IMRs when used with a good domain ontology and is potentially suitable for application in practice by expert

Framework for context analysis and planning of an assistive robot

This paper presents the developments with the SAM robot, established in the ARMEN project. We are interested in cognitive robotics. We have developed two complementary modules. The first one deals with the representation of knowledge, while the second develops the scenario generation. Indeed, the representation of knowledge tells us about the scene, the current state of the robot and the strategy to be adopted by the robot to achieve goals specified by an assisted person. The information extracted from the knowledge representation is the starting point to generate the action plan and the implementation of the scenario by the robot

The Benefits of Explicit Ontological Knowledge-Bases for Robotic Systems

Abstract With the increasing abilities of robots comes a corresponding in-crease in the complexity of creating the software enabling these abilities. We present a case study of a sophisticated robotic system which uses an ontology as the central data store for all information processing. We show how this central, structured and easily human-understandable knowledge-base makes for a system that is easier to develop, understand, and modify.

OCRA – An ontology for collaborative robotics and adaptation

Industrial collaborative robots will be used in unstructured scenarios and a large variety of tasks in the near future. These robots shall collaborate with humans, who will add uncertainty and safety constraints to the execution of industrial robotic tasks. Hence, trustworthy collaborative robots must be able to reason about their collaboration’s requirements (e.g., safety), as well as the adaptation of their plans due to unexpected situations. A common approach to reasoning is to represent the knowledge of interest using logic-based formalisms, such as ontologies. However, there is not an established ontology defining notions such as collaboration or adaptation yet. In this article, we propose an Ontology for Collaborative Robotics and Adaptation (OCRA), which is built around two main notions: collaboration, and plan adaptation. OCRA ensures a reliable human-robot collaboration, since robots can formalize, and reason about their plan adaptations and collaborations in unstructured collaborative robotic scenarios. Furthermore, our ontology enhances the reusability of the domain’s terminology, allowing robots to represent their knowledge about different collaborative and adaptive situations. We validate our formal model, first, by demonstrating that a robot may answer a set of competency questions using OCRA. Second, by studying the formalization’s performance in limit cases that include instances with incongruent and incomplete axioms. For both validations, the example use case consists in a human and a robot collaborating on the filling of a tray.Peer ReviewedPostprint (published version