Engineering an Ontology for Autonomous Systems - The OASys Ontology

This paper describes the development of an ontology for autonomous systems, as the initial stage of a research programe on autonomous systems’ engineering within a model-based control approach. The ontology aims at providing a unified conceptual framework for the autonomous systems’ stakeholders, from developers to software engineers. The modular ontology contains both generic and domain-specific concepts for autonomous systems description and engineering. The ontology serves as the basis in a methodology to obtain the autonomous system’s conceptual models. The objective is to obtain and to use these models as main input for the autonomous system’s model-based control system

Model-based Engineering of Autonomous Systems using Ontologies and Metamodels

Our research focuses on engineering processes for autonomous intelligent systems construction with a life-cycle holistic view, by means of a model-based framework. The conceptual core of the framework is ontologically-driven. Our ontological approach consists of two elements. The first one is a domain Ontology for Autonomous Systems (OASys) to capture the autonomous system structure, function and behaviour. The second element is an Ontology-driven Engineering Methodology (ODEM) to develop the target autonomous system. This methodology is based on Model-based Systems Engineering and produces models of the system as core assets. These models are used through the whole system life-cycle, from implementation or validation to operation and maintenance. On the application side, the ontological framework has been used to develop a metacontrol engineering technology for autonomous systems, the OM Engineering Process (OMEP), to improve their runtime adaptivity and resilience. OMEP has been applied to a mobile robot in the form of a metacontroller built on top of the robot's control architecture. It exploits a functional model of the robot (TOMASys Model) to reconfigure its control if required by the situation at runtime. The functional model is based on a metamodel about controller function and structure using concepts form the ontology. The metacontroller was developed using the ontology-driven methodology and a robot control reference architecture

Ontology driven description and engineering of autonomous systems: application to process system engineering

Autonomous systems refer to systems capable of operating in a real world environment without any form of external control for extended periods of time. Autonomy is a desired goal for every system as it improves its performance, safety and profit. Ontologies are a way to conceptualize the knowledge of a specific domain. In this paper an ontology for the description of autonomous systems as well as for its development (engineering) is presented and applied to a process. This ontology is intended to be applied and used to generate final applications following a model driven methodology

Crowd simulation-based knowledge mining supporting building evacuation design

Assessing building evacuation performance designs in emergency situations requires complex scenarios which need to be prepared and analysed using crowd simulation tools, requiring significant manual input. With current procedures, every design iteration requires several simulation scenarios, leading to a complicated and time-consuming process. This study aims to investigate the level of integration between digital building models and crowd simulation, within the scope of design automation. A methodology is presented in which existing ontology tools facilitate knowledge representation and mining throughout the process. Several information models are used to integrate, automate and provide feedback to the design decision-making processes. The proposed concept thus reduces the effort required to create valid simulation scenarios by applying represented knowledge, and provides feedback based on results and design objectives. To apply and test the methodology a system was developed, which is introduced here. The context of building performance during evacuation scenarios is considered, but additional design perspectives can be included. The system development section expands on the essential theoretical concepts required and the case study section shows a practical implementation of the system

Knowledge Representation in Engineering 4.0

This dissertation was developed in the context of the BMBF and EU/ECSEL funded projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2. Goal of the projects is to improve communication and collaborative planning, especially in early development stages. Besides SysML, the use of agreed vocabularies and on- tologies for modeling requirements, overall context, variants, and many other items, is targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning capabilities. For this purpose, state-of-the-art ontologies have been investigated and integrated that entail domains like hardware/software, roadmapping, IoT, context, innovation and oth- ers. New ontologies have been designed like a HW / SW allocation ontology and a domain-specific "eFuse ontology" as well as some prototypes. The result is a modular ontology suite and the GENIAL! Basic Ontology that allows us to model automotive and microelectronic functions, components, properties and dependencies based on the ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is included. These knowledge bases are integrated in a novel tool that allows for collabo- rative innovation planning and requirements communication along the automotive value chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial task, requiring a lot of context and background knowledge. Some of this background knowledge has been selected for presentation and was utilized either in designing models or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced content like multi-level theory, foundational ontologies and reasoning. Finally, at the end, we demonstrate the overall framework, and show the ontology with reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip- tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp- fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation und gemeinsame Planung, speziell in den frühen Entwicklungsphasen zu verbessern. Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web- datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern von implizitem Wissen und Regeln unterstützen. Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer, für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen hierfür sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort- geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft. Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason- ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert

Knowledge-centric autonomic systems

Autonomic computing revolutionised the commonplace understanding of proactiveness in the digital world by introducing self-managing systems. Built on top of IBM’s structural and functional recommendations for implementing intelligent control, autonomic systems are meant to pursue high level goals, while adequately responding to changes in the environment, with a minimum amount of human intervention. One of the lead challenges related to implementing this type of behaviour in practical situations stems from the way autonomic systems manage their inner representation of the world. Specifically, all the components involved in the control loop have shared access to the system’s knowledge, which, for a seamless cooperation, needs to be kept consistent at all times.A possible solution lies with another popular technology of the 21st century, the Semantic Web,and the knowledge representation media it fosters, ontologies. These formal yet flexible descriptions of the problem domain are equipped with reasoners, inference tools that, among other functions, check knowledge consistency. The immediate application of reasoners in an autonomic context is to ensure that all components share and operate on a logically correct and coherent “view” of the world. At the same time, ontology change management is a difficult task to complete with semantic technologies alone, especially if little to no human supervision is available. This invites the idea of delegating change management to an autonomic manager, as the intelligent control loop it implements is engineered specifically for that purpose.Despite the inherent compatibility between autonomic computing and semantic technologies,their integration is non-trivial and insufficiently investigated in the literature. This gap represents the main motivation for this thesis. Moreover, existing attempts at provisioning autonomic architectures with semantic engines represent bespoke solutions for specific problems (load balancing in autonomic networking, deconflicting high level policies, informing the process of correlating diverse enterprise data are just a few examples). The main drawback of these efforts is that they only provide limited scope for reuse and cross-domain analysis (design guidelines, useful architectural models that would scale well across different applications and modular components that could be integrated in other systems seem to be poorly represented). This work proposes KAS (Knowledge-centric Autonomic System), a hybrid architecture combining semantic tools such as: • an ontology to capture domain knowledge,• a reasoner to maintain domain knowledge consistent as well as infer new knowledge, • a semantic querying engine,• a tool for semantic annotation analysis with a customised autonomic control loop featuring: • a novel algorithm for extracting knowledge authored by the domain expert, • “software sensors” to monitor user requests and environment changes, • a new algorithm for analysing the monitored changes, matching them against known patterns and producing plans for taking the necessary actions, • “software effectors” to implement the planned changes and modify the ontology accordingly. The purpose of KAS is to act as a blueprint for the implementation of autonomic systems harvesting semantic power to improve self-management. To this end, two KAS instances were built and deployed in two different problem domains, namely self-adaptive document rendering and autonomic decision2support for career management. The former case study is intended as a desktop application, whereas the latter is a large scale, web-based system built to capture and manage knowledge sourced by an entire (relevant) community. The two problems are representative for their own application classes –namely desktop tools required to respond in real time and, respectively, online decision support platforms expected to process large volumes of data undergoing continuous transformation – therefore, they were selected to demonstrate the cross-domain applicability (that state of the art approaches tend to lack) of the proposed architecture. Moreover, analysing KAS behaviour in these two applications enabled the distillation of design guidelines and of lessons learnt from practical implementation experience while building on and adapting state of the art tools and methodologies from both fields.KAS is described and analysed from design through to implementation. The design is evaluated using ATAM (Architecture Trade off Analysis Method) whereas the performance of the two practical realisations is measured both globally as well as deconstructed in an attempt to isolate the impact of each autonomic and semantic component. This last type of evaluation employs state of the art metrics for each of the two domains. The experimental findings show that both instances of the proposed hybrid architecture successfully meet the prescribed high-level goals and that the semantic components have a positive influence on the system’s autonomic behaviour

Supporting Collaborative Communication in a Multi-layer Meta-process Model for Evolutionary Shared Workflows

A key planning activity within a Virtual Enterprise (VE) is to establish agreed inter-organizational processes. This activity, or meta-process, has to allow for gradual evolution of the VE processes and for a multi layer development from informal business agreements to precise workflows. To support this meta-process, a collaborative electronic whiteboard supported by a tuplespace is proposed. The whiteboard supports a mixed graphical and text interface, with support for keeping track of the changes made. The participating organizations upload workflow definitions from their own IT systems into the tuplespace. Workflows are then discussed, modified and evolved before being downloaded again and mapped to the partners’ individual systems

Knowledge representation, storage and retrieval for BIM supported building evacuation design

Safe evacuation design is a complex process, which relies on crowd simulation models when assessing the performance of large or complicated building layouts. Current simulation methods and tools lack automation and are limited to geometry when relying on BIM interoperability. The use of semantic web linked data is seen as a step towards integrating and leveraging current digital resources to facilitate intelligent and automatic design capable of knowledge processing. An intelligent software system has been developed which is capable of integrating multiple information sources and which can facilitate fast automatic construction and analysis of crowd simulation models for design decision support. The system includes several developed OWL ontologies and SWRL rules which represent design knowledge from the fire evacuation field, thus being able to process and store data about a multi-disciplinary design field. The work conducted towards the development of the system involved investigation into crowd analysis tools, evacuation and digital building models. The ontology and knowledge operators are presented and discussed, providing insight into future exploration of such methods with the aim of outlining their benefits and limitations. The system and knowledge engineered have been tested using a case study, proving they are capable of fast processing and correct interpretation of model data