3,937 research outputs found
Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms
The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent âdevicesâ, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew âcognitive devicesâ are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications
Towards a robot task ontology standard
Ontologies serve robotics in many ways, particularly in de-
scribing and driving autonomous functions. These functions are
built around robot tasks. In this paper, we introduce the IEEE
Robot Task Representation Study Group, including its work plan,
initial development efforts, and proposed use cases. This effort
aims to develop a standard that provides a comprehensive on-
tology encompassing robot task structures and reasoning across
robotic domains, addressing both the relationships between tasks
and platforms and the relationships between tasks and users. Its
goal is to develop a knowledge representation that addresses task
structure, with decomposition into subclasses, categories, and/or
relations. It includes attributes, both common across tasks and
specific to particular tasks and task types
International conference on software engineering and knowledge engineering: Session chair
The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing.
The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome
Investigating biocomplexity through the agent-based paradigm.
Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex
Ontologies on the semantic web
As an informational technology, the World Wide Web has enjoyed spectacular success. In just ten years it has transformed the way information is produced, stored, and shared in arenas as diverse as shopping, family photo albums, and high-level academic research. The âSemantic Webâ was touted by its developers as equally revolutionary but has not yet achieved anything like the Webâs exponential uptake. This 17 000 word survey article explores why this might be so, from a perspective that bridges both philosophy and IT
Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving
Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and exemplarily evaluate the method by means of a large-scale drone data set of urban traffic scenarios
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 frĂŒhen 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
MissverstĂ€ndnissen und Fehlplanungen zu unterstĂŒtzen. Dieser Ansatz schlĂ€gt eine Web-
datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern
von implizitem Wissen und Regeln unterstĂŒtzen.
Diese Arbeit beschreibt Ontologien fĂŒr die DomĂ€ne des Engineering 4.0, oder spezifischer,
fĂŒr die DomĂ€ne, die fĂŒr das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft
DomÀnen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und
andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software
Allokations-Ontologie und eine domÀnen-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
AbhÀngigkeiten 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. AusgewÀhlte Grundlagen
hierfĂŒr sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das
Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort-
geschrittene Methoden erlĂ€utert, z.B wie man mit Ontologien SchluĂfolgerungen 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
Dynamic Healthcare Connectivity and Collaboration with Multi-Agent Systems
With the growth of international healthcare operations, methods to improve connectivity are sought, along with a reduction in major barriers of electronic connectivity between global trading partners. To address these barriers, a conceptual agent-based framework following a proposed methodology for the analysis and design stages is developed to allow for improved ease of connectivity and interpretability between international trading partners. This framework is comprised of agents and is applied to connectivity between healthcare entities such as payers and providers. While many healthcare entities exchange information electronically, few do so without some form of manual intervention. Information systems may be engaged to further enhance the healthcare industry. Given the increases in costs and international presence, it is vital to make use of electronic systems that improve overall quality and cost of healthcare
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