Specification and Verification of Commitment-Regulated Data-Aware Multiagent Systems

In this paper we investigate multi agent systems whose agent interaction is based on social commitments that evolve over time, in presence of (possibly incomplete) data. In particular, we are interested in modeling and verifying how data maintained by the agents impact on the dynamics of such systems, and on the evolution of their commitments. This requires to lift the commitment-related conditions studied in the literature, which are typically based on propositional logics, to a first-order setting. To this purpose, we propose a rich framework for modeling data-aware commitment-based multiagent systems. In this framework, we study verification of rich temporal properties, establishing its decidability under the condition of “state-boundedness”, i.e., data items come from an infinite domain but, at every time point, each agent can store only a bounded number of them

Ontology for Representing Human Needs

Need satisfaction plays a fundamental role in human well-being. Hence understanding citizens' needs is crucial for developing a successful social and economic policy. This notwithstanding, the concept of need has not yet found its place in information systems and online tools. Furthermore, assessing needs itself remains a labor-intensive, mostly offline activity, where only a limited support by computational tools is available. In this paper, we make the first step towards employing need management in the design of information systems supporting participation and participatory innovation by proposing OpeNeeD, a family of ontologies for representing human needs data. As a proof of concept, OpeNeeD has been used to represent, enrich and query the results of a needs assessment study in a local citizen community in one of the Vienna districts. The proposed ontology will facilitate such studies and enable the representation of citizens' needs as Linked Data, fostering its co-creation and incentivizing the use of Open Data and services based on it

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 “devices”, 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 “cognitive devices” 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

A conceptual data model promoting data-driven circular manufacturing

Circular economy (CE) paradigm fosters manufacturing companies’ sustainability taking place through different circular manufacturing (CM) strategies. These strategies allow companies to be internally committed to embrace circular values and to be externally aligned with several stakeholders not necessarily belonging to the same supply chain. Nevertheless, these CM strategies adoption is limited by heterogeneous barriers, among which the management and sharing of data and information remain the most relevant ones, bounding the decision-making process of manufacturers in CM. Moreover, the extant literature unveiled the need to structure data and information in a reference model to make them usable by manufacturers. Therefore, the goal of the present work is to propose a reference model by developing a conceptual data model to standardise and structure the necessary data in CM to support manufacturers’ decision-making process. Through this model, data and information to be gathered by manufacturers are elucidated, providing an overview of which ones should be managed internally, and shared externally, clarifying the presence of their mutual interdependencies. The model was conceptualised and developed relying on the extant literature and improved and validated through academic and industrial experts’ interviews

Semantic Model Alignment for Business Process Integration

Business process models describe an enterprise’s way of conducting business and in this form the basis for shaping the organization and engineering the appropriate supporting or even enabling IT. Thereby, a major task in working with models is their analysis and comparison for the purpose of aligning them. As models can differ semantically not only concerning the modeling languages used, but even more so in the way in which the natural language for labeling the model elements has been applied, the correct identification of the intended meaning of a legacy model is a non-trivial task that thus far has only been solved by humans. In particular at the time of reorganizations, the set-up of B2B-collaborations or mergers and acquisitions the semantic analysis of models of different origin that need to be consolidated is a manual effort that is not only tedious and error-prone but also time consuming and costly and often even repetitive. For facilitating automation of this task by means of IT, in this thesis the new method of Semantic Model Alignment is presented. Its application enables to extract and formalize the semantics of models for relating them based on the modeling language used and determining similarities based on the natural language used in model element labels. The resulting alignment supports model-based semantic business process integration. The research conducted is based on a design-science oriented approach and the method developed has been created together with all its enabling artifacts. These results have been published as the research progressed and are presented here in this thesis based on a selection of peer reviewed publications comprehensively describing the various aspects