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

Aligning Interdisciplinary Healthcare Team Behavior with Workflow Execution: An Example of a Radical Prostatectomy Workflow

Operationalizing care delivery through an interdisciplinary healthcare team (IHT) requires knowledge about the overall structure of an IHT and the behavioral rules that "control" the dynamics of this structure interpreted as team and clinical leadership maintenance and task allocation. While progress has been made in understanding IHT structure, there is less work on the behavioral aspects of an IHT associated with its dynamics. In this paper we fill this void by extending our Team and Workflow Management Framework (TWMF) with a set of rules to operationalize IHT behavior in terms of clinical leadership, coordination of workflow execution over multiple days as part of continuity of care, and management of tasks, including urgent ones that prevent planned workflow execution. We briefly describe a proof-of-concept implementation of extended TWMF in the form of a computer system for supporting cooperative execution of clinical workflows by an IHT. The system is built on top of an existing business workflow execution engine and employs behavioral rules to control the IHT behavior. We also illustrate the operations of TWMF in a case study where an IHT is executing a workflow for the management of post-operative inpatient recovery after radical prostatectomy

-ilities Tradespace and Affordability Project – Phase 3

One of the key elements of the SERC’s research strategy is transforming the practice of systems engineering and associated management practices – “SE and Management Transformation (SEMT).” The Grand Challenge goal for SEMT is to transform the DoD community’s current systems engineering and management methods, processes, and tools (MPTs) and practices away from sequential, single stovepipe system, hardware-first, document-driven, point- solution, acquisition-oriented approaches; and toward concurrent, portfolio and enterprise- oriented, hardware-software-human engineered, model-driven, set-based, full life cycle approaches.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046).This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant Secretary of Defense for Research and Engineering (ASD(R&E)) under Contract H98230-08- D-0171 (Task Order 0031, RT 046)

Developing agent-based simulation models of task performance of cognitively diverse teams

Team-oriented work dominates industry, government, and academic areas with the goal of solving increasingly complex problems. However, the scope and external validity of traditional human factors research is inherently limited by the time and resources required to conduct laboratory studies. The model described in this thesis integrates simulation with human factors by providing an operationalized model that incorporates cognitive diversity and domain expertise. Convergence and functionality of the model have been established through a series of analyses, and a clear path for future research has been identified. By integrating simulation methods into human factors subject areas, researchers may be able to gain understanding of a more diverse set of teams, team dynamics, and group performance in a fraction of the time and resources required for traditional methods

Artificial Intelligence in Business: A Literature Review and Research Agenda

The rise of artificial intelligence (AI) technologies has created promising research opportunities for the information systems (IS) discipline. Through applying latent semantic analysis, we examine the correspondence between key themes in the academic and practitioner discourses on AI. Our findings suggest that business academic research has predominantly focused on designing and applying early AI technologies, while practitioner interest has been more diverse. We examine these differences in the socio-technical continuum context and relate existing literature on AI to core IS research areas. In doing so, we identify existing research gaps and propose future research directions for IS scholars related to AI and organizations, AI and markets, AI and groups, AI and individuals, and AI development

Knowledge-driven delivery of home care services

The version of record is available online at: http://dx.doi.org/10.1007/s10844-010-0145-0Home Care (HC) assistance is emerging as an effective and efficient alternative to institutionalized care, especially for the case of senior patients that present multiple co-morbidities and require life long treatments under continuous supervision. The care of such patients requires the definition of specially tailored treatments and their delivery involves the coordination of a team of professionals from different institutions, requiring the management of many kinds of knowledge (medical, organizational, social and procedural). The K4Care project aims to assist the HC of elderly patients by proposing a standard HC model and implementing it in a knowledge-driven e-health platform aimed to support the provision of HC services.Peer ReviewedPostprint (author's final draft

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Customization of an agent-based medical system

In this paper, the automatic customization of an agent-based medical system is approached by means of ontologies. The particular case of Home Care studied and developed in the EU K4Care project, is presented. The customization is achieved by means of generating individual versions of a reference ontology, called Actor Profile Ontology, which defines the behaviour of the actors in the multi-agent system. The paper, analyses the usability and advantages of this customization in order to add flexibility and adaptability to the system. It also shows how the personalized ontology is able to represent the liabilities and permissions of a particular user, providing the base for automatically generating the behaviour of the corresponding personal agent. A tool, called ATAPO, is also presented. It has been designed to assist the user in the personalization process. The way how this tool interacts with the system to permit the online modification of the behaviour of the agents is also discussed.Postprint (published version