Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

Locating distributed leadership

This special issue addresses a number of the key themes that have been surfacing from the literature on distributed leadership (DL) for some time. Together with those papers selected to be included in this special issue, the authors set out both to explore and contribute to a number of the current academic debates in relation to DL, while at the same time examining the extent to which research on DL has permeated the management field. The paper examines a number of key concepts, ideas and themes in relation to DL and, in so doing, highlights the insights offered through new contributions and interpretations. The paper offers a means by which forms of DL might be conceptualized to be better incorporated into researchers' scholarship and research, and a framework is presented which considers a number of different dimensions of DL, how it may be planned, and how it may emerge, together with how it may or may not align with other organizational activities and aspects. © 2011 The Authors. International Journal of Management Reviews © 2011 British Academy of Management and Blackwell Publishing Ltd

Linking Moving Object Databases with Ontologies

This work investigates the supporting role of ontologies for supplementing the information contained in moving object databases. Details of the spatial representation as well as the sensed location of moving objects are frequently stored within a database schema. However, this knowledge lacks the semantic detail necessary for reasoning about characteristics that are specific to each object. Ontologies contribute semantic descriptions for moving objects and provide the foundation for discovering similarities between object types. These similarities can be drawn upon to extract additional details about the objects around us. The primary focus of the research is a framework for linking ontologies with databases. A major benefit gained from this kind of linking is the augmentation of database knowledge and multi-granular perspectives that are provided by ontologies through the process of generalization. Methods are presented for linking based on a military transportation scenario where data on vehicle position is collected from a sensor network and stored in a geosensor database. An ontology linking tool, implemented as a stand alone application, is introduced. This application associates individual values from the geosensor database with classes from a military transportation device ontology and returns linked value-class pairs to the user as a set of equivalence relations (i.e., matches). This research also formalizes a set of motion relations between two moving objects on a road network. It is demonstrated that the positional data collected from a geosensor network and stored in a spatio-temporal database, can provide a foundation for computing relations between moving objects. Configurations of moving objects, based on their spatial position, are described by motion relations that include isBehind and inFrontOf. These relations supply a user context about binary vehicle positions relative to a reference object. For example, the driver of a military supply truck may be interested in knowing what types of vehicles are in front of the truck. The types of objects that participate in these motion relations correspond to particular classes within the military transportation device ontology. This research reveals that linking a geosensor database to the military transportation device ontology will facilitate more abstract or higher-level perspectives of these moving objects, supporting inferences about moving objects over multiple levels of granularity. The details supplied by the generalization of geosensor data via linking, helps to interpret semantics and respond to user questions by extending the preliminary knowledge about the moving objects within these relations

Minds Online: The Interface between Web Science, Cognitive Science, and the Philosophy of Mind

Alongside existing research into the social, political and economic impacts of the Web, there is a need to study the Web from a cognitive and epistemic perspective. This is particularly so as new and emerging technologies alter the nature of our interactive engagements with the Web, transforming the extent to which our thoughts and actions are shaped by the online environment. Situated and ecological approaches to cognition are relevant to understanding the cognitive significance of the Web because of the emphasis they place on forces and factors that reside at the level of agent–world interactions. In particular, by adopting a situated or ecological approach to cognition, we are able to assess the significance of the Web from the perspective of research into embodied, extended, embedded, social and collective cognition. The results of this analysis help to reshape the interdisciplinary configuration of Web Science, expanding its theoretical and empirical remit to include the disciplines of both cognitive science and the philosophy of mind

Internet of Robotic Things Intelligent Connectivity and Platforms

The Internet of Things (IoT) and Industrial IoT (IIoT) have developed rapidly in the past few years, as both the Internet and “things” have evolved significantly. “Things” now range from simple Radio Frequency Identification (RFID) devices to smart wireless sensors, intelligent wireless sensors and actuators, robotic things, and autonomous vehicles operating in consumer, business, and industrial environments. The emergence of “intelligent things” (static or mobile) in collaborative autonomous fleets requires new architectures, connectivity paradigms, trustworthiness frameworks, and platforms for the integration of applications across different business and industrial domains. These new applications accelerate the development of autonomous system design paradigms and the proliferation of the Internet of Robotic Things (IoRT). In IoRT, collaborative robotic things can communicate with other things, learn autonomously, interact safely with the environment, humans and other things, and gain qualities like self-maintenance, self-awareness, self-healing, and fail-operational behavior. IoRT applications can make use of the individual, collaborative, and collective intelligence of robotic things, as well as information from the infrastructure and operating context to plan, implement and accomplish tasks under different environmental conditions and uncertainties. The continuous, real-time interaction with the environment makes perception, location, communication, cognition, computation, connectivity, propulsion, and integration of federated IoRT and digital platforms important components of new-generation IoRT applications. This paper reviews the taxonomy of the IoRT, emphasizing the IoRT intelligent connectivity, architectures, interoperability, and trustworthiness framework, and surveys the technologies that enable the application of the IoRT across different domains to perform missions more efficiently, productively, and completely. The aim is to provide a novel perspective on the IoRT that involves communication among robotic things and humans and highlights the convergence of several technologies and interactions between different taxonomies used in the literature.publishedVersio

From engagement to alignment : exploring enterprise architecture through the lens of design science

Information Systems Design Science (ISDS) as a research community is limited by a small number of research frameworks with considerable influence. The small triad of influential ISDS research, consisting of Walls, et al (1992), March and Smith (1995), and Hevner et al (2004) have primarily limited ISDS research to the positivist paradigm and the IT artifact. In contrast, Herbert Simon’s intentions for design science never had such restrictions and intended a broader perspective. This dissertation explores Simon’s intentions for design science, the Simonian stream of thought that includes The Sciences of the Artificial, as well as much of his most notable research, and offers an ‘informed view’ of design science in the tradition of Rortyian neopragmatism. Using this new lens of design science, a Bhaskarian critical realist treatment of human artifacts is also developed. Collectively, a Rortyian neopragmatist treatment for design science, and a Bhaskarian critical realist treatment of human artifacts are used as a lens to augment the Walls et al (1992) framework for Information Systems Design Theories (ISDT). An example of how to apply this lens is accomplished in Paper 2 of the dissertation. The ISDS lens is applied to the topic of Enterprise Architecture (EA). EA as vehicle for IS Alignment is well defined in terms of frameworks, artifacts, and methodology. However little is understood with respect to the discipline and practice of EA. Seeking to advance our understanding of effective vehicles for IS alignment, this research examines EA as an alignment practice and how it attempts to realize alignment. Specifically, we address the following question: How does EA manifest itself in organizations? This research employs an interpretivist epistemology in a manner quite distinct from ISDS research and thus provides contributions to academia in terms of methodology and insight on EA, and for practitioners who wish to mature an EA practice in their organization. Some of the main concepts discovered in the empirical study in Paper 2 are used to develop a practitioner-oriented framework for EA practice in Paper 3

On the cognitive microfoundations of effectual design: the Situated Function–Behavior–Structure framework

PurposeThe purpose of this article is to extend effectuation theory at the front end by building cognitive foundations for the effectual design process.Design/methodology/approachWe adopt an integrative conceptual approach drawing on design cognition theory to explain entrepreneurial cognition.FindingsWe find a significant gap in the entrepreneurial cognition literature with respect to effectuation processes. We thus integrate the Situated Function–Behavior–Structure framework from design theory to elaborate on the cognitive processes of effectuation, specifically with regard to the opportunity development process. This framework describes the cognitive subprocesses by which entrepreneurs means and ends are cyclically (re)formulated over time until a viable “opportunity” emerges, and the venture is formalized, or else, the entrepreneur abandons the venture and exits.”Practical implicationsUnravelling this entrepreneurial design process may facilitate more appropriate and effective design work by entrepreneurs, leading to more successful product designs. It also should facilitate the development of better design techniques and instruction.Originality/valueThis research contributes to new cognitive foundations for effectuation theory and entrepreneurial process research. It better explains how means are transformed into valuable goods over time through an iterative reconsideration of means-ends frameworks. This theoretical elaboration will expectedly facilitate additional research into the iterative cognitive processes of design and enable more formulaic design thinking