THE EVOLUTION OF C 2 : WHERE HAVE WE BEEN ? WHERE ARE WE GOING ? ” PROBABILISTIC ONTOLOGIES AND PRAGMATICS FOR COMPLEX SYSTEMS INTEGRATION

Information systems are increasingly pervasive in our lives, and are becoming increasingly sophisticated as technology evolves. This has resulted in an evergrowing complexity that makes interoperability a major issue. We argue that addressing the interoperability problem requires consideration of at least three distinct aspects of data: syntax, semantics, and pragmatics. The syntactical dimension, which provides the link between the data and its representation, is already well understood. The semantic dimension provides the link between the data and its meaning. Ontology engineering addresses this aspect. Technologies and tools for ontology engineering are under active development. Current-generation semantic technology lacks a principled way of representing uncertainty, a requirement for many domains of interest. The pragmatic dimension provides the link between the data and its use. Pragmatic frames, in close relationship with ontologies, address this dimension both in its static and dynamic aspects. This paper presents our research on pragmatics and probabilistic ontologies, which are integrated in a coherent, mathematically sound computational environment via the System Entity Structure (SES) architecture. The key objective is to present a principled theoretical framework for seamless integration through a scalable highlevel knowledge fusion methodology. I Motivation: The Knowledge Integration Challenge Networked systems are becoming ubiquitous across a wide array of human activities, growing ever more complex with each passing year. Complexity increases not as an end in itself, but as a side effect of success. New capabilities are implemented, new technologies are added, scope is broadened, specialization increases, larger problems are tackled -and complexity grows. Because of complexity creep, success can be a mixed blessing. An intricate but capable system is a blessing in its ability to support complex user operations, but it can become a curse when the need arises to interoperate with other equally complex systems. As an example, disaster relief operations engage players such as governmental agencies, private companies, NGOs, military, law enforcement volunteers, and many others whose numbers and diversity make command and control (C) a great challenge. In this environment, a key issue is to provide information technology support for rapid and accurate decision-making, which involves finding, integrating, and continuously converting vast amounts of heterogeneous data into actionable information. Effective use of these resources can lead to more informed decisions and contribute to the success of the overall operation. However, this can only be achieved by addressing the challenges of: (1) enabling interoperability among diverse systems and data repositories; (2) incorporating a wide variety of traditional and non-traditional types of data coming from geographically dispersed sources; and (3) providing the ability to process massive volumes of noisy, incomplete and uncertain data in a timely manner. Advances in connectivity and computation alone are insufficient to meet these challenges. The sheer volume of data creates informational and cognitive bottlenecks. Incompatible formats and semantic mismatches demand tedious and time-consuming manual processing at various points in the decision cycle. As a result, massive amounts of potentially relevant data remain unexploited, and decision makers’ precious cognitive resources are too often focused on low-level manual data integration rather than high-level reasoning about the situations to be addressed. New approaches are needed to bridge the gap from data interchange to knowledge interchange, to free human decision-makers from information overload and lowlevel manual tasks, and to provide them with actionable, decision-relevant information. Our approach is to achieve true high-level knowledge interchange by leveraging the unexplored synergistic potential of current state-of-the-art approaches to interoperable systems. More specifically, the main thrust of the research is to merge probabilistic ontologies with pragmatic frames so to establish a consistent framework that can be applied to many domains of application. Synthesizing these leading edge technologies is by itself a major challenge with a great potential to advance science. A second, equally ambitious goal is to move towards a general theory of high-level knowledge integration. Research on the subject of information fusion has focused primarily on specific application areas. The bulk of research effort has concentrated on lower-level data alignment (e.g. multisensor data fusion, syntactic protocols, distributed simulation, etc), on semantic mapping solutions (e.g. Semantic Web approaches, specialized semantic mapping solutions, etc), or other topics that do not fully address the fundamentals of high-level knowledge integration. This gap has been recognized and there have been some efforts to address it (e.g. [1]). However, research on this subject is still in its infancy. In other words, although some of the above issues have been addressed in an isolated fashion, more has to be done towards a solution for the problem of automated high-level knowledge fusion. This is a gap that has been identified in applications of complex networked systems, but has not been addressed in its fundamental aspects with the academic rigor necessary to produce a full-fledged theory and scientific discipline. Our aim is not only to bring about important theoretical and practical advances through our research, but in addition, to foster the development of a rigorous scientific discipline of complex systems integration. Following this initial motivation on the problem of Knowledge Integration for complex endeavors, section 2 provides an overview of the current research regarding interoperability. In section 3 we present the main concepts, methods, and technologies behind our approach, emphasizing the relevant issues in merging probabilistic ontologies with pragmatic frames. Then, section 4 illustrates how our approach would be applied to a complex endeavor, by means of a case study inspired by the disaster relief operations following hurricane Katrina. Section 5 provides a brief overview of our general approach for integrating the complex suite of technologies we are dealing with. Section 6 is a short discussion of our research. II. BACKGROUND AND RELATED RESEARCH II.1 Theories of Interoperability There are some proposed theories that focus on assessing the interoperability level of distinct systems. An example is the two-level theory of interoperability by Dahmann et al. [2] or Tolk’s Levels of Conceptual Interoperability Model (LCIM) [3]. These theories build up from key research ideas that shaped the field of data alignment. The first of these ideas came from the work by Zeigler in 1976 [4] on the Discrete Event System Specification (DEVS), which is a mathematical formalism for specifying and composing components into systems. DEVS is used to describe components across a spectrum that ranges from mathematical expressions and mathematical approximations to discrete approximations and discrete interpolation, with the discrete aspects of the DEVS spectrum executable on a digital computer. The descriptive range allows DEVS to cope with the specification of components across several levels. Dahmanm [2] asserted that interoperability occurs in levels, while Tolk [3] and Turnitsa [5] further lend support to the idea of interoperability levels by specifying the LCIM classification. There, each increase in the level of interoperability gives rise to a corresponding increase in the density of information that is exchanged. For example, the shift from the syntactic to the semantic to the pragmatic level of interoperability brings a shift from “Which format? ” to “What is going on? ” to “How can/should I respond? ” Each level presupposes and requires the levels below it. At the syntactic level, the systems are able to interchange data; at the semantic level, they attach the same meaning to the data being interchanged; the pragmatic level, additional context or information is exchanged to enable appropriate action to be taken. Tolk et al. [6] provides an in depth review into each of the LCIM complementary theories of model based data engineering, process engineering and constraint-assumption engineering. Page et al. [7] address the issue from a process-centric perspective and propose a framework for the broader simulation interconnection problem based on the ideas of capability maturity models (CMM) [8, 9]. They argue that the problem should be seen from three distinct dimensions: composability, interoperability, and integratability. Composability between two models is achieved when they share compatible objectives and can be directly related with the LCIM’s conceptual interoperability level. Along the same lines, interoperability is the ability of two models to exchange data or services at run-time, and is directly related to the LCIM’s Dynamic, Pragmatic, Semantic and Syntactic levels. Finally, integratability is the ability of configuring and modifying a set of components to make them interoperable and possibly composable, and is directly related to the LCIM’s technical level. While other models of interoperability such as the Levels of Information Systems Interoperability Model (LISI) [10] or the NATO Reference Model of Interoperability (NMI) [11] exist, these models address only limited specification of higher levels of interoperability. The LCIM, by considering alignment and harmonization of data, provides the required theoretical framework to explore interoperability at a higher level. Although our research is not specifically aimed at defining taxonomies for interoperability, the LCIM levels are incorporated into our framework for high-level information fusion using probabilistic ontologies (POs) [12]. As an example, our use