Intelligent Decision Technologies

The Levels of Conceptual Interoperability Model (LCIM) has been developed to provide both a metric of the degree of conceptual representation that exists between interoperating systems and also as a guide showing what is necessary to accommodate a targeted degree of conceptual representation between systems. The model was originally developed to support the interoperability of simulation systems, but has been shown to be useful for other domain areas. The model is stratified into seven general levels, and these are introduced and defined. Implied within the model is that the information and processes of one system should be described and that description is then made available to another system. This description of information and processes can take many forms, but is generally an ontological representation. The components of an ontological representation are defined in form and also as elements for the various layers of the LCIM.

This article describes how Technologies from the Semantic Web community can directly and indirectly support Planners in heterogeneous environments of Effects Based Approach to Operations (EBAO). For this firstly the conditions under which these endeavors take place are outlined and specific requirements elaborated. Secondly Decision Supporting functionality of Ontologies and their associated applications for gaining Situational Awareness of a certain Environment are presented where the focus here is on Description Logics (DL) and Alignment. Thirdly to fill the gap of instance inferences, a complex inference is used as a vehicle to demonstrate the portability of ontological functionality into logical systems. In this article the concept of the Situation Calculus is used as a representative and vehicle for complex inferences. This is done for the reason that some inferences require a formalized Knowledge Base to work on. And so does the Situation Calculus, where a Golog interpreter is used as implementation. It is not intended to show the validity or applicability of Golog's capability to infer Courses of Action to achieve given Objectives, but to show that although conceptually orthogonal both technologies and methodologies can be integrated synergistically.

Engineering of large and complex simulation systems is becoming more reliant on the reuse of existing simulation models. While existing technical standards facilitate syntactic and technical interoperability among disparate simulation models, there is still lack of formal methods that enable sound reasoning about the conceptual congruity of models that are selected for composition. This paper suggests a graph-theoretic approach to measure the extent of conceptual congruity of models within a new context. The premise of the approach is based on having contextualized models that provide introspective access to their metamodels. A metamodel associated with a reusable model entails a conceptualization of the domain in which it is originally designed to be situated in. The metamodels are used to instantiate a metagraph and graph distance metrics are used to measure the alignment of metamodels in the context of the new application domain. The paper also presents a strategy for packaging and distributing such metamodels with implemented models to facilitate practical application of the proposed method.

Many defense, homeland security, and commercial security objectives require continuous tracking of mobile entities such as aircraft. The systems that perform these functions produce information products called tracks. A track associates observations with the mobile entity and typically includes position, velocity, and other similar attributes. Military systems have sophisticated tracking and track fusion processes, but lack uniformity in syntactic and semantic content, preventing effective sharing of the information. In other domains of interest, such as seagoing surface ships, dangerous cargo and persons of interest, tracking systems are less mature and have marginal performance. It is now essential that we be able to share information across different tracking systems working in related domains.

To combine information from different sources, we need a flexible framework that can tolerate and exploit data products from those systems, even though these systems employ different representations and embody different assumptions. The most basic assumptions concern what the information is intended to mean (semantics) and how it is intended to be used by a recipient (pragmatics). In accordance with best practices in the technology areas of the semantic web and knowledge representation, we seek to reduce the barriers to efficient sharing of information.

Our approach is to identify a rich semantic model of tracks that can support multiple important functions: (1) represent a wide variety of meanings and support a broad array of pragmatic goals; (2) reduce the time and cost required to implement capabilities to reason about a new, specialized type of track; (3) simplify the understanding and importation of external sources of track information; (4) help operators describe what attributes of tracks they value in performing their tasks; (5) significantly improve our ability to combine multiple sources of track information; (6) provide a stable and evolvable base for key standards and best practices that support information sharing; and (7) improve bandwidth utilization, raising the proportion of communicated information that recipients consider significant, by delivering valued information at the right time (VIRT). This paper describes the proposed rich semantic track model and ongoing efforts to share it widely with appropriate communities of interest.

The need to support context-level interoperability is increasingly gaining importance in today's arena of semantic-oriented, decision-support systems. Unlike data-oriented exchange, such semantic interoperability must venture beyond the elementary communication of discrete data values and endeavor to translate between significantly more expressive, context-rich representations. Further, support of this level of interoperability must not require contamination of native perspective embedded within each participant's representation. The solution offered in this paper presents a service-oriented framework supporting an extensible set of translation paradigms to effectively connect expressive, ontology-based environments. Fundamental to this solution is the notion of a remote service request. Employing this metaphor as the basis for participant interaction allows each system within a universe of potentially diverse representations to interoperate as collections of invocatible services. Further, by transparently marshalling such requests between client and service representations, such translation engine offers each member of this multi-lingual reality the means to interoperate within the familiar confines of their native representations. Finally, the discussion concludes with an evaluation of this capability in terms of the Levels of Conceptual Interoperability Model (LCIM) for assessing degrees of interoperability.