**3.3 Application Ontology**

8 Underwater Vehicles

Fig. 5. Levels of generality of the library of knowledge bases for *SAV*. They include the

Foundational Ontologies (FOs) represents the very basic principles and includes Upper and Utility Ontologies. Upper ontologies describe generic concepts (e.g., the Suggested Upper Merged Ontology or SUMO (Niles & Pease, 2001)) while Utility ontologies describe support concepts or properties (e.g. OGC\_GML for describing geospatial information (Portele, 2007)). FOs meet the requirement that a model should have as much generality as possible, to ensure

The Core Ontology provides a global and extensible model into which data originating from distinct sources can be mapped and integrated. This layer provides a single knowledge base for cross-domain agents and services (e.g., vehicle resource / capabilities discovery, vehicle physical breakdown, and vehicle status). A single model avoids the inevitable combinatorial explosion and application complexities that results from pair-wise mappings

In the bottom layer, an Application Ontology provides an underlying formal model for agents that integrate source data and perform a variety of extended functions. As such, higher levels of complexity are tolerable and the design is motivated more by completeness and logical correctness than human comprehension. Target areas of these Application Ontologies are found in the status monitoring of the vehicle and its environment and the planning of the

Figure 6 represents the relationship between the Foundation Ontologies (Upper and Utility), the Core Ontology and the Application Ontology for each service-oriented agent. Raw data gets parsed from sensors into assertions during the mission using a series of adapter modules for each of the sensing capabilities. It also shows that the knowledge handling by the agent during its decision making process is helped by the reasoner and the rule engine process.

Fig. 6. *SAV* representation in the Knowledge Base using Core and Application ontologies supported by Upper and Utility ontologies. Generation of instances from raw data is

performed by the Adapter. Handling of knowledge is done by the Reasoner, Rule Engine and

To lay the foundation for the knowledge representation of unmanned vehicles, consideration was placed on the Joint Architecture for Unmanned Systems (JAUS) (SAE, 2008a). This

Foundation Ontology, the Core Ontology, and the Application Ontology levels.

reusability across different domains.

mission.

the Service-Oriented Agent.

**3.2 Foundation and core ontology**

between individual metadata formats and ontologies.

Each service-oriented agent has its own Application Ontology. It represents the agent's awareness of the situation by including concepts that are specific to the expertise of the agent. In the case study presented in this chapter, these agents are the status monitor and the mission planner. Together, they provide the *status monitor* and *mission adapter* components described in Fig. 3 required for closing the OODA-loop and provide on-board decision making adaptation.
