Knowledge-Driven Agile Sensor-Mission Assignment

In this paper, we show how knowledge representation and reasoning techniques can support sensor-mission assignment, proceeding from a high-level specification of information requirements, to the allocation of assets such as sensors and platforms. In our previous work, we showed how assets can be matched to mission tasks by formalising the military missions and means framework in terms of an ontology, and using this ontology to drive a matchmaking process derived from the area of semantic Web services. The work reported here extends the earlier approach in two important ways: (1) by providing a richer and more realistic way for a user to specify their information requirements, and (2) by using the results of the semantic matchmaking process to define the search space for efficient asset allocation algorithms. We accomplish (1) by means of a rule-based representation of the NIIRS approach to relating sensed data to the tasks that data may support. We illustrate (2)by showing how the output of our matching process can drive a well-known efficient combinatorial auction algorithm (CASS). Finally, we summarise the status of our illustration-of-concept application, SAM (Sensor Assignment to Missions), and discuss various roles such an application can play in supporting sensormission assignment