Matching sensors to missions using a knowledge-based approach

Making decisions on how best to utilise limited intelligence, surveillance and reconnaisance (ISR) resources is a key issue in mission planning. This requires judgements about which kinds of available sensors are more or less appropriate for specific ISR tasks in a mission. A methodological approach to addressing this kind of decision problem in the military context is the Missions and Means Framework (MMF), which provides a structured way to analyse a mission in terms of tasks, and assess the effectiveness of various means for accomplishing those tasks. Moreover, the problem can be defined as knowledge-based matchmaking: matching the ISR requirements of tasks to the ISR-providing capabilities of available sensors. In this paper we show how the MMF can be represented formally as an ontology (that is, a specification of a conceptualisation); we also represent knowledge about ISR requirements and sensors, and then use automated reasoning to solve the matchmaking problem. We adopt the Semantic Web approach and the Web Ontology Language (OWL), allowing us to import elements of existing sensor knowledge bases. Our core ontologies use the description logic subset of OWL, providing efficient reasoning. We describe a prototype tool as a proof-of-concept for our approach. We discuss the various kinds of possible sensor-mission matches, both exact and inexact, and how the tool helps mission planners consider alternative choices of sensors.