**1.1 Contribution**

In this chapter, we first provide a review to the different approaches solving the decision making process for UUV missions. Then, we propose a semantic framework that provides a solution for hierarchical distributed representation of knowledge for multidisciplinary agent interaction. This framework uses a pool of hierarchical ontologies for representation of the knowledge extracted from the expert and the processed sensor data. It provides a common machine understanding between embedded agents that is generic and extendable. It also includes a reasoning interface for inferring new knowledge from the observed data and guarantee knowledge stability by checking for inconsistencies. This framework improves local (machine level) and global (system level) situation awareness at all levels of service capabilities, from adaptive mission planning and autonomous target recognition to deliberative collision avoidance and escape. It acts as as an enabler for on-board decision making.

Based on their capabilities, service-oriented agents can then gain access to the different levels of information and contribute to the enrichment of the knowledge. If the required information is unavailable, the framework provides the facility to request other agents with the necessary capabilities to generate the required information, i.e. an target classification algorithm could query the correspondent agent to provide the required object detection analysis before proceeding with its classification task.

Secondly, we present an algorithm for autonomous mission adaptation. Using the knowledge made available by the semantic framework, our approach releases the operator from decision making tasks. We show how adaptation plays an important role in providing long term autonomy as it allows the platforms to react to events from the environment while at the same time requires less communication with the operator. The aim is to be effective and efficient as a plan costs time to prepare. Once the initial time has been invested preparing the initial plan, when changes occur, it might be more efficient to try to reuse previous efforts by repairing it. Also, commitments might have been made to the current plan: trajectory reported to other intelligent agents, assignment of mission plan sections to executors or assignment of resources, etc. Adapting an existing plan ensures that as few commitments as possible are invalidated. Using plan proximity metrics, we prove how similar plans are more likely to be accepted and trusted by the operator than one that is potentially completely different.

Finally, we show during a series of in-water trials how these two elements combined, a decision making algorithm and shared knowledge representation, provide the required interoperability between embedded service-oriented agents to achieve high-level mission goals, detach the operator from the routinary mission decision making and, ultimately, enable the permanent presence of dynamic sensing networks underwater.
