**5. Results**

12 Underwater Vehicles

Fig. 8. Example of a partial ordered plan representation of an autonomously generated UUV

*πq*. It can be seen that mission repair better exploits the orientation capabilities for decision making: instead of taking the new mission environment as a given, it uses the diagnosis

We have now identified the benefits of mission plan repair over mission replan. Mission plan repair modifies the partial plan *ψq*, so that it uses a different composition, though it still maintains some of the actions and the constraints between actions from the previous partial plan. However, mission plan adaptation can also be achieved by mission execution repair by looking directly at the mission plan instantiation *πq*. Execution repair modifies

instantiated by some execution *eq* is newly bound by another action execution instance *e*

Executive repair is less expensive and it is expected to be handled directly by the mission executive agent. Plan repair, however, is computationally more expensive and requires action

The objective is to maximise the number of execution repairs over plan repairs and, at the plan repair level, maximise the number of decisions reused from the previous mission instantiation. The information provided by the semantic-base knowledge base during the plan diagnosis

Executive repair fixes plan failures identified in the mission plan during the diagnosis stage. Our approach uses ontology reasoning in combination with an action execution template to

Once a mission plan *π<sup>q</sup>* is calculated by the mission planner, its list of ground actions is

script of commands required to perform its correspondent ground action. Flexibility in the execution of an action instance is critical in real environments. This is provided by a timer, an execution counter, a time-out register and a register of the maximum number of executions in the action execution instance. Additionally, three different outputs control the success, failure or time-out of its execution. These elements handle the uncertainty during the execution phase and enable the executive repair process. This minimise the number of calls to the adaptive

*<sup>q</sup>* that was previously

*<sup>q</sup>* of *π<sup>q</sup>* gets instantiated

*<sup>q</sup>* contains the

*<sup>q</sup>* using the action template for the action *ah* available in

*q*.

mission. The ordering constraints are represented using the graph depth, interval preservation constraints are represented with black arrows, point truth constraints are represented with PTC-labelled arrows, and binding constraints are shown in the top left box.

information about the changes occurred to guide the adaptation process.

the instantiation of the mission plan *<sup>π</sup><sup>q</sup>* such that a ground action *<sup>g</sup>ah*

transferred to the executive layer. In this layer, each ground action *gah*

mission planner agent and therefore the response time for adaptation.

the Core Ontology of the knowledge base. At the end of this phase, each *e<sup>t</sup>*

of the mission planner agent.

adapt the mission plan at the executive level.

into an action execution instance *e<sup>t</sup>*

phase is critical.
