## 2 Selected Features for a Mission Manager for Underwater Cooperative Robotics Operations
### Use of Self-Adaptive System Models  
In an uncertain and unreliable environment like the underwater one it is common to design mission management architectures considering self-adaption to unforeseen events in order to improve the reliability of the vehicles entrusted with the execution of a mission plan. To better understand this concept, it is interesting to know how a self-adaptive system has been usually defined in the literature. In [34] [PERSON] et al. defined an adaptive system as the set of elements interacting with each other that has at least one process controlling the system adaptation to increase its efficiency to achieve its goals. [PERSON] et al. described in [22] a self-adaptive system as that kind of system that has the ability to adapt itself to the changes in its execution environment and internal dynamics with the purpose of continuing to achieve its goals. [PERSON] et al. [35] defined self-adaptive systems as those systems that aim to adjust various artifacts or attributes in response to changes in the self or in the context of the system. There are several self-adaptive models that have been formalized in the literature regarding the design of self-adaptive systems: the Observe, Orient, Decide, Act loop (OODA) [36]; the Event, Condition, Action (ECA) [37]; the Sense, Plan, Act (SPA) [38]; and the autonomic manager model [39; 40], better known as MAPE-K due to the names of its four functional parts (Monitor, Analyze, Plan, Execute), and the use of shared knowledge among them. The OODA loop has been used as a reference in one of the architectures included in this survey, while the ECA and the SPA are typical self-adaptive models used in robotics. However, the most prominent model in recent years, mostly in control systems, has been the MAPE-K. The MAPE-K model defines the concept of an autonomic manager as a component that implements an intelligent control loop consisting in four main functional parts [40], as shown in Figure 3:  
* A Monitor function that gathers the information of the managed resource through the sensors. * An Analyze function that correlates the gathered information from the Monitor to known situations. * A Plan function that provides the relevant actions to be executed according to the results from the Analyze function. * An Execute function that dispatches and control the execution of the actions resulting from the Plan function through the effectors of the autonomic manager. As shown in Figure 3 the MAPE-K includes also a Knowledge function that is shared with the Monitor, Analyze, Plan and Execute functions in the loop. In regards of the design of a self-adaptive system [PERSON] et al. [35] proposed the following questions to obtain their essential requirements:  
* Where, addressing which layer/component is required to do the change. \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Description** \\ \hline
**M** & The architecture proposal includes a multi-agent approach. \\ \hline
**T** & The architecture proposal includes a temporal constraint approach. \\ \hline
**H** & The architecture proposal includes a hierarchical task network. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mission plan specification additional categories assessment (2 nd group). * When, addressing at what time it is needed to apply a change. * What, addressing what attributes or components of the system can and should be changed by means of adaptation, and what is required to be changed. * Why, addressing the motivations of building a self-adaptive system. * Who, addressing the level of automation and human involvement in the self-adaptive system. * How, addressing how the adaptable systems can be changed and which adaptation actions can be appropriate for each situation.