## 1 Introduction  
In recent years, the use of autonomous underwater vehicles (AUVs) for cooperative robotics in underwater operations has increased. This interest includes their use in military interventions [1; 2; 3], scientific interventions like the survey and exploration of the Arctic Ocean [4; 5], and other marine interventions like, for instance, underwater infrastructure maintenance [6; 7; 8] and oil spill response [9; 10; 11]. The essential information structure for these operations has been identified as the mission. [PERSON] et al. defined a mission as a set of goals to be achieved [12], and a mission plan as the schema for achieving the goals of a mission, expressed in terms of tasks like maneuvers, communication, sensors, and payload primitives. [PERSON] et al. described a mission as the set of tasks a vehicle must execute [13]. In their proposal, they described the mission life cycle as the process depicted in Figure 1. A mission is first created by a human operator, either by hand or using the aid of an automated planning application. The created mission is validated, and then transferred to the vehicle, where it is executed. The proposed life cycle includes an optional step for replaying the mission in a simulator for offline analysis. Mission management can then be defined as the set of processes and techniques to control the mission life cycle. The study of mission management for AUV interventions has attracted the interest of the research community, but mostly focused on the creation, and maybe the validation of the mission plan. The definition of the mission plan is equivalent to the definition of a plan as a sequence of actions to achieve a goal used in AI literature [14]. And consequently it is common that mission plans for underwater operations are generated with the aid of automated planning algorithms adapted from the AI literature. For instance, a comprehensive survey of mission planning solutions used for autonomous marine vehicle fleets was done by [PERSON] and [PERSON] in [15]. In their work, they restricted the planning paradigms applicable to marine operations to the following four state-action planning methods:  
* Deterministic planners: the most common and basic ones, that typically assume that he execution environment does not change at all. * Multi-Agent planners: where the agents are individual vehicles participating in a mission. * Temporal planners: where specified goals must be reached in a finite time. * Non-Deterministic planners: referred to as planners working with partially observable Markov decision processes, and that provide a policy to be used to map any state to a suitable action. Other works published related to mission management include the survey on autonomous mission planning and management systems done by [PERSON] et al. for AUVs and Unmanned Aircraft Vehicles (UAVs) [16]. Their survey focuses mainly on the autonomy and the situation awareness features of the selected architectures. Moreover, most of the architectures included in their work are focused on the mission planning part, including route planning and re-planning. [PERSON] et al. conducted another survey on the state-of-the-art for autonomous mission planning and task managing for unmanned vehicles [17]. Again, most of the cited architectures were focused on topics related to the mission planning part, including rout planning, resource allocation and re-planning. Nevertheless, we find the need to address other aspects related to mission management beyond the creation of the plan. First of all, still related to the mission planning, the AI literature related to automated planning includes several specifications for how to describe a plan. However, usually the literature about mission planning and management architectures for cooperative robotics operations do not cover this. Further, we have to take into account that the underwater environment is usually considered as unreliable and uncertain. And when there is uncertainty, an agent cannot ensure that it will be able to achieve the expected goals [18].