## 2 Related Work  
The design of intelligent exploration algorithms and coordination mechanisms for multi-agent systems applied to target searching includes different multi-agent coordination approaches for environment exploration. According to the occupancy maps, the most common deliberative technique is the frontier-based exploration algorithm [11]. The technique is based on identifying the boundary or frontier of the map-covered area; and then, select the proper frontier where the next agent should move. When the target frontier is assigned to an agent, the goal is to approach the frontier using the least number of moves while avoiding obstacles. To effectively plan the path to reach the frontier, probability-based algorithms, such as Probabilistic Road Map and Rapid Exploring Random Trees, can be used [12]. This path planning is a Non-Polynomial (NP) hard problem, and it can require heavy computations in cluttered environments. In addition, the development of an exploration algorithm based on a utility function to make effective decisions by a single robot has been explored. For example, the utility function can be designed to achieve new navigation goals by the agent, considering short distances and higher chances to increase the knowledge about the map. Some param eters (factors) used to enhance the implementation of these algorithms, and therefore, the agent decision-making, are cluster, distance, clearance, and unreachable-points [13]. Single robot exploration has been expanded towards the implementation of cooperative exploration. To enable such exploration using frontier-based algorithms, multiple agents must share their local maps to find together the global frontiers. If the agents can be located within the environment, they can share and merge their findings by summing or multiplying the state values in each local map [14]. However, if the location is not accurate enough, the agents must use probabilistic algorithms to merge the local map information. For instance, particle filters can support a group of agents to merge local maps under the agents uncertainties [15]. Furthermore, the agents can negotiate to allocate frontiers to more suitable agents when the local maps are merged into a global map. In [16], a potential field-based algorithm makes it possible for each agent to select the closest frontier to approach. In addition, to avoid an agent from attempting to move to a nearby but inaccessible frontier, a higher priority is given to a visible frontier. An optimal frontier assignment algorithm allows the agents to select frontiers sequentially. Once an agent has selected its destination frontier, this frontier relative weight will decrease so that the next agent will no longer select this frontier [17]. In the literature, the random search is most frequently applied for target searching [18]; however, it is key to coordinate agents to search efficiently for multi-agent systems. For instance, by creating a special pattern following it, the agents have better sense/sensitive coverage and can avoid re-exploring the area that has already been covered [19]. Several projects involve multi-agent cooperation architectures. For instance, [PERSON] et al. consider teams of miniature agents who overcome the limits imposed by their small scale by exchanging mapping and sensor data [20; 21]. As a part of this plan, a team leader incorporates the information collected by the other agents. In addition, it orders the other agents to bypass obstacles or direct them to unknown areas. [PERSON] and [PERSON] review different task assignment strategies in agent teams and analyze the team performance through extensive experimentations [22]. [PERSON] et al. consider the problem of a collaborative exploration of an unknown environment by multiple agents [23] but restricting the agents movement so that two agents do not approach the same target position or visit a position in the visibility area of another. The algorithm considers at the same time the utility of frontier cells and the cost for achieving them.