Efficient decision-making for multiagent target searching and occupancy in an unknown environment

Target searching in an unknown environment is a traditional research issue in the multiagent area. In some real cases, the agents do not only discover the targets; instead, they have subsequent tasks that must be completed before a deadline. In this paper, these cases are abstracted as the agents searching for target locations in an unknown environment and then occupying these target locations within a limited time. The agents can obtain rewards by occupying the target locations, and the goal of this problem is to maximize net income, defined as total reward minus the moving cost of the agents. This problem can be transformed into the traditional problems, and then be solved by previous related algorithms. However, this approach is not optimal. In this paper, we present a method that combines previous algorithms and a decision-making algorithm. The experiments demonstrate that the method containing our decision-making algorithm can lead to higher net income than simply using previous algorithms.This work was supported by the National Natural Science Foundation of China (61472079, 61807008 and 61806053), the Natural Science Foundation of Jiangsu Province of China (BK20171363, BK20180356, BK20180369)