Agent-based task allocation under uncertainties in disaster environments: An approach to interval uncertainty

The uncertainty of task allocation in disaster environments is challenging both practically and theoretically. In real environments, agents encounter uncertain factors for the selection and execution of tasks. The present methods for task allocation seem inadequate in such environments. This paper aims to provide an efficient approach to improving the task allocation, despite the uncertainty in disaster environments. Therefore, after deduction of the major uncertainties in disaster environments, we propose a method for the agents' decision-making about the task allocation. The allocation procedure includes four phases of ordering the tasks, choosing the coordinating agent, implementing the auction by considering the uncertainties, performing tasks and observing the real environment. The main innovation of this research is using the concepts of interval uncertainty in the task ordering as well as in the auction implementing. The results were obtained by comparing the proposed method with the contract net protocol (CNP) at three scales. In addition, the results were evaluated in the presence of uncertainties at different ranges. On average, the proposed method was better than the CNP in terms of search and rescue (SAR) operation time (124 min), the number of dead people (8) and the number of incorrect allocations (180 tasks). The uncertainties range in the tasks' decision-making procedure affected SAR operation time by more than 26%. Therefore, considering that uncertainty in task allocation can be a great advantage in the disaster environment, and considering these factors in the decision-making procedure, we have improved confidence in allocating tasks with fewer errors.