Distributed task allocation in multi-agent environments using cellular learning automata

People's safety is threatened by the repetition of critical events. Many people lose their lives due to unprofessional rescue operation as well as time pressure of the rescue operation. A key problem in urban search and rescue teams, considering the severe turbulence and complexity of the environments which are hit by a crisis, is the coordination between the team members. In order to solve this problem, an effective plan would be the provision of measures where human works with intelligent assistant agents to assign the tasks in any way. Dynamic tasks are identified by the human agent of the rescue team in the crisis environment and are characterized by spatial-temporal characteristics assigned to the appropriate rescue team by the intelligent assistant agents who apply intelligent decision-making techniques. The objective of this study is to propose a new approach for allocating spatial-temporal tasks in multi-agent systems through cellular learning automata as the decision-making technique. Results obtained here indicate that this proposed model can significantly improve the rescue time and space. Rescue teams could cover all critical areas by going through the minimum distance to make maximum use of time.