Distributed team formation in urban disaster environments

In the disaster management, the agents have to coordinate them to form groups of agents to solve disaster tasks. They must satisfy resource, temporal and communication constraints. In multiagent systems, disaster management can be formalized as a task assignment problem (TAP). In TAP, agents with different capabilities must satisfy constraints to assign values associated with the disaster tasks. In other hand, the tasks must join sets of agents with specific features. From the point of view of the task, the disaster management can be formalized as a partitioning or clustering problem. The agents must to cooperate to solve tasks and to minimize damage. The allocation of tasks to groups of agents is necessary when one single agent cannot perform them efficiently. In this paper, we discuss an algorithm to provide partitions of agents to assign tasks in urban disaster environment. Our algorithm creates partitions of agents in a tree-structure factor graph. The vertices are the agents (variable nodes) or the tasks (factor nodes). We explore the efficiency of a recursive cardinality model, and belief propagation to reduce the communication among the agents. Our empirical evaluations show that, by using our approach, it is possible to create partitions of agents to solve the tasks in less time than a swarm intelligence approach. The agents self organize themselves to represent the priorities over the observed states.