A Model Based on Cellular Learning Automata for Improving the Intelligent Assistant Agents & Its Application in Earthquake Crisis Management

Spatial-temporal coordination problem (STCP) plays a critical role in urban search and rescue (USAR) operations. Artificial Intelligence has tried to tackle this problem by taking advantage of multi-agent systems, GIS, and intelligent algorithms to enhance the task allocation by establishing collaboration between human agents and intelligent assistant agents. This paper presents a model based on cellular learning automata (CLA) to improve the teamwork interaction between human-agent teams in performing the distributed tasks. In this model, the main objective is to add the learning ability to the assistant agents in a way that they can guide human-agent toward the optimal decision(s). The effectiveness of the proposed model is evaluated on different scenarios of an earthquake simulation. Results indicate that the proposed model can significantly improve the rescue time and the maximum distance traveled by the rescue teams.