GT-BDI: 

A combined game-theoretic and BDI-based computational model for emergency evacuation with search-and-rescue robots

The impact of disasters on the affected population is catastrophic. Proper management practices are needed to reduce the societal and economical damages caused by disasters. These include planning efficient search-and-rescue (SaR) missions, which pertain measures to find and rescue trapped victims in time. 

SaR in general is very challenging, as the number of the trapped victims may be unknown and their behavior while trying to evacuate the disaster area is prone to variations. SaR robots have recently gained lots of attention in assisting the SaR crew to find and rescue the victims. 

A behavioural model of the victims can provide insights for both the staff and the robots in SaR missions on how the trapped victims act during an evacuation, and to plan the SaR mission accordingly. Such a model, after being validated, can also be used for analysis of the influence of the robots in SaR missions. 

This project proposes a novel evacuation model that integrates game theory and the belief-desire-intention (BDI) framework, in order to incorporate in the model both the interactions of the trapped victims, as well as their cognitive processes at the individual level. The model is validated using existing benchmark models for evacuation behaviour. Furthermore, the validated model is used to assess the effectiveness of the SaR robots within the evacuation procedure. It is found that the presence of SaR robots can reduce the evacuation time, as a function of the trust of the victims in these robots.