GPU-based bat algorithm for discovering cultural coalitions

Abstract

Nowadays, artificial intelligence makes a great success in our modern social life. The human should be prepared to be able to live with social robots that can provide him comfort and help in solving complex processes. Extending the use of robot’s technology is certainly desirable, but preventing certain catastrophes from the misdeeds of artificial intelligence is crucial. One of these troubles could be for instance the creation of robots’ coalitions to impose pernicious decisions. As a contribution to cope with such issue, we propose a parallel approach for the detection of cultural coalitions based on Bat Algorithm. This GPU-based bat algorithm approach can treat very large datasets due to the possibility of launching several artificial bats simultaneously, which contribute to reducing the runtime without affecting the performance. To proof the effectiveness of the parallel detection coalition method, we conducted several experiments on datasets of different sizes. These datasets represent the result of cultural artificial agents playing the colored trails (CT) game. For the creation of agents’ profiles, we use real cultural datasets generated based on the WV survey. The experimental analysis demonstrates that the use of the proposed method will considerably reduce the runtime.