Bio-Inspired Collective Decision Making in Multiagent System: From Low- to High-Cognition Algorithms

Abstract

At the beginning, this thesis proposes an algorithm inspired by ant's scout-and-recruit for foraging providing a low-cognition algorithm to rescue civilians in distress and explaining how it could be achieved. Being low-cognition algorithm, this algorithm is characterized by having minimal computation and communication between the agents of the MAS as well as being applicable for large scale environment due to its inspiration from ants. Then, an algorithm inspired by human's conformity is provided which is considered a high-cognition algorithm. This algorithm is applied to a rescue mission that is simulated using \emph{RoboCup Rescue Simulator}. Being high-cognition algorithm, this algorithm is characterized by having heavy computation and communication made by the agents of the MAS. Finally, the conformity algorithm is integrated on top of the scout-and-recruit algorithm to enhance its performance. While the low-cognition part insures its applicability to large scale, the high-cognition algorithm pushes the agents to make smarter decisions enhancing the overall system performance.