Leung, HenryAichour, Hichem Zakaria2014-05-142014-06-162014-05-142014Aichour, H. Z. (2014). Bio-Inspired Collective Decision Making in Multiagent System: From Low- to High-Cognition Algorithms (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25126http://hdl.handle.net/11023/1520At 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.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Artificial IntelligenceEngineering--Electronics and ElectricalRoboticsCDMMASBio-Inspiredmaster thesis10.11575/PRISM/25126