Abstract
When solving a distributed problem based on a multi-agent system, the local behaviors of agents are aggregated to the global behaviors of the multi-agent system towards a solution state. This work presents a distributed discrete Lagrange multiplier (DDLM) method for solving distributed constraint satisfaction problems (distributed CSPs). In this method, the local behaviors of agents are aggregated as a descent direction of an objective function corresponding to the problem at hand. Thus, a trend to a solution state are formed. Furthermore, we provide three techniques to speed up the aggregation of agents' local behaviors. Through experiments on benchmark graph coloring problems, we validate the effectiveness of the presented DDLM method as well as the three techniques in solving distributed CSPs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
