Year: 2018
Type: article
Abstract: The maximization of submodular functions is an NP-Hard problem for certain subclasses of functions, for which a simple greedy algorithm has been shown to guarantee a solution whose quality is within 1/2 of the optimal. When this algorithm is implemented in a distributed way, agents sequentially make decisions based on the decisions of all previous agents. This work explores how limited access to the decisions of previous agents affects the quality of the solution of the greedy algorithm. Specifically, we provide tight upper and lower bounds on how well the algorithm performs, as a function of the information available to each agent. Intuitively, the results show that performance roughly degrades proportionally to the size of the largest group of agents that make decisions independently. Additionally, we consider the case where a system designer is given a set of agents and a global limit on the amount of information that can be accessed. Our results show that the best designs partition the agents into equally sized sets and allow agents to access the decisions of all previous agents within the same set.
Authors: David Grimsman, Mohd. Shabbir Ali, Joao P. Hespanha, Jason R. Marden
Institutions: University of California, Santa Barbara, Orange (France)
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: Distributed learning algorithms orchestration for mobile networks resource management, G1759400851, CAREER: Game Theoretic Methods for Multiagent Coordination, G4504108201, G4612476680, G481154834, EPCN - Online Optimization for the Control of Small Autonomous Vehicles, G8876996369, G8906680872
OpenAlex ID: W2905647983