Global-Entropy Driven Exploration with Distributed Models under Sparsity Constraints
Source abstract
Abstract
This paper focuses on exploration when using different data distribution schemes and ADMM as a solver for swarms. By exploration, we mean the estimation of new measurement locations that are beneficial for the model estimation. In particular, the different distribution schemes are splitting-over-features or heterogeneous learning and splitting-over-examples or homogeneous learning. Each agent contributes a solution to solve the joint optimization problem by using ADMM and the consensus algorithm. This paper shows that some information is unknown to the individual agent, and thus, the estimation of new measurement positions is not possible without further communication. Therefore, this paper shows results for how to distribute only necessary information for a global exploration. We show the benefits between the proposed global exploration scheme and benchmark exploration schemes such as random walk and systematic traversing, i.e., meandering. The proposed waypoint estimation methods are then tested against each other and with other movement methods. This paper shows that a movement method, which considers the current information within the model, is superior to the benchmark movement methods.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Distributed Control Multi-Agent Systems · Target Tracking and Data Fusion in Sensor Networks · Insect Pheromone Research and Control
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
German Aerospace Center, Institute of Communication and Navigation, Münchener Straße 20, 82234 Wessling, Germany
References (28)
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