Minimal Representation and Decision Making for Networked Autonomous Agents
Petros G. Voulgaris, Soon‐Jo Chung, Seth Hutchinson, Steve LaValle, Magnus Engestadt · 2015
Abstract : This project addresses fundamental issues that arise in information representation architectures for autonomous reasoning and learning, decentralized planning, and decision-making in multiagent systems. The overall goal of the project is to develop efficient and adaptive strategies to process, represent, exchange, and act upon relevant information from massive data collections, much of which can be irrelevant, imprecise, and contradictory. Within this context we develop results in an array of relevant topics. These include the characterization of the minimum amount of information required by a team of networked agents to solve a geometric task and the minimal number of agents required, the accurate state estimation for agent synchronization, the resilient coordination in the presence of uncertainty and failures, and the multiobjective coordination for safe operation.