Minimal Representation and Decision Making for Networked Autonomous Agents

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.

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Document Details

Document Type
Technical Report
Publication Date
Aug 27, 2015
Accession Number
AD1001335

Entities

People

  • Magnus Engestadt
  • Petros G. Voulgaris
  • Seth A. Hutchinson
  • Soon-Jo Chung
  • Steve Lavalle

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Autonomy
  • Engineered Resilient Systems
  • Human Systems
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Autonomous Agents
  • Classification
  • Collision Avoidance
  • Computations
  • Control Systems
  • Electronic Mail
  • Multiagent Systems
  • Network Topology
  • Probability
  • Probability Distributions
  • Robotic Swarms
  • Robots
  • Sensor Networks
  • Uncertainty

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.