1 / 13

Probabilistic Inference in Multi-Agent Systems

Probabilistic Inference in Multi-Agent Systems. Steven Reece Oxford University. State. Estimate. Covariance ellipse. agent 1. agent 2. agent 4. agent 3. Context. Estimation Target tracking Map building Decentralised estimation Multiple observers No central estimator

osgood
Download Presentation

Probabilistic Inference in Multi-Agent Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Probabilistic Inference in Multi-Agent Systems Steven Reece Oxford University ARGUS II DARP - Unclassified

  2. State Estimate Covariance ellipse agent1 agent2 agent4 agent3 Context • Estimation • Target tracking • Map building • Decentralised estimation • Multiple observers • No central estimator • Local message passing • Inference graph can be arbitrary ARGUS II DARP - Oxford University - Unclassified

  3. Data Incest Problem • Multiple estimates • Correlated errors • Maintain correlations • Centralised • Choke network • … or infer bounds on correlations! agent1 agent2 agent4 x2 P22 x1P11 agent3 Estimate Covariance ARGUS II DARP - Oxford University - Unclassified

  4. Rival Approaches • Existing technology • Kalman filter ignores correlations. Fused estimates can be too confident • E.g. Disaster when aircraft believe they are sufficiently far apart to manoeuvre! • Covariance intersection (CI) assumes all correlations are possible. Fused estimates can be uninformative • E.g. Disaster when aircraft must manoeuvre but have insufficient information about how far apart they are. They are flying blind! • New technology • Covariance inflation (CInf) ARGUS II DARP - Oxford University - Unclassified

  5. agent2 agent4 x2 P22 x1P11 agent3 Covariance Inflation/Deflation • Family of 2D covariance matrices. • Crucially, correlation is boundable. • Fit outer or inner ellipseto family. • Reduce risk. ARGUS II DARP - Oxford University - Unclassified

  6. Covariance Inflation • Transmitter agent knows fraction of its own estimate that could be shared by other agents (coupling scalar). • Agents communicate • Estimate vector • Covariance matrix • Coupling scalar • Receiver determines correlation bounds by combining coupling scalars. agent2 agent4 x2 P22 x1P11 agent3 + coupling scalar + coupling scalar ARGUS II DARP - Oxford University - Unclassified

  7. Efficiency and Computational Cost • CInf requires only minor changes to existing data fusion code. • CInf invokes some extra computational cost for each agent but no significant communication cost. • Along with the estimate and covariance matrix, an agent is required to communicate an extra scalar only. • Critical for limited bandwidth applications! • Both the Kalman filter and Covariance intersection are special cases of CInf. • CInf estimates are more certain than those of its nearest rival, Covariance Intersection (CI). ARGUS II DARP - Oxford University - Unclassified

  8. Application • SLAM • Vehicle location is uncertain • Landmark estimates therefore inherit common error • Correlated errors everywhere! • DSLAM • Multiple platforms • Limited bandwidth • Communicate sub-maps • Sub-maps are correlated! ARGUS II DARP - Oxford University - Unclassified

  9. Simulator Details • Simulator developed from code supplied by Eric Nettleton, now at BAE SYSTEMS • Scenario comprises • Two agents • Each communicates a sub-map every 10 time steps • Compare CInf and CI • … you will see • Individual feature location uncertainty (ellipses) • Total uncertainty in combined agent/feature estimates ARGUS II DARP - Oxford University - Unclassified

  10. CInf CI Comparison of Covariance Inflation (CInf) and Covariance Intersection (CI) ARGUS II DARP - Oxford University - Unclassified

  11. Many Applications of CInf • Applications described in this conference • Loopy communication networks • SLAM • Area surveillance (QinetiQ) • Free flight (BAE SYSTEMS) • Failure risk envelopes (Rolls-Royce) • Data reduction (academic demonstrator) • Also … • Multi-agent fault detection (decorrelation of fault bids) • Control (behaviour envelopes) ARGUS II DARP - Oxford University - Unclassified

  12. Take Home Message • Data incest is a significant problem for flexible multi-agent information systems. • Covariance Inflation (CInf) offers a robust, efficient and computationally inexpensive solution to data incest problems. • For full details, see our publication to appear at the Eighth International Conference on Information Fusion • … and available on-line on the ARGUS web site. ARGUS II DARP - Oxford University - Unclassified

  13. Questions? ARGUS II DARP - Unclassified

More Related