CoAX Stand-alone Contributions DARPA Briefing - November 2000 Dartmouth College, UMichigan, MIT Sloan, Coalition Agents eXperiment (CoAX)

Slides:



Advertisements
Similar presentations
Active Jitter Control Stephen F. Bush General Electric Corporate R & D DARPA ITO F C-0230 supported by the Air.
Advertisements

Semantic Web for the Military User C4 Summary of Actions From June 6/7 Meeting.
CoABS Grid Military Users Group (GMUG) for the SWMU Tom Martin November 13, 2001 for LCDR Dylan Schmorrow CoABS Program Manager
Interim Guidance on the Application of Travel and Land Use Forecasting in NEPA Statewide Travel Demand Modeling Committee October 14, 2010.
CoAX – Coalition Agents eXperiment AIAI, BBN, CMU, U.Dartmouth, DSTO, GITI, Lockheed Martin ATL, U.Maryland, U.Michigan, Potomac Inst., QinetiQ, USC/ISI,
M R. S T E V E K A R L / D A L O - S M M / D S N – / S T E V E N. K A R U S. A R M Y. M I L Deliver Logistics Readiness N O V E M B E.
© 2014 wheresjenny.com Dock management DOCK MANAGEMENT.
1 Approved For Public Release; Distribution Unlimited Atmospheric Impacts Routing (AIR) Web Service Support to the Tactical Airspace Integration System.
A Cloud-Assisted Design for Autonomous Driving Swarun Kumar Shyamnath Gollakota and Dina Katabi.
Systems Engineering in a System of Systems Context
Lab 2 Group Communication Andreas Larsson
Chapter 19: Network Management Business Data Communications, 4e.
Effective Coordination of Multiple Intelligent Agents for Command and Control The Robotics Institute Carnegie Mellon University PI: Katia Sycara
Using GIS in Search and Rescue Search: To locate persons in distress Rescue: To retrieve persons in distress, provide for their initial medical or other.
CoABS Grid Component: MultiLevel Coordination Agent Edmund H. Durfee (PI) Brad Clement, Pradeep Pappachan, and Jeff Cox (GSRAs) University of Michigan.
Software Connectors. Attach adapter to A Maintain multiple versions of A or B Make B multilingual Role and Challenge of Software Connectors Change A’s.
Adaptive Infrastructures EPRI/DoD Initiative on Complex Interactive Networks/Systems Joint innovative research ·EPRI and ·Office of the Director of Defense.
Ensuring Non-Functional Properties. What Is an NFP?  A software system’s non-functional property (NFP) is a constraint on the manner in which the system.
CoAX Technology Contributions TTCP Meeting - Malvern - November 2000 AFRL Rome, AIAI, Boeing, Dartmouth, DERA Malvern, Lockheed Martin ATL, Michigan,
Modeling Capabilities and Workload in Intelligent Agents for Simulating Teamwork Thomas R. Ioerger, Linli He, Deborah Lord Dept. of Computer Science, Texas.
Client-Server Computing in Mobile Environments
New Challenges in Cloud Datacenter Monitoring and Management
Client/Server Grid applications to manage complex workflows Filippo Spiga* on behalf of CRAB development team * INFN Milano Bicocca (IT)
Machine Learning and Optimization For Traffic and Emergency Resource Management. Milos Hauskrecht Department of Computer Science University of Pittsburgh.
Computer System Architectures Computer System Software
© 2006 Avaya Inc. All rights reserved. Avaya Services Michael Dundon Business Development Manager.
Michael Ernst, page 1 Collaborative Learning for Security and Repair in Application Communities Performers: MIT and Determina Michael Ernst MIT Computer.
. Traffic Flow Management System Benefits Flexibility for Future Growth: TFMS provides a modern software architecture to meet future growth and support.
Service Transition & Planning Service Validation & Testing
Evgueni (Eugene) Khokhlov1 A Mobility-Centric Data Dissemination Algorithm for Vehicular Networks (MDDV) Evgueni (Eugene) Khokhlov.
CoAX Technology Contributions AFRL Rome, AIAI, Boeing, Dartmouth, DERA Malvern, Lockheed Martin ATL, Michigan, MIT Sloan, OBJS, USC/ISI, UWF/IHMC Support.
Lab 2 Group Communication Farnaz Moradi Based on slides by Andreas Larsson 2012.
CoAX Technology Contributions TTCP Meeting - Malvern - September 2000 AFRL Rome, AIAI, Boeing, Dartmouth, DERA Malvern, Lockheed Martin ATL, Michigan,
Parallel and Distributed Simulation Introduction and Motivation.
Advanced Computer Networks Topic 2: Characterization of Distributed Systems.
I-Room: a Virtual Space for Intelligent Interaction Low cost, simple setup, mixed-reality meetings spaces and operations centres
INFORMATION MANAGEMENT Unit 2 SO 4 Explain the advantages of using a database approach compared to using traditional file processing; Advantages including.
September 25, 2013 Greg Davis FHWA Office of Safety Research, Development and Test Overview of V2I Safety Applications.
Farnaz Moradi Based on slides by Andreas Larsson 2013.
Intelligent Agents RMIT Prof. Lin Padgham (leader) Ass. Prof. Michael Winikoff Ass. Prof James Harland Dr Lawrence Cavedon Dr Sebastian Sardina.
SMUCSE 8394 BTS – Communication Technologies. SMUCSE 8394 Objectives To establish and maintain a unifying exchange and sharing framework for different.
EURAILSPEED - Milano – November 8th, 2005 MGV SNCF Train Set for on-board Measurement at Great Velocity Eric REBEYROTTE– Technical Manager of SNCF Engineering.
Database Administration
Coalition Agents eXperiment (CoAX) The Coalition TIE AFRL Rome, AIAI, Boeing, Dartmouth, DERA Malvern, Lockheed.
SOFTWARE DESIGN AND ARCHITECTURE LECTURE 13. Review Shared Data Software Architectures – Black board Style architecture.
CoAX – Coalition Agents eXperiment AIAI, BBN, CMU, Dartmouth, DSTO, GITI, Lockheed Martin ATL, NRL, Potomac Inst., U.Maryland, U.Michigan, QinetiQ, UT-Austin,
The Analytic Blunder Risk Model (ABRM) A computer model for predicting collision risk Kenneth Geisinger Operations Research Analyst Federal Aviation Administration.
Wireless sensor and actor networks: research challenges
Parallel and Distributed Simulation Data Distribution II.
A Binary Agent Technology for COTS Software Integrity Anant Agarwal Richard Schooler InCert Software.
Application Communities Phase 2 (AC2) Project Overview Nov. 20, 2008 Greg Sullivan BAE Systems Advanced Information Technologies (AIT)
PGDM/ / II Trimester/E-Business. What is supply chain management?  Supply chain management is the co- ordination of entities, activities, information.
1 ME Spring 2015 Systems Engineering, Part II Session 8 5 February 2015 Mr. Larry Hopp, CPL © Copyright 2013.
Wireless sensor and actor networks: research challenges Ian. F. Akyildiz, Ismail H. Kasimoglu
1 OF 17 INFORMATION TECHNOLOGY CAPITAL PLANNING FOR YOUR ENTERPRISE Steven Carpenter 14 October 2006.
Chapter 19: Network Management
Albert I. Reuther & Joel Goodman HPEC Sept 2003
Constructs agent’s situational picture from messages and sensor input
CoAX - Coalition Agents Experiment
Street Cleanliness Assessment System for Smart City using Mobile and Cloud Bharat Bhushan, Kavin Pradeep Sriram Kumar, Mithra Desinguraj, Sonal Gupta Project.
Description of Revision
Field Teams and Wireless Networks
Building a Database on S3
Large-Scale Mobile-Agent Systems
Distributed Sensing, Control, and Uncertainty
DARPA Coalition Agents eXperiment - CoAX DARPA CoABS, AFRL, BBN, Boeing, DSTL, DSTO, Dartmouth, UEdinburgh/AIAI, LM-ATL, OBJS, QinetiQ, UMichigan, USC/ISI,
Introduction of Week 13 Return assignment 11-1 and 3-1-5
Sybert Stroeve, Henk Blom, Marco van der Park
Operations Management
Multilevel Mission Coordination for Coalition Operations
Presentation transcript:

CoAX Stand-alone Contributions DARPA Briefing - November 2000 Dartmouth College, UMichigan, MIT Sloan, Coalition Agents eXperiment (CoAX) DARPA

CoAX /Tech Briefing - 2 Stand-alone Contributions u Dartmouth Field Observation Agent u MIT Robustness Service u Michigan Coordination Planning Aid

CoAX /Tech Briefing - 3 ActComm Project Dartmouth, Harvard, RPI, Illinois, ALPHATECH, Lockheed Martin Department of Defense Multidisciplinary University Research Initiative Developing a system to provide network access to soldiers in the field CoAX Goal Demonstrate the ease with which the large ActComm “legacy” system can be integrated with the rest of CoAX via the DARPA CoABS Grid Field Observations (Dartmouth)

CoAX /Tech Briefing - 4 Field Observations (Dartmouth) Team of soldiers PDA’s Ad-hoc wireless networking Soldiers make observations. Ground and air traffic Personnel and equipment Buildings and other structures Observations fed into battle-planning systems (e.g., MBP) through the CoABS Grid. In the demo, a team of CoAX soldiers will make observations to correct Gao misinformation.

CoAX /Tech Briefing - 5 Observations Observation Agent D’Agents API Grid API I see a tank! Observation Viewer MBP (9-month demo - standalone) (18-month demo - integrated) Query/ Response Registration/ Update Stream Field Observations (Dartmouth)

CoAX /Tech Briefing - 6 Field Observations (Dartmouth) 29-SEP :47.56 OBSERVATION 0018 VEHICLE Observer : N, E, Elevation 530 m Sightline: 270 deg, 0 deg down, 2000 m Vehicle : Gao, flatbed truck, 3 axles, heading: 180, speed: 60 km/h Note : 12 soldiers in flatbed

CoAX /Tech Briefing - 7 The Challenge: Robust Agent Coalitions u Coalitions are open systems u Dynamic membership, often novel partners u Agents in open systems will be unreliable u Intermittent bugs (3 per 1000 lines in the best crafted code) as well as the possibility of malice u Infrastructures can be unreliable u Current failure tolerance approaches are insufficient u Assume closed systems (e.g. mirroring) u Full rollbacks are unnecessarily inefficient for agents

CoAX /Tech Briefing - 8 The MIT Robustness Service u Monitors agent ‘health’ via polling u Responds to agent failure via intelligent task cancellation & task re-announcement u Maintains reliability information (for failure avoidance) u Designed for open systems - makes minimal assumptions about agents

CoAX /Tech Briefing - 9 A Working Grid Service Message Log Robustness Service EH API u Transparently infers commitment structures u Assumes (some) agents support (some of ) EH API u Polling (backup: existing Grid is-alive? method) u Task re-announce u Cancel-task

CoAX /Tech Briefing - 10

CoAX /Tech Briefing - 11 Benefits Validated Empirically u Up to 3x speedup and 8x reduced variability vs. standard timeout-retry approach u Benefits increase with task complexity (decomposition tree height) and with level of EH API support u 05.ps

CoAX /Tech Briefing - 12 Michigan Multilevel Coordinator Agent u Analyses the alternative plan spaces of coalition functional teams that plan independently and act asynchronously u Works top-down with plans chosen by teams to predict unintended interactions (resource contentions; friendly fire). u Identifies candidate resolutions (timing or action constraints). u Notifies process panel of possible plan conflicts and computed workarounds. u Operationalizes/enforces coordination decisions selected. u Given more time, isolates and resolves conflicts more precisely and efficiently. u Allows planning and coordination decisions to be postponed until runtime conditions become better known. u Packaged as a Grid-aware component that will be proactively executing and will be utilized by the AIAI Process Panel.

CoAX /Tech Briefing - 13 Potential plan conflicts include friendly fire in TEZ on ArmyDiv2, destruction of roads through E that ArmyDiv2 might need, and contention for sea and rail transport among army divisions and logistics. Michigan Coalition Coordination Example Forces begin at aircraft carrier AC Airforce sorties to C, E, & Q for Total Exclusion Zone (TEZ) Logistics delivers humanitarian aid to refugees at F and R ArmyDiv1 occupies X to prevent Agadez forces from reaching and inciting refugees at R ArmyDiv2 crosses TEZ to occupy Y to monitor for Gao crossings

CoAX /Tech Briefing - 14 Coordinated Plans Hierarchical plan coordination incrementally recommends coordinated plans that are increasingly detailed and parallelized Fly sorties Move to X Time = cpu sec. Time = cpu sec. Time = cpu sec. Move to Y Logistics Airforce Army Div 1 Army Div 2 Move C1  R Fly sorties AC  P Move to Y Logistics Airforce Army Div 1 Army Div 2 PP PP P  ZZ  X Move C1  R, C2  F Fly sorties AC  P Move to Y Logistics Airforce Army Div 1 Army Div 2 PP PP P  ZZ  X Move C2  F PP PPMove C1  R, C2  F

CoAX /Tech Briefing - 15 Michigan Multilevel Coordinator Agent