1 / 15

Research on Multi-Agent Systems with Applications to Simulation and Training

Research on Multi-Agent Systems with Applications to Simulation and Training. Thomas R. Ioerger Associate Professor Department of Computer Science Texas A&M University. University XXI - DoD funding (1999-2000)

chaela
Télécharger la présentation

Research on Multi-Agent Systems with Applications to Simulation and Training

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. Research on Multi-Agent Systems with Applications to Simulation and Training Thomas R. Ioerger Associate Professor Department of Computer Science Texas A&M University

  2. University XXI - DoD funding (1999-2000) developed TRL for modeling information flow in battalion tactical operations centers (TOCs) with Volz, Yen, and Jim Wall (Texas Center for Appl. Tech.) MURI - AFOSR funding ($4.3M, 2001-2005) worked with cognitive scientists to develop theories of how to use agents in training, e.g. for AWACS with Volz (TAMU), Yen (PSU), Shebilske (Wright) NASA-Langley (current) SATS: future ATC with aircraft self-separation with John Valasek (Aero) and John Painter (EE) Historical Context

  3. TOC Staff - Agent Decomposition Maintain friendly situation, Maneuver sub-units Control indirect fire, Artillery, Close Air, ATK Helicopter S3 FSO Maintain enemy situation, Detect/evaluate threats, Evaluate PIRs S2 CDR Move/hold, Make commands/decisions, RFI to Brigade Companies Scouts Maneuver, React to enemy/orders, Move along assigned route Move to OP, Track enemy

  4. developed at Texas A&M; part of MURI grant from DoD/AFOSR multi-agent system implemented in Java components: MALLET: a high-level language for describing team structure and processes JARE: logical inference, knowledge base Petri Net representation of team plan special algorithms for: belief reasoning, situation assessment, information exchange, etc. CAST: Collaborative AgentArchitecture for Simulating Teamwork

  5. (role sam scout) (role bill S2) (role joe FSO) (responsibility S2 monitor-threats) (capability UAV-operator maneuver-UAV) (team-plan indirect-fire (?target) (select-role (scout ?s) (in-visibility-range ?s ?target)) (process (do S3 (verify-no-friendly-units-in-area ?target)) (while (not (destroyed ?target)) (do FSO (enter-CFF ?target)) (do ?s (perform-BDA ?target)) (if (not (hit ?target)) (do ?s (report-accuracy-of-aim FSO)) (do FSO (adjust-coordinates ?target)))))) MALLET descriptions of team structure evaluated by queries to JARE knowledge base descriptions of team process

  6. CAST Architecture expand team tasks into Petri nets keep track of who is doing each step agent teammates MALLET knowledge base (definition of roles, tasks, etc.) messages human teammates events, actions state data JARE knowledge base (domain rules) simulation make queries to evaluate conditions, assert/retract information models of other agents’ beliefs Agent

  7. Automatic Coordination no need to explicitly encode it - agents infer the need and communicate as necessary Backup Behavior (robustness) if one member fails, others help, since they have shared goals Dynamic Role Selection (flexibility) agents dynamically cooperate to assign tasks to the most appropriate member Proactive Information Exchange (efficiency) agents infer what is relevant to teammates based on their role in team plan Modeling Team Behavior

  8. AWACS - DDD (Aptima, Inc.)

  9. Agents can track trainees’ actions using team plan, offer hints (either online or via AAR) Standard approach: plan recognition Team context increases complexity of explaining actions and mistakes failed because lack domain knowledge, situational information, or “it’s not my responsibility”? Agent-Based Coaching in Teams

  10. Civilian as well as military applications... information management is the key Cognitive Aspects of C2 Naturalistic Decision Making (Klein) Situation Awareness (Endsley) Recognition-Primed Decision Making (RPD) situations: S1...Sn e.g. being flanked, ambushed, bypassed, diverted, enveloped, suppressed, directly assaulted features associated with each situation: Fi1...Fim evidence(Si)=Sj=1..m wij. Fij > qi Modeling Command and Control

  11. Dr. John Valasek, director (Aerospace Engr Dept) fixed-based F4 cockpit flight dynamics models for military (e.g. Harrier), and GA (e.g. Commander-700 twin) 155º wrap-around projection programmable cockpit displays projected heads-up display TAMU Flight Simulation Lab (FSL)

  12. NAV/MAP DISPLAY SYMBOLOGY

  13. Inputs are ADS-B state vectors of aircraft in immediate airspace On-board agents detect potential traffic conflicts Use inter-aircraft negotiation to determine mutually acceptable trajectory changes based on goals, constraints, and intentions Alert Zone Protected Zone TRAFFIC Conflict Detection and Resolution AGENT  

  14. FAF RUNWAY SATS - THE APPROACH Small Aircraft Transportation System ATC: FAA Air Traffic Control. IAF & FAF: Initial- and Final-Approach Fixes. ADS-B: Automatic Dependent Surveillance Broadcast (Radar Xpndr.) AMM: Airport Management Module (Digital Data-Link) • ATC Clears Aircraft to SCA Holding Stack at IAF. • Self-Separation via ADS-B (Req. Conflict Mgt. Software). • Approach Sequencing and Airport Info. via AMM.

More Related