Presentation on theme: "Research on Multi-Agent Systems with Applications to Simulation and Training Thomas R. Ioerger Associate Professor Department of Computer Science Texas."— Presentation transcript:
Research on Multi-Agent Systems with Applications to Simulation and Training Thomas R. Ioerger Associate Professor Department of Computer Science Texas A&M University
Historical Context University XXI - DoD funding ( ) –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, ) –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)
TOC Staff - Agent Decomposition CDR FSO S3 S2 Companies Scouts Control indirect fire, Artillery, Close Air, ATK Helicopter Maintain enemy situation, Detect/evaluate threats, Evaluate PIRs Maintain friendly situation, Maneuver sub-units Maneuver, React to enemy/orders, Move along assigned route Move to OP, Track enemy Move/hold, Make commands/decisions, RFI to Brigade
CAST: Collaborative Agent Architecture for Simulating Teamwork 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.
MALLET (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)))))) evaluated by queries to JARE knowledge base descriptions of team structure descriptions of team process
CAST Architecture MALLET knowledge base (definition of roles, tasks, etc.) JARE knowledge base (domain rules) Agent expand team tasks into Petri nets keep track of who is doing each step make queries to evaluate conditions, assert/retract information models of other agents’ beliefs agent teammates human teammates simulation messages events, actions state data
Modeling Team Behavior 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
AWACS - DDD (Aptima, Inc.)
Agent-Based Coaching in Teams 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”?
Modeling Command and Control 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: S 1...S n e.g. being flanked, ambushed, bypassed, diverted, enveloped, suppressed, directly assaulted –features associated with each situation: F i1...F im –evidence(S i )= j=1..m w ij. F ij > i
TAMU Flight Simulation Lab (FSL) 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
NAV/MAP DISPLAY SYMBOLOGY
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 TRAFFIC Conflict Detection and Resolution AGENT Protected Zone Alert Zone
SATS - THE APPROACH Small Aircraft Transportation System 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. FAF RUNWAY 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)