research on multi-agent systems with applications to simulation and training thomas r. ioerger...
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Research on Multi-Agent Systems withApplications to Simulation and Training
Thomas R. IoergerAssociate Professor
Department of Computer ScienceTexas A&M University
Historical Context• 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)
TOC Staff Training Layout
S2
S3 FSO
Trainer/Observer OTBScenario
CDR
BDE Trainees
BN
Agents
TOC Staff - Agent Decomposition
CDR
FSO
S3
S2
CompaniesScouts
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 AgentArchitecture 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 JAREknowledge base
descriptions of team structure
descriptions of team process
CAST Architecture
MALLETknowledge base(definition of roles,tasks, etc.)
JARE knowledge base (domain rules)
Agent
expand team tasksinto Petri nets keep track of
who is doingeach step
make queriesto evaluateconditions,assert/retractinformation
models of otheragents’ beliefs
agent teammates
human teammates
simulation
messages
events, actionsstate 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: S1...Sn
• e.g. being flanked, ambushed, bypassed, diverted, enveloped, suppressed, directly assaulted
– features associated with each situation: Fi1...Fim
– evidence(Si)=j=1..m wij . Fij > 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 APPROACHSATS - THE APPROACHSmall Aircraft Transportation SystemSmall 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 TrafficControl.
IAF & FAF: Initial- andFinal-Approach Fixes.
ADS-B: AutomaticDependent SurveillanceBroadcast (Radar Xpndr.)
AMM: Airport ManagementModule (Digital Data-Link)
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