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)