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Modeling Teamwork and Command and Control in Multi-Agent Systems Thomas R. Ioerger

Modeling Teamwork and Command and Control in Multi-Agent Systems Thomas R. Ioerger Department of Computer Science Texas A&M University. similar to process networks, or HTN’s

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Modeling Teamwork and Command and Control in Multi-Agent Systems Thomas R. Ioerger

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  1. Modeling Teamwork and Command and Control in Multi-Agent Systems Thomas R. Ioerger Department of Computer Science Texas A&M University

  2. similar to process networks, or HTN’s has a customized knowledge representation language (TRL) for encoding knowledge about tasks and methods (doctrine, mission) agents run as independent processes each may have multiple parallel activities agents represent staff positions (S2, S3...) communicate with each other for teamwork interact with humans (via forms: info/cmds) interact with OTB for scenario simulation TaskableAgents Architecture

  3. Simulation OneSAF Testbed DIS Periodic Updates From Simulation Cache Agents KB PDUs Translated To Facts (speed, location, unit type, etc.)

  4. High-Level Architecture of DBST PDUs OTB Agents actions PDUs inform, request, direct, approve, respond RFS, CFF mouse Puckster Interface BDE Interface text, forms, map

  5. TaskableAgents Architecture • Written in Java • TRL Knowledge Representation Language • - For Capturing Procedural Knowledge (Tasks & Methods) • APTE Method Selection-Algorithm • - responsible for building, maintaining, and repairing task-decomposition trees • Inference Engine JARE • - Java Automated Reasoning Engine • - Knowledge Base with Facts and Horn Clauses • - back-chaining (like Prolog) • - Updating World With Facts

  6. OtherAgents TaskableAgents TaskableAgents TRL Task Decomposition Hierarchy assert, query, retract APTE Algorithm TRL KB: tasks & methods JARE KB: facts & Horn-clauses Process Nets results messages sensing operators messages OTB (simulation)

  7. Task Representation Language (TRL) • Provides descriptors for: goals, tasks, methods, and operators • Tasks: “what to do” • Can associate alternative methods, with priorities or preference conditions • Can have termination conditions • Methods: “how to do it” • Can define preference conditions for alternatives • Process Net • - Procedural language for specifying how to do things • - While loops, if conditionals, sequential, parallel constructs • - Can invoke sub-tasks or operators • - Semantics based on Dynamic Logic • Operators: lowest-level actions that can be directly executed in the simulation environment, e.g. move unit, send message, fire on enemy • Each descriptor is a schema with arguments and variables • Conditions are evaluated as queries to JARE

  8. Example TRL Knowledge (:Task Monitor (?unit) (:Term-cond (destroyed ?unit)) (:Method (Track-with-UAV ?unit) (:Pref-cond (not (weather cloudy)))) (:Method (Follow-with-scouts ?unit) (:Pref-cond (ground-cover dense)))) (:Method Track-with-UAV (?unit) (:Pre-cond (have-assets UAV)) (:Process (:seq (:if(:cond(not(launched UAV)))(launch UAV)) (:let((x y)(loc ?unit ?x ?y))(fly UAV ?x ?y)) (circle UAV ?x ?y))))

  9. useful for describing multiple ways of accomplishing tasks may encode preference conditions APTE algorithm will automatically try another if one method fails examples: use of UAV vs. ATK helicopters vs. scouts for recon suppression of direct fire with Arty/CAS use of FASCAM to slow or re-direct advancing enemy maintaining security: flank guard, patrols neighboring units use of terrain features electronic surveillance Alternative Methods

  10. Task-Decomposition Hierarchy level 1 T1 level 2 M1 T3 level 3 T2 T5 T4 level 4 M7 M12 M92 M60 level 5 T40 T15 T18 T40 T45 T2 C T45 Tx =Task Mx = Method C = Condition

  11. 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

  12. S2 Agent and Interactions Enemy info BDE/DIV Sensors/ Recon BDE S2 RFI/RFS SALT/ INTSUM intel intel Move to OP S2 Scout spot reports Threat level, PIRs CDR S3 CCIR DP approval

  13. Vignette 2 – Decision Point 1 [Shift Main Effort] 1 CD 1 CD 4ID X X 4ID X X TmA TmB TmA CoC TmB CoC Company Size forces Company Size forces IB AA5 AA5 1-235 234 3-234 3 235 1 IB 234 1 1-234 Company Size forces AA5a AA5a AA3 AA4 AA3 AA4 AA5c PL PL AA6c Main Effort(ME) ME Switch Main Effort(ME) 3-66=1-22 3-66=1-22 PL PL • 2 Companies of 234 heading along AA3 & AA4 • 3-234 lead Bn of 234 Regt intent is unclear. • Lead Bn (1-235) of 235 Regt on AA5a • 2 Companies of 3-234 heading along AA3 • 3-234 lead Bn of 234 Regt. • Situation unclear on AA5 INTEL INTEL DP1 DP1 Variation A: Enemy major threat is on main route (AA) as in route (planned AA). Variation B: Enemy major threat changes to secondary approach Shift ME from Tm B to Co C? Shift ME from Tm B to Co C? Y N Y N

  14. Vignette 3 – Decision Point 2 [Commit TF Reserve] 1 CD 1 CD 4ID X X 4ID X X … Res TmA CoC TmB TmB TmA CoC Company Size forces Company Size forces AA5 IB AA5 234 3-234 3 235 1-235 1 IB 234 1 1-234 Company Size forces AA5a AA5a AA3 AA4 AA3 AA4 Blocking Positions AA5c TAI PL Y PL AA6c Main Effort(ME) (-) Main Effort(ME) 3-66=1-22 3-66=1-22 (+) PL PL … Res • 2 Companies of 234 heading down AA3 • Uniform pressure on AA’s 4 & 5ar. • Calculations indicate Tm A unit can move • to PL Y prior to lead of enemy unit. • Company units of 3 different Bns on all 3 AAs • Estimate enemy will reach PL Y at same time • 238 Regt lead units not committed INTEL INTEL DP2 DP2 Variation A: Heavy enemy threat across across entire sector. Commit the TF Reserve platoon? Commit the TF Reserve platoon? Variation B: Major enemy movement on one avenue (AA). Y N Y N Go to DP 3

  15. TOC is more than just collection of staff members helping/backup behavior, information fusion, resource sharing, and joint decision-making how to model collaborative behavior? CAST (extension to TaskableAgents) adds features to TRL language for encoding team structure and process (MALLET) adds algorithms for coordination and communication within teams semantics based on joint intention theory and mutual awareness (beliefs) Modeling Teamwork

  16. Various components static: structure of the team, communication policies... goals and plans dynamic: current situation, others’ workloads/status Team knowledge needed by agent team members: roles, responsibilities, capabilities, team plans need to know who should act and when need to reason about each other need to know when to communicate for synchronization, coordination, disambiguation, infomation sharing, etc. Key Concept: Shared Mental Models

  17. MALLET - team knowledge repres. language team structure (roles, capabilities, responsibilities) team process (plans, policies) CAST kernel (interpreter) convert to Petri nets (track progress, select actions) use back-chaining theorem-prover for inference dynamic role selection - make choices in context DIARG - information exchange algorithm proactive: offer new info to those who need it primary references: (Yen et al., IJCAI, 2001), (Yin et al., Autonomous Agents Conf., 2000) The CAST Agent Architecture

  18. extension of TRL basic definitions (team search-and-rescue (bill ted)) (role pilot) (role spotter) (plays-role bill pilot) (capable spotter use-IR-binoculars) conditions: (<predicate>*) with variables prefixed by ‘?’ e.g. ((forward-scout ?unit) (location ?unit ?x ?y)) team operators: (team-oper lift-heavy-object (?obj) (pre-cond (at ?obj) (num-agents >= 2)) (share-type AND))) share types: AND=together, OR=any, XOR=only 1 (excl.) MALLET Multi-Agent Logical Language for Encoding Teamwork

  19. team plans: can select certain agents or roles to do steps (like SharedPlans of Kraus and Grosz) (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)))))) “compile” these into TRL using methods of Biggers and Ioerger (2001) other scouts can take over as backup in case of failure of ?s responsibilities (such as monitoring, reporting); semantics similar to joint intentions (Johnson and Ioerger, 2001)

  20. compile team plans into Petri nets (expand sub-tasks) cycle: sense/decide/act loop 1. update beliefs about environment in self’s KB 2. check for any incoming messages from other agents 3. find active steps in plan (transitions with tokens in all input places) 4. if self is uniquely resp., consider executing oper. 5. if oper is XOR and resp. is ambiguous, offer 6. if oper is AND, broadcast READY and wait for others 7. randomly choose among remaining actions and execute 8. inform others of completed steps Dynamic Role Selection (DRS) check role definitions, must satisfy any constraints, capable? communicate when ambiguity exists sync. for AND operators; select for XOR operators could also allow individuals to vote/negotiate CAST Kernel

  21. Info. sharing is a key to flexible teamwork Want to capture information flow in team, including proactive distribution of information Want to restrict to only the most relevant cases Ideal criterion: (Bel A I) ^ (Bel A (Bel B I)) ^ (Bel A (Goal B G) ^ [(Bel B I)   (Done B G)] ^ [(Bel B I)    (Done B G)]  (Goal A (Inform B I)) where  is the temporal operator for ‘always’ DIARGDynamic Inter-Agent Rule Generator

  22. Explanation - A should send message I to B iff: A believes I is true A believes B does not believe I (or believes it is false) I is relevant to one of B’s goals i.e. pre-cond of current action that B is resp. for in team plan, and that action would not succeed without knowing the info. Reasoning about observability agents can sometimes infer that other team members already believe certain information e.g. based on common observability in environment use this to filter out superfluous messages recent work: (Rozich and Ioerger, submitted) DIARG, continued

  23. Need for tactical decision-making more flexibility in unplanned situation commander agent How to represent of “tactics”? battlefield geometry, relative force strength, combined arms theory terrain, effects on mobility discovery of enemy intent Command and Control

  24. NDM (Klein) - a cognitive model of human decision-making in complex environments based heavily on situation assessment (SA) 3 stages (Endsley): acquisition of factual information comprehension (abstraction, relevance, goal impact) projection (prediction of consequences) NDM is “satisficing” take first adequate match; don’t extensively evaluate and compare alternatives; respond Naturalistic Decision-Making

  25. verbal protocol analyses characterize types of utterances and interactions representative studies Serfaty, Entin, et al. (NDM, 1997) expertise as independent variable Pascual and Henderson (NDM, 1997) reliance on recall from experience Schmitt and Klein (CCRTS, 1999) recognitional processes in MDMP/COA Endsley (ARL report) information flow in infantry platoon/urban combat lots of other similar C2 environments... CIC/AAW, AWACS, fire fighting, ATC Evidence for NDM in TOCs

  26. a model of NDM characterized by “feature-matching” look for enough cues to trigger recognition features could be weighted for each situation, there is a typical response (doctrinal, or learned from experience) role of “mental simulation”? Recognition-Primed Decision Making (RPD)

  27. Len Adelman and Denny Leedom integrate RPD within battalion TOC staff RPD Flow Chart monitor progress current mission plan generate response, mental sim, compat. test, modify plan ... Yes situation clear? modify No generate new options status quo acceptable? No Yes reduce uncertainty

  28. often features are unknown, can’t evaluate options include: 1. suppress uncertainty 2. make default assumptions 3. confirmation bias (expectations) 4. take “probing” actions 5. forestalling until situation is more clear Methods to Deal With Uncertainty

  29. task DetectSituation runs in parallel with other routines... loop until match enough features for one situation while some features are unknown, try various methods to findout (e.g. UAV, scouts, radar, JSTARS, Bde Int, feint...) drive information collection to discriminate situations trigger plans for response maneuvers once situation is determined (takes priority) go back to original mission once threat is handled all of this can be implemented as tasks and methods in TRL Implementation of RPDin TaskableAgents

  30. Situations must have lists of typical features Characterize by: location of nearby enemy, combat strength in regions relative to local axis (frame of ref.) distance, speed, direction intent? (e.g. attacking, bypassing, objective) cover, uncertainty effect of terrain on mobility/reachability roads, mountains, streams, bridges, marshes, forests also minefields, targeted areas of interest... Characterization of Situations

  31. Defensive getting: flanked, ambushed, enveloped, etc. response: shift, request for support, withdraw... over-whelming enemy maneuver/main effort response: impede, divert, CAS, inform Bde... forked maneuvers (intent?); bypass attempt Offensive (opportunities worth recognizing) exploit gaps, isolate enemy units, envelopment, use fixing force + flank attack, canalize enemy, bypass Types of Situations

  32. Managing priorities when to abandon mission plan & react tactically? return to mission plan once done? Dependence of response on goals ROE, aggression/initiative vs. defense/security should SA involve impact on goals? (= threat?) Need to have “critics” to revise responses avoid enemy TAI’s, minefields, contact... stay near adjacent friendly units, air defense... Practical Issues

  33. TaskableAgents architecture CAST extensions for teamwork TOC staff agent model modeling command and control (on-going) HLA interoperability (on-going) Conclusion

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