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CAST: Collaborative Agents for Simulating Teamwork

CAST: Collaborative Agents for Simulating Teamwork. John Yen, Jianwen Yin, Thomas R. Ioerger, Michael Miller, Dianxiang Xu, Richard Volz Department of Computer Science Texas A&M University. Acknowledgement: DoD/AFOSR MURI grant. Our Goal: Building Intelligent Team-Training Systems.

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CAST: Collaborative Agents for Simulating Teamwork

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  1. CAST: Collaborative Agentsfor Simulating Teamwork John Yen, Jianwen Yin, Thomas R. Ioerger, Michael Miller, Dianxiang Xu, Richard Volz Department of Computer Science Texas A&M University Acknowledgement: DoD/AFOSR MURI grant Texas A&M University

  2. Our Goal: Building IntelligentTeam-Training Systems • Not just better individual performance, but better coordination, load-balancing, situation awareness • Examples: fire fighters, air traffic controllers, TOCs, CICs, AWACS, shuttle mission control • Scenario-based training via distributed simulation • The need for agents - automated coaches and role players (virtual team members) • Agents must understand the team structure and teamwork process Texas A&M University

  3. Studies on Team Trainingfrom Psychology Literature • teamwork vs. taskwork (Salas & Cannon-Bowers) • process vs. outcome measures • communications frequency, type (Serfaty) • situation awareness (Endsley) • adaptiveness of team to workload • (also personality, leadership...) • training protocols like cross-training to learn other roles (Salas & Cannon-Bowers) Texas A&M University

  4. Roles for Agents in Team-Training • Role players/Virtual Team members • reduce cost of training (e.g. need for human role players) • need to know when to act and when to defer • need to know whom to coordinate with • need to know whom to share info with • must describe team task (plan) and role assignments • Automated Coaches • build student model (observe actions; infer their view) • compare to “expert” model (what should the trainee do ideally, given the team structure?) • give feedback, critique, instruction, more training... Texas A&M University

  5. Key Concept: Shared Mental Model • Various components • static: structure of the team, comm. policies... • goals and plans • dynamic: current situation, others’ workloads/status • Needed for virtual team members • not just “domain” knowledge • also roles, responsibilities, capabilities, team plans • need to know who should act and when • need to know when to communicate for sync. & coordination, disambiguation, infomation sharing, etc. Texas A&M University

  6. Shared Mental Models, continued • Needed for user modeling • how to interpret incorrect actions? • alternative explanations of observed failures of action: • thought it was another’s responsibility? • waiting for synchronization or approval? • meaning of shared responsibilities & delegation for backup behavior (important for robustness) Texas A&M University

  7. Related Work • STEAM (Tambe) • STEVE (Rickel & Johnson) • REACT (Hill & Johnson) • SWARMM/dMARS (Tidhar; Jennings) • lots of others... Texas A&M University

  8. The CAST Agent Architecture • MALLET - team KR language • team structure • team process • 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 Texas A&M University

  9. MALLET Multi-Agent Logical Language for Encoding Teamwork • syntax based on S-expressions (symbolic, nested lists) • basic definitions • (team <teamName> (<agentName>*)) • (role <roleName>) • (plays-role <agentName> <roleName>) • (capable <agentName> <operName>) • conditions: (<predicate>*) with variables prefixed by ‘?’ • e.g. ((forward-scout ?unit) (location ?unit ?x ?y)) • operators: • (indiv-oper <operName> (<var>*) (pre-cond <cond>) (effects <cond>)) • (team-oper <operName> (<var>*) (share-type [AND|OR|XOR]) (pre-cond <cond>) (effects <cond>)) Texas A&M University

  10. team plans: • (team-plan <planName> (<var>*) (pre-cond <cond>) (effects <cond>) (term-cond <cond> [SUCCESS|FAILURE]) [(role-select <var> (<roleName>*) (constraint <cond>))]* (process <process>)) • process expressions: • <process> ::= (seq [<call>]*) | (par [<call>]+) | (while <cond> do <process>) | (if <cond> then <process> else <process>) | (do <agentName>|<roleVar> <call>) where <call> ::= (<planName>|<operName> <arg>*) • semantics of responsibilities - similar to joint intentions (mutual belief) but asymmetric (Ioerger and Johnson, IC-AI 2001) Texas A&M University

  11. (team-plan T1 () (process (par (kill-wumpuses) (collect-gold)))) (team-plan kill-wumpuses () (role ?s (scout)) (role ?f (fighter)) (process (while ((wumpus ?x) (not (dead ?x)))) (seq (do ?s (find-wumpus ?x)) (do ?f (move-to-wumpus ?x)) (do ?f (shoot-wumpus ?x))))) (team-plan find-gold () (role ?c (carrier)) (process (while (true) (if (see ?any-agent glitter) (do ?c (carrier-pickup gold)))) wumpus exists start find move shoot done pickup glitter no wumpuses left Texas A&M University

  12. CAST Kernel • compile team plans into Petri nets (incl. expanding 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 Texas A&M University

  13. DIARGDynamic Inter-Agent Rule Generator • Info. sharing is a key to flexible teamwork • more generally: distributed SA • training target: learning what is relevant to whom? • Want to capture information flow in team, including proactive distribution of information • Want to restrict to only the most relevant cases (Tambe) • Ideal criteria: (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’ Texas A&M University

  14. DIARG, continued • 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. • Algorithm: 1. check for transitions which other agents are responsible for that can fire (pre-conds satisfied) 2. infer whether other agent might not believe pre-conds are true (currently, beliefs based on post-conditions of executed steps, i.e. tokens in output places) 3. send proactive message with information Texas A&M University

  15. Experimental Testbed • Wumpus World (Russell and Norvig) extended to teamwork environment • several agents exploring a 10x10 cave • goals: collect gold, kill wumpuses • assume variable # wumpuses, but stationary • Roles: • scouts/climbers - look for (smell for) wumpuses, can climb over pits • shooters/fighters - have bow to shoot wumpus (from adjacent room), must collect arrows • carriers - strong for carrying gold Texas A&M University

  16. Experiment 1 • Evaluate effect of teamwork and information exchange on team performance • Team A: CAST, using DRS and DIARG • Team B: CAST - no DIARG, just broadcast all new info. • Team C: no teamwork - agents wander randomly 138 Texas A&M University

  17. Experiment 2 • Effect of Dynamic Role Selection • constraint: choose closest fighter to wumpus • test scalability via: • increasing # of pits (in 10x10 cave) • makes it harder for agents to navigate Texas A&M University

  18. Future Work • Add delegation (role/resp change over time) • contract about communication between A and B (maintain mutual belief about goal status) • Info. exchange (DIARG) depends on frequency of change and observability • More complex model of agent capabilities • degrees of success (performance scores) • depends on workload, individual’s skill, deadlines, dual-tasks • affects team decision-making Texas A&M University

  19. Future Work, continued • Dynamic planning • generate plan if pre-conditions of current action are unsatisfied (instead of just waiting) • how will this impact team modeling? • Interaction with humans • must infer their view of the team’s progress • deception, untruthfulness • failures due to forgetting/overload • conflicts with private goals? Texas A&M University

  20. Conclusion • Introduced CAST agent architecture • MALLET team-representation language • Major algorithms: • dynamic role selection (in kernel) • proactive information exchange (in DIARG) • Simulation of a shared mental model • Working toward support of intelligent team-training systems (user-modeling coaches, and virtual team members) Texas A&M University

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