1. Modeling Teamwork in Multi-Agent Systems: The CAST Architecture Dr. Thomas Ioerger, Jianwen Yin, and Michael Miller Computer Science, Texas A&M University March 21, 2001

2. Agenda Teamwork CAST MALLET DIARG Dynamic Role Selection Conclusions

3. What is a Team? Definition of a Team –A group of entities that are working together to achieve a shared (or common) goal Team needs –Coordination –Sharing of information –Distributed decision-making Focus of (human) teamwork optimizations –Situational awareness –Communications efficiency –Effectiveness of group decision-making

4. Previous work on Teamwork Multi-agent teams –Cohen & Levesque’s joint intention theory –Planning and plan specification/recognition –Team-oriented programming –STEAM based on SOAR by Tambe based on joint-intentions –Grosz’s Shared Plans Psy. Research on Human Team-Training –Shared mental models

5. Issues in Multi-Agent Team Training System Design How to represent a team? (Team ontology and MALLET) How to effectively reason about other team members’ roles and beliefs to collaborate? How to initiate communication? (DIARG)

6. Shared Mental Model Team structure: roles –responsibilities for individual steps in plans –mutual belief assumption Team process = team plan –use Petri Nets as an approximate finite and computable model of team process –use the token flow in Petri Nets to monitor and track the plan execution) start end A B C D

7. From teamwork to agent model A compositional multi-agent architecture

8. CAST (Collaborative Agents for Simulating Teamwork) Model effective teamwork by capturing team structures and teamwork process Enable agents in a team to have flexibility for adapting the team to changes in the environment

9. CAST Architecture Team Process and Structure Related Domain Knowledge in MALLET Petri Nets Reactive Action Rules and Agent Beliefs in JARE Interface Env. Simulator JARE Actor Java Implementation for Domain Related OPs CAST MALLET Parser and offline DIARG CAST Kernel Information Flows

10. MALLET (Multi-Agent Logic Language for Encoding Teamwork) Basic Object Type Predicates Actions and Plans Responsibilities Capabilities Belief Communication

11. MALLET Specification Team plans –role specifications (with constraints) –process specification (team-plan make-dinner (?entrée) (role shopper ?X (has-money ?X)) (role cook ?Y ) (process (if (need-ingredients ?entree) (sequence (do ?X (go-to store)) (do ?X (buy-ingredients ?entrée))) (parallel (do ?Y (prepare ?entrée)) (do ?X set-table)) (forall ?Z (team-member ?Z) (do ?Z eat)) (do-all (clean-up dishes)))) // an `AND’ team-operator

12. Team Operators Joint actions & coop-mode –and: lifting a couch together –xor/or : hitting a volleyball Team operator –team, co-mode, arguments –precond, postcond

13. Responsibilities Responsibilities are relationships between a set of roles, a goal, and a stage, represented as Responsibility(,, stage) Redundant responsibilities: Or- responsibility(,, stage) Shared competitive responsibilities: Xor- responsibility(,, stage) Shared complementary responsibilities: And- responsibility(,, stage)

14. Capabilities Capabilities are relationships between roles and actions, which is represented as Capability(, ) Backup capability: Or- capability(, ) Shared competitive capability: Xor- capability(, ) Shared conflicting capability: And- capability(, )

15. Three Major Algorithms Petri Net generation algorithm DIARG Dynamic role selection

16. Petri Nets place/transition Petri nets vs. PrT nets –propositional  first-order predicate PrT nets vs. logic programs –happy(x)  cat(x), purrs(x). –Concurrence –Firing rule vs. meta-predicates

17. Petri Net Generation Sequential process Parallel process sub- net startend start sub- net end

18. Petri Net Generation (Cont.) If process start sub-net end Then cond else cond

19. DIARG (Dynamic Inter-Agent Rule Generator) Proactive information exchange –inferring what might be needed for others to complete their (current) tasks –Proactively provide timely information –Reduce communication overhead Examines needers and providers of information in order to allow dynamic generation of information flow

20. Dynamic Role Selection Whenever there is a choice on who performs an action (i.e.role redundancy) –Postpone role selection until reach the individual plan or individual operator level Might need to communicate to… –resolve ambiguities of responsibility –synchronize for joint actions Advantages –Provide flexibility in team formation –Others can play “backup” role, in case of failure –Improve teamwork efficiency, Balance workload

21. Future Work Simulating more dynamic teams –handling failures, load-balancing, delegation, unreliable agents Handling more types of communication –resolving conflicting information –situation assessment (cognitive models) Reasoning about agent capabilities Goal regression and planning User-modeling: inferring state/beliefs/intent from monitoring actions

22. Conclusions First version (CAST 1.0) running now –see demo Target domains: Team Training –AWACS –Fire Fighting –Army Brigade Operations Staff –NASA Flight Controllers

23. Acknowledgements Dr. Richard Volz, Dr. John Yen, Dr. Dianxiang Xu Heejin Lim, Sen Cao, Yue Zhou, Xueqi Cheng, Colby Johnson