1. The DEFACTO System: Training Incident Commanders Nathan Schurr Janusz Marecki, Milind Tambe, Nikhil Kasinadhuni, and J. P. Lewis University of Southern California Paul Scerri Carnegie Mellon University

2. Outline Motivation and Domain DEFACTO Team Level Adjustable Autonomy Experiments with DEFACTO Conclusions

3. Motivation: Help Incident Commanders Incident Commander First Response Disaster Rescue Scenario  Urban Environment  Large Scale  Crime Scene Incident commander must control situation, monitor situation, and allocate resources Goal: Initially a Training Simulation  Later: Decision Support/Replacement

4. LAFD Exercise: Simulations by People Playing Roles

5. Aims of DEFACTO LAFD Exercise Challenges  Personnel Heavy  Smaller Scale  Low Fidelity Environment Key Exercise Components  Communication  Allocation Agent-teams replace people playing roles Demonstrating Effective Flexible Agent Coordination of Teams via Omnipresence

6. Outline Motivation and Domain DEFACTO Team Level Adjustable Autonomy Experiments with DEFACTO Conclusions

7. DEFACTO Architecture

8. Disaster Rescue Simulation: USC Map, Different underlying simulators Statistics

9. Challenges in Extending to Human-Agent Teams Teamwork  Communication  Role Allocation Agent team to incorporate human  Adjustable Autonomy (Scerri et al JAIR 2002)  Interface

10. DEFACTO Teamwork Proxies  Machinetta Continued development with CMU Used in many other domains – UAVs, sensor nets etc. Flexible Interaction  Team Level Adjustable Autonomy Strategies  Dynamic Strategy Selection Omni-Viewer  2D – Standard with Simulator  3D – Developed by us  Interaction

11. DEFACTO Architecture

12. RAP Other State Communication Coordination RAP Interface Adjustable Autonomy Proxies Abstracted Theories of Teamwork (Scerri et al AAMAS 03) Communication: communication with other proxies Coordination: reasoning about team plans and communication State: the working memory of the proxy Adjustable Autonomy: reasoning about whether to act autonomously or pass control to the team member RAP Interface: communication with the team member Proxy Architecture

13. Teamwork Proxies Higher level TOP Reuse across domain Flexible Teamwork (Tambe JAIR 97) Communication  Joint Intentions (Cohen & Levesque 1991) Allocation  Role allocation algorithms (Xu et al AAMAS 2005) Machinetta  Platform Independent  Modular Structure  Downloadable – Free, Publicly available

14. Outline Motivation and Domain DEFACTO Team Level Adjustable Autonomy Experiments with DEFACTO Conclusions

15. Adjustable Autonomy(AA) Strategies for Teams Agents dynamically adjust own level of autonomy  Agents act autonomously, but also...  Give up autonomy, transferring control to humans When to transfer decision-making control  Whenever human has superior expertise  Yet, too many interrupts also problematic  Previous: Individual agent-human interaction

16. AA: Novel Challenges in Teams Transfer of control strategies for AA in teams  Planned sequence of transfers of control A T - Team level A strategy H - Human strategy for all tasks AH - Individual A followed by H A T H - Team level A strategy followed by H Goal: Improve Team Performance

17. DEFACTO Architecture

18. Omni-Viewer

19. DEFACTO Movie

20. Outline Motivation and Domain DEFACTO Team Level Adjustable Autonomy Experiments with DEFACTO Conclusions

21. Experiments Initial evaluation of system and of strategies Details  3 Subjects  Allocation Viewer  Same Map for each scenario Building size and location Initial position of fires  4, 6, and 10 agents  A, H, AH, A T H Strategies  Averaged over 3 runs

22. Empirical Studies with Users

23. Conclusions from Results No strategy dominates through all cases Humans may sometimes degrade agent team results Slope of strategy A > Slope of H  Humans are not as good at exploiting additional agents resources If EQ H is low, then as we grow to larger numbers of agents, A will dominate AH, A T H and H Dip at 6… LAFD – “Not surprising.”

24. Summary DEFACTO  Teamwork  Team Level Adjustable Autonomy Strategies  Interface Experimented with strategies for adjustable autonomy Future Directions  Experiments with LAFD  Study strategy behavior  Train the “system” Training today, real response in the future.

25. Future

26. Thank You Email: schurr@usc.eduschurr@usc.edu Web Site: http://teamcore.usc.eduhttp://teamcore.usc.edu Machinetta  http://teamcore.usc.edu/doc/Machinetta/ http://teamcore.usc.edu/doc/Machinetta/ Thanks  CREATE Center  Fred Pighin and Pratik Patil

27. Related Work: Disaster Response Simulations LA County Fire Department Simulators  DEFACTO focuses on “incident commander” “Environment” simulators:  E.g., Terrasim, EPICS  Not provide on agent behaviors “Agent-based” simulators  E.g., Battlefield simulators  Adjustable autonomy

28. Strategy Models

29. Models of the Strategies

31. Outline Motivation  Objectives  CREATE Research Center  Current State of the Art DEFACTO  Simulator  Teamwork Proxies  3D Visualization Team Level Adjustable Autonomy  Models  Predictions Experiments with DEFACTO Conclusions

32. DEFACTO: Key Research Areas Enable effective interactions of agents with humans  Research: Adjustable autonomy  Previous work: Often single agent-single human interactions Scale-up to 100s of agents with fire engines, ambulances, police  Research: Scale-up in team coordination  Previous work: Limited numbers of agents coordinating in teams Visualization  Robust 3D visualization

33. Adjustable Autonomy: Novel Challenges in Teams Previous transfer-of-control fails in teams:  Ignore costs to team (just concerned about individual)  One shot transfers of control, too rigid Transfer control to a human (H) or agent (A)  If human fails to make a decision, miscoordination!!  Forcing agent to decide can cause a poor decision Expensive lesson learned in the “Electric-Elves” project  Major errors by software assistants  Hence need more flexible transfer of control

34. Predictions EQh: Expected quality of human decision AG H : How many agents human can control A Strategy has constant slope

35. CREATE Research Center Center for Risk and Economic Analysis of Terrorism Events MANPAD Scenario  Large Scale Disaster  Limited Resources First Response Help incident commander control situation  Large Scale  Crime Scene

36. Simulator  Robocup Rescue 10 different Simulators Multiple Agent Types

37. Team Level AA Model How to select the strategy among many? Key idea: Calculate expected utility of different strategies  Mathematical model of strategies EQ: Quality of an entity’s decision P: Probability of response of that entity W: Cost of miscoordination Traditional Expected Utility  Probability of response * decision quality Integrate over time

38. Agents Per Fire Subject ASubject B Subject C

39. LA City Fire Dept Exercise: Fire Progression Fire starts on 1 st floor Spreads to Attic

40. LAFD Exercise: Simulations by People Playing Roles LAFD officials simulate fire progression and the resource availability Battalion Chief allocates available resources to tasks

41. RAP Other State Communication Coordination RAP Interface Adjustable Autonomy Proxies Abstracted Theories of Teamwork (Machinetta) Platform Independent Modular Structure Proxy Architecture

42. DEFACTO Movie

43. Objectives: Agent-based Simulation Tools for Disaster Response Improve training and decision making Present  Teach and evaluate LAFD response tactics Future  Agent/Robot disaster response Key research questions in:  Multiagent coordination, Adjustable Autonomy  Visualization of multiagent systems