The DEFACTO System: Training Incident Commanders

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. Proxy Architecture • 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 Other RAP Communication RAP Interface Proxies State Coordination Adjustable Autonomy

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 • AT - Team level A strategy • H - Human strategy for all tasks • AH - Individual A followed by H • ATH - 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, ATH 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 EQH is low, then as we grow to larger numbers of agents, A will dominate AH, ATH 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.edu • Web Site: http://teamcore.usc.edu • 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

30. 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 • AGH: 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 A Subject B Subject C

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

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

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

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