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Information Sharing for Distributed Planning

Information Sharing for Distributed Planning. Prasanna Velagapudi. Large Heterogeneous Teams. 100s to 1000s of robots, agents, people Complex, collaborative tasks Dynamic, uncertain environment Joint planning intractable. Scaling Team Planning.

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Information Sharing for Distributed Planning

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  1. Information Sharing forDistributed Planning Prasanna Velagapudi AAMAS 2010 - Doctoral Symposium

  2. Large Heterogeneous Teams • 100s to 1000s of robots, agents, people • Complex, collaborative tasks • Dynamic, uncertain environment • Joint planning intractable AAMAS 2010 - Doctoral Symposium

  3. Scaling Team Planning • Independent planners: can’t account for teammates • Existing work: needs specific structure or doesn’t scale to these sizes • DPC, Prioritized Planning • JESP, Factored MDP, ND-POMDP AAMAS 2010 - Doctoral Symposium

  4. Iterated Distributed Planning • Factor the problem, enumerate interactions • Compute independent plans & potential interactions • Exchange messages about interactions • Use exchanged information, improve local model AAMAS 2010 - Doctoral Symposium

  5. Iterated Distributed Planning • Factor the problem, enumerate interactions • Compute independent plans & potential interactions • Exchange messages about interactions • Use exchanged information, improve local model ? AAMAS 2010 - Doctoral Symposium

  6. Iterated Distributed Planning • Factor the problem, enumerate interactions • Compute independent plans & potential interactions • Exchange messages about interactions • Use exchanged information, improve local model ? AAMAS 2010 - Doctoral Symposium

  7. Iterated Distributed Planning • Factor the problem, enumerate interactions • Compute independent plans & potential interactions • Exchange messages about interactions • Use exchanged information, improve local model AAMAS 2010 - Doctoral Symposium

  8. A Tale of Two Distributed Planners Distributed Prioritized Planning (DPP) L-TREMOR AAMAS 2010 - Doctoral Symposium

  9. Distributed Prioritized Planning AAMAS 2010 - Doctoral Symposium

  10. Multiagent Path Planning Start Goal AAMAS 2010 - Doctoral Symposium

  11. Multiagent Path Planning AAMAS 2010 - Doctoral Symposium

  12. Prioritized Planning • Assign priorities to agents based on path length [van den Berg, et al 2005] AAMAS 2010 - Doctoral Symposium

  13. Prioritized Planning • Plan from highest priority to lowest priority • Use previous agents as dynamic obstacles [van den Berg, et al 2005] AAMAS 2010 - Doctoral Symposium

  14. Distributed Prioritized Planning Parallelizable & Equivalent AAMAS 2010 - Doctoral Symposium

  15. Large-Scale Path Solutions AAMAS 2010 - Doctoral Symposium

  16. Large-Scale Path Solutions AAMAS 2010 - Doctoral Symposium

  17. DPP Results Fewer Sequential Plans AAMAS 2010 - Doctoral Symposium

  18. DPP Results Fewer Sequential Plans Longer Planning Time AAMAS 2010 - Doctoral Symposium

  19. Why does this happen? • Prioritized Planning • DPP Longest planning agents might replan multiple times A A B B C C Individual agent planning times varied by >2 orders of magnitude D D Solution 1: Prioritize by plan time? Solution 2: Incremental Planning AAMAS 2010 - Doctoral Symposium

  20. Summary of DPP • Observable, certain world • Only one type of interaction: collision • Far fewer sequential planning iterations • Incremental planning may reduce execution time AAMAS 2010 - Doctoral Symposium

  21. L-TREMOR AAMAS 2010 - Doctoral Symposium

  22. A Simple Rescue Domain Unsafe Cell Rescue Agent Clearable Debris Narrow Corridor Victim Cleaner Agent AAMAS 2010 - Doctoral Symposium

  23. A Simple (Large) Rescue Domain AAMAS 2010 - Doctoral Symposium

  24. Distributed POMDP with Coordination Locales (DPCL) • Often, interactions between agents are sparse Only fits one agent Passable if cleaned [Varakantham, et al 2009] AAMAS 2010 - Doctoral Symposium

  25. Distributed POMDP with Coordination Locales (DPCL) • Define coordination locales (CLs) where POMDP model functions are not independent: <S, A, Ω, P, R, O> (states) (actions) (obs.) (transition)(reward)(obs. fn) [Varakantham, et al 2009] AAMAS 2010 - Doctoral Symposium

  26. Distributed POMDP with Coordination Locales (DPCL) • Define coordination locales (CLs) where POMDP model functions are not independent: Outside CL: (typical) Sglobal R1, P1, O1 R2, P2, O2 S1, A1 S2, A2 [Varakantham, et al 2009] AAMAS 2010 - Doctoral Symposium

  27. Distributed POMDP with Coordination Locales (DPCL) • Define coordination locales (CLs) where POMDP model functions are not independent: Inside CL: (interaction) Sglobal R12, P12, O12 S1, A1 S2, A2 [Varakantham, et al 2009] AAMAS 2010 - Doctoral Symposium

  28. TREMOR [Varakantham, et al 2009] AAMAS 2010 - Doctoral Symposium

  29. L-TREMOR Distributed & Parallelizable AAMAS 2010 - Doctoral Symposium

  30. Preliminary Results – Joint Utility N = 100 (structurally similar to N=10) N = 6 N = 10 AAMAS 2010 - Doctoral Symposium

  31. Preliminary Results – Timing AAMAS 2010 - Doctoral Symposium

  32. Preliminary Results – Model Accuracy R = 0.804 AAMAS 2010 - Doctoral Symposium

  33. Current Issues • Oscillations in solutions • Discovery of relevant locales ? AAMAS 2010 - Doctoral Symposium

  34. Summary of L-TREMOR • Partially-observable, uncertain world • Multiple types of interactions • Role-allocation of tasks • Improvement over independent planning • Handles large problems • Next steps: improving convergence AAMAS 2010 - Doctoral Symposium

  35. Conclusions • Two approaches to distributed planning • DPP: approaching centralized performance • L-TREMOR: exceeding joint tractability • Analogous strategies for distributing planning • Both iterate independent planners • Both exchange messages about states, actions AAMAS 2010 - Doctoral Symposium

  36. Future Work • Generalized framework for distributed planning through iterative message exchange • Reduce necessary communication • Better search over task allocations • Scaling to larger team sizes AAMAS 2010 - Doctoral Symposium

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