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Information Sharing in Large Heterogeneous Teams

Information Sharing in Large Heterogeneous Teams. Prasanna Velagapudi Robotics Institute Carnegie Mellon University. Large Heterogeneous Teams. 100s to 1000s of agents (robots, agents, people) Shared goals Must collaborate to complete complex tasks Dynamic, uncertain environment.

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Information Sharing in Large Heterogeneous Teams

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  1. Information Sharing in Large Heterogeneous Teams Prasanna Velagapudi Robotics Institute Carnegie Mellon University FRC Seminar - August 13, 2009

  2. Large Heterogeneous Teams • 100s to 1000s of agents (robots, agents, people) • Shared goals • Must collaborate to complete complex tasks • Dynamic, uncertain environment FRC Seminar - August 13, 2009

  3. Scaling Teams • Far more data than can be feasibly shared • Amount of information exchanged often grows faster than amount of available bandwidth • Vague, incomplete knowledge of large parts of the team • Often not important • Shared information improves team performance

  4. Search and Rescue • Air robots, ground robots, human operators • Each is generating information • Humans  Classify objects and issue commands • Robots  Explore and map area • Geometric Random Graph FRC Seminar - August 13, 2009

  5. Search and Rescue Video Streams (320kbps x 24, For operators) Operator Control (<1kbps x 24, For robots) Decentralized Evidence Grid (14kbps x 24, For all agents) O(n2) O(n2) Available throughput: Θ(WN0.5) [Gupta 2000] FRC Seminar - August 13, 2009

  6. Available Network Technologies Source: William Webb - Ofcom FRC Seminar - August 13, 2009

  7. Scaling Teams • We need to deliver information efficiently • Get to the agents that can make use of it most • Don’t waste communication bandwidth • Key Idea: Different agents have different needs for a given piece of information

  8. Sharing information • When information generation exceeds network capacity, there are a few options: • Compression/Fusion (Eliminate redundant data) • Structuring (Eliminate overhead costs) • Selection (Eliminate unimportant data) FRC Seminar - August 13, 2009

  9. Related work • Distributed Data Fusion • Channel filtering (DDF) [Makarenko 04] • Particle exchange [Rosencrantz 03] • Networking • Gossip[Haas 06], SPIN[Heinzelman 99], IDR[Liu 03] • Multiagent Coordination • STEAM [Tambe 97] • ACE-PJB-COMM [Roth 05], Reward-shaping [Williamson 09], dec-POMDP-com [Zilberstein 03] FRC Seminar - August 13, 2009

  10. Domain assumptions • Information generated dynamically and asynchronously • Limited bandwidth and memory • With respect to size of team • Significant local computing • Some predictive knowledge about other agents’ information needs • Peer-to-peer communications FRC Seminar - August 13, 2009

  11. Domain assumptions Inconsistency Tokens Our domains Particle Exchange Gossip Channel Filter Reward Shaping STEAM SPIN, IDR ACE-PJB-COMM dec-POMDP-com Flooding Complexity Communication FRC Seminar - August 13, 2009

  12. Abstract Problem • Suppose we are given some metric for team performance in a domain: • How much information sharing complexity and communication is necessary to achieve good performance in a large team? • How can we characterize the effects of information sharing on performance in large teams? • Suppose we are given some metric for team performance in a domain: • How much information sharing complexity and communication is necessary to achieve good performance in a large team? • How can we characterize the effects of information sharing on performance in large teams? FRC Seminar - August 13, 2009

  13. A simple example • Two robots (1 static, 1 mobile) in a maze • Limited sensing radius, global communication • Team task: Get mobile robot to goal point • Team performance = battery power • Movement and communication use power • How useful is it to the teamfor the static robot to share its info with the mobile robot? FRC Seminar - August 13, 2009

  14. A simple example FRC Seminar - August 13, 2009

  15. A simple example • Without information • With information FRC Seminar - August 13, 2009

  16. A simple example • Without information • With information The change in path cost is the “utility” of this information FRC Seminar - August 13, 2009

  17. Utility of Information • Utility: the change in team performance when an agent gets a piece of information • Often dependent on other information • Difficult to calculate during execution, even with complete real-time knowledge • Need to know final state of team FRC Seminar - August 13, 2009

  18. Objective • Utility: the change in team performance when an agent gets a piece of information • Communication cost: the cost of sending a piece of information to a specific agent FRC Seminar - August 13, 2009

  19. Objective • Maximize team performance: utility communication agents info. source dissemination tree In actual systems, this solution must be formed through local decisions! FRC Seminar - August 13, 2009

  20. Distributions of Utility • For large amounts of information, consider the distribution of utility • May be conditioned on known data, or just independently sampled • Characterize domains as having specific distributions of utility • Estimate performance of various algorithms as function of this distribution FRC Seminar - August 13, 2009

  21. Back to the simple example Maze Utility Distribution Frequency Utility (Δ path cost) FRC Seminar - August 13, 2009

  22. Abstract Problem • Suppose we are given some metric for team performance in a domain: • How much information sharing complexity and communication is necessary to achieve good performance in a large team? • How can we characterize the effects of information sharing on performance in large teams? FRC Seminar - August 13, 2009

  23. Approach • Useful information sharing algorithms fall between two extremes: • Full knowledge/high complexity (omniscient) • No knowledge/low complexity (blind) • Observe performance of two extremes of information sharing algorithms • Learn when it is useful to use complex algorithms • If blind policies do well, other low complexity algorithms will also work well FRC Seminar - August 13, 2009

  24. Utility vs. Communication Distributional upper bound Omniscient policy Team Utility Efficient policies Blind policy Communication Cost FRC Seminar - August 13, 2009

  25. Expected Upper Bound • Order statistic: expectation of k-th highest value over n samples • Computable for many common distributions • Expected best case performance • What values of utility would we expect to see in a team of n agents? • Sum of k highest order statistics FRC Seminar - August 13, 2009

  26. Utility vs. Communication Distributional upper bound Omniscient policy Team Utility Blind policy Efficient policies Communication Cost FRC Seminar - August 13, 2009

  27. Omniscient Policy • Lookahead policy • Assume we are given estimate of utility for every other node (possibly with noise) • Exhaustively search all n-length paths from current node • Send information along best path • Repeat until TTL reaches 0 • Approximation of best omniscient policy • Full exhaustive search is intractable FRC Seminar - August 13, 2009

  28. Utility vs. Communication Distributional upper bound Omniscient policy Team Utility Blind policy Efficient policies Communication Cost FRC Seminar - August 13, 2009

  29. Blind policies • Random: “Gossip” to randomly chosen neighbor • Random Self-Avoiding • Keep history of agents visited • O(lifetime of piece) • Random Trail • Keep history of links used • O(# of pieces/time step) FRC Seminar - August 13, 2009

  30. Questions • How well does the lookahead policy approximate omniscient policy performance? • How wide is the performance gap between the omniscient policy and blind policies? • How does team size affect performance? • Is omniscient policy performance better because it knows where to route, or where not to route? FRC Seminar - August 13, 2009

  31. Experiment • Network of agents with utility sampled from distribution • Single piece of information shared each trial • Average-case performance recorded • Distributions: • Normal • Exponential • Uniform • Networks: • Small-Worlds (Watts-Beta) • Scale-free (Preferential attachment) • Lattice (2D grid) • Hierarchy (Spanning tree) FRC Seminar - August 13, 2009

  32. Questions • How well does the lookahead policy approximate omniscient policy performance? • How wide is the performance gap between the omniscient policy and blind policies? • How does team size affect performance? • Is omniscient policy performance better because it knows where to route, or where not to route? FRC Seminar - August 13, 2009

  33. Lookahead convergence 2-step lookahead: pathological case? FRC Seminar - August 13, 2009

  34. Questions • How well does the lookahead policy approximate omniscient policy performance? • How wide is the performance gap between the omniscient policy and blind policies? • How does team size affect performance? • Is omniscient policy performance better because it knows where to route, or where not to route? FRC Seminar - August 13, 2009

  35. Performance Results Normal Distribution Exponential Distribution FRC Seminar - August 13, 2009

  36. Policy Performance (Utility sampled from Exponential distribution) FRC Seminar - August 13, 2009

  37. Utility of knowledge ~120 communications FRC Seminar - August 13, 2009

  38. Questions • How well does the lookahead policy approximate omniscient policy performance? • How wide is the performance gap between the omniscient policy and blind policies? • How does team size affect performance? • Is omniscient policy performance better because it knows where to route, or where not to route? FRC Seminar - August 13, 2009

  39. Scaling effects The costs of maintaining utility estimates for Lookahead increase with team size, but the costs of Random policy do not. FRC Seminar - August 13, 2009

  40. Questions • How well does the lookahead policy approximate omniscient policy performance? • How wide is the performance gap between the omniscient policy and blind policies? • How does team size affect performance? • Is omniscient policy performance better because it knows where to route, or where not to route? FRC Seminar - August 13, 2009

  41. Noisy estimation • How does the omniscient policy degrade as its estimates of utility become noisy? • As noise increases, the omniscient policy approaches an ideal blind policy • Gaussian noise scaled by network distance: FRC Seminar - August 13, 2009

  42. Noisy estimation FRC Seminar - August 13, 2009

  43. Modeling maze navigation Frequency Utility (Δ path cost) FRC Seminar - August 13, 2009

  44. Modeling maze navigation FRC Seminar - August 13, 2009

  45. Summary of Results • Omniscient policy approaches optimal routing on many graphs (not hierarchies) • Gap between omniscient and blind policies is small when: • Network is conducive (Small Worlds, Lattice) • Maintaining shared knowledge is expensive • Network is massive • Estimation of value is poor FRC Seminar - August 13, 2009

  46. Improving the model • Current work on validating this model • USARSim (Search and Rescue) • VBS2 (Military C2) • TREMOR (POMDP) • Predictive utility estimation and dynamics • Better solution for optimal policy: • Prize-collecting Steiner Tree [Ljubić 2007] FRC Seminar - August 13, 2009

  47. Conclusions • Utility distributions: a mechanism to test information sharing performance • Computable from real-world data • Can be conditional/joint/marginal to encode domain dependencies • Simple random policies: surprisingly competitive in many cases • No structural or computational overhead • No expensive costs to maintain utility estimates FRC Seminar - August 13, 2009

  48. Questions? FRC Seminar - August 13, 2009

  49. FRC Seminar - August 13, 2009

  50. Outline • What we mean by large heterogeneous teams • The common assumptions in our domains • What we mean by utility  utility distributions • The experiment • The results • Conclusions • Future work/validation FRC Seminar - August 13, 2009

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