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Analyzing the Performance of Randomized Information Sharing under Noise and Dynamics

Analyzing the Performance of Randomized Information Sharing under Noise and Dynamics. Paul Scerri , Prasanna Velagapudi , Katia Sycara Robotics Institute Carnegie Mellon University. Large Multiagent Teams. 1000s of robots, agents, and people Must collaborate to complete complex tasks

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Analyzing the Performance of Randomized Information Sharing under Noise and Dynamics

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  1. Analyzing the Performance of Randomized Information Sharing under Noise and Dynamics Paul Scerri, PrasannaVelagapudi, KatiaSycara Robotics Institute Carnegie Mellon University

  2. Large Multiagent Teams • 1000s of robots, agents, and people • Must collaborate to complete complex tasks • Necessitate distributed algorithms • Assuming peer-to-peer communication model Search and Rescue Disaster Response UAV Surveillance

  3. Information Sharing • How do we deliver information efficiently? • Get to the people that need it most • Don’t waste communication bandwidth • Key Idea: Different agents have different utility for a single piece of information!

  4. Information Sharing • How do we measure information need? • “Need” is domain-specific • Define a utility function for each agent which is maximized when it receives the information it needs

  5. Existing Approaches • Simple • Flooding • Gossip • Tokens • Intelligent • STEAM • Channel Filtering • Particle Filter exchange

  6. Classical Flooding • Agent pushes information to every neighbor Info Info Info Info Info

  7. Gossip • Agent pushes information probabilistically to subset of neighbors Info Info Info

  8. Random Token Routing • Agent pushes information to a single random neighbor Info

  9. Problem • When are intelligent strategies necessary? • Complexity adds overhead • In many simple domains, random policies work • Is there a set of problem characteristics that can predict algorithm performance?

  10. “Optimal” performance • Simplest case: • Single piece of information • Static network • Optimal algorithm for a fully connected network: • Use first transmission to get to agent with the highest utility for the information • Use second transmission to get to agent with second highest utility, etc. [Velagapudi et al., AAMAS 2009]

  11. “Optimal” performance • Suppose distribution of utility over network can be approximated by a well-known distribution • Expected utility of the optimal algorithm for k transmissions is sum of k highest order statistics • Forms upper bound on performance for partially connected networks with same utility distribution [Velagapudi et al., AAMAS 2009]

  12. “Optimal” performance • In partially connected networks, analytic expression for optimality is much harder to compute • For the class of token algorithms, approximate the optimal token policy using an n-step lookahead policy: • Assume we have some 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 [Velagapudi et al., AAMAS 2009]

  13. Optimality of n-step lookahead 2-step lookahead: pathological case? [Velagapudi et al., AAMAS 2009]

  14. Experimental Setup • Objective: • Study effects of network properties on optimality of random token routing • Single piece of information (token) • Static networks • Scale-Free, Small Worlds, Hierarchical, Lattice, Random • Agents’ utilities sampled from utility distribution • Normal, Exponential [Velagapudi et al., AAMAS 2009]

  15. Experimental Setup • Algorithms: • Random: • Send to random neighbor each time step • RandomSelfAvoid • Send to random neighbor that has not already been visited • RandomTrails • Send to random neighbor using an edge that was not previously used • Lookahead • 4-step lookahead policy (as previously described) [Velagapudi et al., AAMAS 2009]

  16. Normal distribution performance [Velagapudi et al., AAMAS 2009]

  17. Exponential distribution performance [Velagapudi et al., AAMAS 2009]

  18. Noise effects on lookahead policy [Velagapudi et al., AAMAS 2009]

  19. Network Density Effects [Velagapudi et al., AAMAS 2009]

  20. Summary of Previous Work • Random policies perform reasonably under certain utility distributions • Adding simple heuristics significantly improves performance • Certain networks are more conducive to randomized methods • As noise is added, gap between random and optimal policies closes

  21. Multiple token interaction • How does performance change when systems are generating many tokens with redundant information? • If noise is added, are dynamic systems affected differently than static systems?

  22. Experimental Setup • Scale-free network of 50 agents • Token time-to-live (TTL) of 20 • Objective: minimize variance • Cost modeled as sum of “covariance” over time • “Covariance” update rules approximate 1D Kalman filter update

  23. Dynamic Effects

  24. Noise Effects

  25. Discussion • Significant difference in performance between random and lookahead policies • Intelligent heuristics may be able to help in dynamic and noisy situations

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