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Analyzing the Performance of Randomized Information Sharing under Noise and Dynamics

This study analyzes the performance of randomized information sharing in large multiagent teams, focusing on how different agents have varying utility for a single piece of information. It explores existing approaches and their limitations, as well as the optimal performance in simple and partially connected networks. The study also investigates the effects of network properties, utility distributions, noise, and dynamics on the performance of randomized information sharing algorithms.

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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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