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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

Analyzing the Performance of Randomized Information Sharing under Noise and Dynamics

Paul Scerri,

PrasannaVelagapudi,

KatiaSycara

Robotics Institute

Carnegie Mellon University

large multiagent teams
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

information sharing
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!

information sharing1
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
existing approaches
Existing Approaches
  • Simple
    • Flooding
    • Gossip
    • Tokens
  • Intelligent
    • STEAM
    • Channel Filtering
    • Particle Filter exchange
classical flooding
Classical Flooding
  • Agent pushes information to every neighbor

Info

Info

Info

Info

Info

gossip
Gossip
  • Agent pushes information probabilistically to subset of neighbors

Info

Info

Info

random token routing
Random Token Routing
  • Agent pushes information to a single random neighbor

Info

problem
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?
optimal performance
“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]

optimal performance1
“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]

optimal performance2
“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]

optimality of n step lookahead
Optimality of n-step lookahead

2-step lookahead: pathological case?

[Velagapudi et al., AAMAS 2009]

experimental setup
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]

experimental setup1
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]

normal distribution performance
Normal distribution performance

[Velagapudi et al., AAMAS 2009]

exponential distribution performance
Exponential distribution performance

[Velagapudi et al., AAMAS 2009]

noise effects on lookahead policy
Noise effects on lookahead policy

[Velagapudi et al., AAMAS 2009]

network density effects
Network Density Effects

[Velagapudi et al., AAMAS 2009]

summary of previous work
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
multiple token interaction
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?
experimental setup2
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
discussion
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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