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Dynamics of Learning & Distributed Adaptation. Santa Fe Institute: James P. Crutchfield, P.I. Multi-Agent System Science (MASS) Dimension Agents learn complex environment ab initio Synchronization of agent to environment Agents adapt to nonstationary environment

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dynamics of learning distributed adaptation
Dynamics of Learning & Distributed Adaptation

Santa Fe Institute: James P. Crutchfield, P.I.

  • Multi-Agent System Science (MASS)
  • Dimension
    • Agents learn complex environment ab initio
    • Synchronization of agent to environment
    • Agents adapt to nonstationary environment
    • Strategies for agent-agent coordination
  • Metrics for large-scale MASs
    • Statistical Complexity:
      • Amount of structure & organization in environ’t
      • Individual agent knowledge v. group knowledge
    • Mutuality: Architecture of information flow
    • Lyapunov Spectra: Degrees of stability and instability
    • Causal Synchrony: Detect coherent subgroup behavior
  • CAHDE REF
  • ACFC: Adapting to instabilities in air flow control
  • AirOps: Emergence of spontaneous leadership
  • Solution:
    • Interacting reinforcement and -machine learning agents solve a group task
  • Approach:

Pattern Discovery: Beyond pattern recognition

Design & analysis based on sound principles of learning

Metrics for cooperation in large-scale systems

  • Results To Date

Predictive theory of agent learning:

Quantify agent modeling capacity

Data Set Size v. Prediction Error v. Model Complexity

Pattern Discovery: The “Aha” Effect

Incremental learning algorithm

Quantify structure in environment:

How structure leads to unpredictability for agent

Define synchronization for chaotic environments:

Predict required data and time to synchronize

Periodic case solved in closed form

Transient information: New metric of synchronization

Dynamics of reinforcement-learning agents:

Nash equilibria v. oscillation v. chaos

Dependence on system architecture and initial state

  • Future Plans (6 months out)
  • New problems:
    • Continuous-state and continuous-time agents
    • Adaptation to active, pattern-forming environments
    • Dynamical theory of how learning and adaptation occur
  • Anticipated results:
    • Monitor emergence of cooperation in agent collectives
    • Measure mutuality in interacting reinforcement learners
    • Test on in-house autonomous robotic vehicle collectives
  • Analytical tools:
    • Predict whether or not group cooperation can occur
    • Agent intelligence versus group size
    • Prediction of the rate of adaptation during collective task
    • Prototype models: Solvable MAS systems
  • Software tools:
    • Ab Initio Learning Algorithms
    • Library for Estimating MASS Metrics
    • Enterprise Java Platform for Robot Collectives