1. Dynamics of Learning & Distributed Adaptation
Santa Fe Institute: James P. Crutchfield, P.I.
Dynamics of Learning & Distributed Adaptation
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
CAHDE Results
Predictive theory of agent learning:
Agent Complexity v. Prediction Error v. Data Size
Pattern Discovery: The “Aha” Effect
Incremental learning algorithm
How env’t structure leads to unpredictability for agent
Synchronization for chaotic environments and agents:
Predict required data and time to synchronize
Transient information: New metric of synchronization
Causal Synchrony: Detect coherent subgroup behavior
Dynamics of reinforcement-learning agents:
Nash equilibria v. oscillation v. chaos
Demonstrations
Causal Synchrony for large-scale MASs:
Biologically realistic neural network
Coordination Game MAS (Minority Game, USC)
Toolkit v1.0
Causal-State Splitting Reconstruction Algorithm
Library for Estimating MASS Metrics
Game-Theoretic Simulation Platform for RL MASs
Future Plans
Solvable MAS systems : Continuous-state, -time agents
Dynamical theory of how learning and adaptation occur
Monitor emergence of cooperation in agent collectives
Test on in-house autonomous robotic vehicle collectives
Multi-Agent System Science (MASS)
Design Approach:
Learning Dynamics for the Emergence of Adaptation in Distributed Systems
Critical Elements: Coordination, adaptation, uncertainty
Problem: Analyze distributed information and coordination mechanisms in large-scale MASs
Metrics: Stored information, causal synchrony-distributed information, predictability, generalization ability