Dynamics of Learning & Distributed Adaptation

1. 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