About the Project...
The Dynamics of Learning project is a Complexity Sciences Center research initiative
that seeks to understand the process of learning using techniques from statistical
mechanics, dynamical systems, and computation theory.
The Dynamics of Learning project is
headed by Professor Jim
Crutchfield and sponsored by DARPA
through its TASK program.
Like much of the work associated with complex systems research, the Dynamics of Learning project is
agent-based, but not in the usual way. Most agent-based research designs
agents for a particular task, or for simulating a particular model; some focus
on building general simulation systems for agent-design. While these efforts
have been valuable and (mostly) successful, they haven't given us a theory
of agents or their collective behavior. This is the gap
Crutchfield hopes to fill. The goal is a general, quantitative, predictive
theory of cognitive agents and of agent collectives, applying both to natural
systems (e.g., the immune system, or insect swarms) and artificial ones (e.g., a
group of autonomous robots). The theory would be analytical, predicting what a
given system would do, rather than synthetic, saying how to design a system
with some desired behaviors, but the analytical methods ought to be useful to
The Dynamics of Learning work builds on computational
mechanics, a theory developed by Crutchfield and his co-workers over
the last decade, combining elements of dynamics, computation and information
theory. The main result of computational mechanics is an automatic method for
pattern discovery from observational data. The method will find all the
patterns in the data, and represent them in the simplest possible way, even
when we know little about the underlying data-generating process. Since any
kind of learning agent is effectively doing some kind of pattern discovery,
computational mechanics puts limits on how well an agent can predict or learn
The Dynamics of Learning project takes computational mechanics in a new
direction. Anything people are willing to call an agent has inputs and
outputs. In organisms, the inputs are all the senses, and the outputs all the
motions of the animal. In machines (e.g., a mobile robot), the inputs would
come from sensors (e.g., cameras, heat detectors, wireless links) and the
outputs would go to ''effectors'' (e.g., motors in wheels and wireless links).
Some mechanism connects them, making an agent into what computer science calls
a transducer or a channel with memory. Computational mechanics now has the
tools to discover the patterns of intrinsic computation going on in a
transducer, including the way it changes its own organization in response to
inputs. These tools work even when the transducer is a ''learning channel''
and works by building a model of its input — in principle, one can do pattern
discovery on pattern discoverers! Using these methods, Crutchfield and his
former student Dave Feldman have already calculated how much internal
complexity an agent must have in order to adequately model its
environment. Excessively simple agents can't grasp all the structure in the
environment, and see it as more random than it really is — and the amount of
excess randomness depends on the mismatch between the agent's cognitive
complexity and the environment's structural complexity.
The next stage of the project will go beyond single-agent learning, to
learning and adaptation in multi-agent systems. A collective of agents is like
a network of interconnected transducers. Computational mechanics can show how
the local behavior of the agents builds up into the global behavior of the
network and can identify the intrinsic computation the collective performs.
Given that, the group will begin seeing when the collective can do things that
individuals cannot — how an adaptation can be distributed, just as a
computation can be. The ultimate goal of the project is to understand
collective cognition, the way groups can sometimes solve problems and learn
things better than any of their members could. It's only fitting that what
amounts to a quantitative sociology of science be initiated at SFI.
(Modified from a piece in the
SFI Bulletin (vol. 16, no. 1) by C. R. Shalizi)