[comp.ai.digest] demons

BARNARD@IU.AI.SRI.COM (Stephen Barnard) (04/13/87)

Everyone is familiar with Maxwell's demon, the tiny sprite that
reverses the increase of entropy dictated by the 2nd Law of
Thermodynamics.  He sits at a trapdoor between two chambers containing
a gas in equilibrium (i.e., with maximum entropy) and segregates the
molecules into low- and high-energy populations, thereby moving the
system away from equilibrium and *decreasing* its entropy.  If
Maxwell's demon could exist (and be tamed), we could build perpetual
motion machines.  The thermal gradient between the chambers could
drive a heat engine.

Brillouin killed the demon by considering the connection between
thermodynamics and information theory.  The demon would have to
acquire information about the position and velocity of the molecules
(for example, by bouncing photons off them), but this information would
be gained only at a cost that would balance the decrease in entropy
due to its actions.

Demons of another kind are still alive and well in physics, however.
Creutz described a Monte Carlo algorithm that simulates a system in
thermal equilibrium, much like the Metropolis algorithm used in
simulated annealing.  The difference is that Creutz samples from the
*microcanonical ensemble*, in which the system is considered to be
thermally insulated (constant energy).  When the state of the system
changes randomly, its potential energy usually changes, and this
difference is absorbed or emitted by a demon, which carries kinetic
energy.

What does this have to do with AI?  Simulated annealing is an
effective optimization technique.  It's been used for several vision
problems.  Creutz's algorithm can be used in a new variant of
simulated annealing that is simpler, more efficient, and more easily
controlled than the standard Metropolis version.

references:

Brillouin, L., Science and Information Theory, Academic Press, New
York, 1962.

Creutz,M., Microcanonical Monte Carlo simulation, Physical Review
Letters, vol. 50, no. 19, May 9, 1983, pp. 363-373.

Barnard, S., Stereo matching by hierarchical, microcanonical
annealing, SRI technical note 414 (to appear in Proc. IJCAI87).
-------