Messages in form of bitstrings of size 
can be
placed on a global message list either by the
environment or by entities called classifiers. Each
classifier consists of a condition part and an
action part defining a message
it might send to the message list. Both parts are strings out
of
where the `_' serves as a `don't care' if it
appears in the condition part. (Less important
for our purposes, the `_' serves as a `pass-through'
if it appears in the action part.) A non-negative real number
is associated with each classifier indicating its `strength'.
During one cycle all messages on the message list are compared with the condition parts of all classifiers of the system. Each matching classifier computes a `bid' by multiplying its specificity (the number of non-don't cares in its condition part) with the product of its strength and a small factor. The highest bidding classifiers may place their message on the message list of the next cycle, but they have to pay with their bid which is distributed among the classifiers active during the last time step which set up the triggering conditions (this explains the name bucket brigade).
Certain messages result in an action within the environment (like moving a robot one step). Because some of these actions may be regarded as 'useful' by an external critic who can give payoff by increasing the strengths of the currently active classifiers, learning may take place. The central idea is that classifiers which are not active when the environment gives payoff but which had an important role for setting the stage for directly rewarded classifiers can earn credit by participating in `bucket brigade chains'. The success of some active classifier recursively depends on the success of classifiers that are active at the following time ticks.
As an additional means for improving performance Holland introduces a genetic algorithm to construct new classifiers from old successful ones. This feature will not be important for our purposes.