The program described learns to improve its performance in
the playing of a game, from experience. The main objectives
of the project are that the system should observe the
following principles:
1) The program should not rely on any special evaluation
functions, which would embody domain-specific information.
2) Initial knowledge of the domain should be minimal,
and further knowledge gained should be assimilated in
terms of prior knowledge 3) The system of representation employed should as far
as possible be independent of the domain, again
avoiding the incorporation of domain-specific information.
In customary Artificial Intelligence terms, the program is
referred to as existing in a domain or environment. The
model has a goal within this domain and has available
certain actions which it may take in order to achieve its
goal. The goal is represented as a Structure. This term
will be used throughout to denote a set of objects from
the domain, constrained by various domain-pertinent relationships. The actions, goals and objects are the
initial known facts of the environment. The program has
an innate ability to plan simple sequences of actions to
achieve its goals. Inevitably, these plans do not take
into account enough of the nature of the domain and prove
inadequate. In such events the descriptive abilities of
the program are invoked to correct the deficiency, and the
program's model of its environment is enriched
