Combining weak learning heuristics in general problem solvers

This paper is concerned with state space problem
solvers that achieve generality by learning strong
heuristics through experience in a particular domain. We specifically consider two ways of learning by analysing past solutions that can improve future problem solving: creating macros and the chunks. A method of learning search heuristics is specified which is related to 'chunking' but which complements the use
of macros within a goal directed system. An example of the creation and combined use of macros and chunks, taken from an implemented system, is described.