For the past several years research on robot problem-solving methods has centered on what may one day be called'simple' plans: linear sequences of actions to be performed by single robots to achieve single goals in static environments. This process of forming new subgoals and new states continues until a state is produced in which the original goal is provable; the sequence of operators producing that state is the desired solution. In the case of a single goal wff, the objective is quite simple: achieve the goal (possibly while minimizing some combination of planning and execution cost). The objective of the system is to achieve the single positive goal (perhaps while minimizing search and execution costs) while avoiding absolutely any state satisfying the negative goal.
While still unable to outplay checker masters, the program's playing ability has been greatly improved. Limited progress has been made in the development of an improved book-learning technique and in the optimization of playing strategies as applied to the checker playing program described in an earlier paper with this same title.' While the investigation of the learning procedures forms the essential core of the experimental work, certain improvements have been made in playing techniques which must first be described. The way in which two limiting values (McCarthy's alpha and beta) are used in pruning can be seen by referring The move tree of Figure 1 redrawn to illustrate the detailed method used to keep track of the comparison values.
The fundamental thesis says, in effect, that statistics on kind, frequency, location, order, etc., of selected words are adequate to make reasonably good predictions about the subject matter of documents containing those words. Given this approach to automatic indexing, two problems present themselves, viz., the selection of clue words and the prediction techniques relating clue words and subject categories. Statistical data relating clue words and subject categories constitute hypotheses. Another and different class of documents was obtained and using the statistical data gathered initially, a machine was programmed to index automatically the documents in question.