PAL: A pattern-based first-order inductive system

It has been argued that much of human intelligence can be viewed as the process of matching stored patterns. In particular, it is believed that chess masters use a pattern-based knowledge to analyze a position, followed by a pattern-based controlled search to verify or correct the analysis. In this paper, a first-order system, called PAL, that can learn patterns in the form of Horn clauses from simple example descriptions and general purpose knowledge is described. The learning model is based on (i) a constrained least general generalization algorithm to structure the hypothesis space and guide the learning process, and (ii) a pattern-based representation knowledge to constrain the construction of hypothesis. It is shown how PAL can learn chess patterns which are beyond the learning capabilities of current inductive systems. The same pattern-based approach is used to learn qualitative models of simple dynamic systems and counterpoint rules for two-voice musical pieces. Limitations of PAL in particular, and first-order systems in general, are exposed in domains where a large number of background definitions may be required for induction. Conclusions and future research directions are given.