The authors develop a simple weighting scheme that maps functional formulas homomorphically onto the integers. An implementation of the algorithm has been successful in finding complex formulas in a few hours' time on a workstation. Most learning algorithms assume a family of possible target functions and then find the best function from this family to describe the examples. Starting from fundamentals, they obtain a mathematical characterization of inductive inference procedures than can refute an entire hypothesis class. As applications, they cite results in extracting rules to classify objects, extract macro operators from plans, and constructing hierarchies from detailed knowledge bases.
Decision lists are a simple form of decision tree.
Some years ago R. Rivest showed that such lists can be learned efficiently from examples. The basic idea is that decisions about what action to take are often made on the basis of patterns in the situation description. Using this algorithm, one starts with a slow but exhaustive strategy, and by compiling ones experience into tree patterns, this strategy becomes much faster. Generalized Unification as Background Knowledge in Learning Logic Program J. Barzdins et al. Complete ATIP reports on Asian Science and Technology go to subscribers and collaborating organizations by direct distribution, or via electronic access.