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I describe several computational complexity results for planning, some of which identify tractable planning problems. The model of planning, called "propositional planning," is simple—conditions within operators are literals with no variables allowed. The different plan­ ning problems are defined by different restrictions on the preconditions and(More)
The problem of abduction can be characterized as nding the best explanation of a set of data. In this paper we focus on one type of abduction in which the best explanation is the most plausible combination of hypotheses that explains all the data. We then present several computational complexity results demonstrating that this type of abduction is(More)
The ability to map the state of an object into a category languages is transforming AI theories into symbolic struc-in a classification hierarchy has long been an important tures. This pattern can be seen in knowledge representa-part of many fields, for example, biology and medicine. tion (for example, semantic nets and KL-ONE [Brachman Recently, AI(More)
An outstanding abstract will be put here. *David E. Hirsch is now at Price Waterhouse. **Correspondence should be sent to Tom Bylander, The Ohio State University, Computer and Information Science, Columbus, OH 43210, USA ***We acknowledge everybody and anybody that we should. This is a nearlynal draft of the paper that appeared as D. E. Hirsch, S. R. Simon,(More)
For two-class datasets, we provide a method for estimating the generalization error of a bag using out-of-bag estimates. In bagging, each predictor (single hypothesis) is learned from a bootstrap sample of the training examples; the output of a bag (a set of predictors) on an example is determined by voting. The out-of-bag estimate is based on recording the(More)