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I present several computational complexity results for propositional STRIPS planning, i.e., STRIPS planning restricted to ground formulas. Diierent planning problems can be deened by restricting the type of formulas, placing limits on the number of pre-and postconditions, by restricting negation in pre-and postconditions, and by requiring optimal plans. For(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)
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)
In this article, we present a programming language for expressing classificatory problem solvers. CSRL (Conceptual Structures Representation Language) provides structures for representing classification trees, for navigating within those trees, and for encoding uncertainty judgments about the presence of hypotheses. We discuss the motivations, theory, and(More)
I introduce a new search heuristic for propositional STRIPS planning that is based on transforming planning instances to linear programming instances. The linear programming heuristic is admissible for finding minimum length plans and can be used by partial-order planning algorithms. This heuristic appears to be the first non-trivial admissible heuristic(More)
This paper describes an extension of the traditional application of Genetic Programming in the domain of the prediction of daily currency exchange rates. In combination with trigonometric operators, we introduce a new set of high-order statistical functions in a unique representation and analyze each system performance using daily returns of the British(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)
I present a probabilistic analysis of propositional STRIPS planning. The analysis considers two assumptions. One is that each possible precondition (likewise postcondition) of an operator is selected independently of other pre-and postconditions. The other is that each operator has a xed number of preconditions (likewise postconditions). Under both(More)