Abstract Invariant relations are used to analyze while loops; while their primary application is to derive the function of a loop, they can also be used to derive loop invariants, weakest preconditions, strongest postconditions, sufficient conditions of correctness, necessary conditions of correctness, and termination conditions of loops. In this paper we present two generic invariant relations that capture the semantics of loops whose loop body applies affine transformations on numeric variables.

Most work in the field of inductive inference regards the learning machine to be a passive recipient of data. In a prior paper the passive approach was compared to an active form of learning where the machine is allowed to ask questions. In this paper we continue the study of machines that ask questions by comparing such machines to teams of passive machines. This yields, via work of Pitt and Smith, a comparison of active learning with probabilistic learning. Also considered are query inference machines that learn an approximation of what is desired. The approximation differs from the desired result in finitely many anomalous places.

A formal procedure is given based on the use of regular expressions, to identify and eliminate nontraversable paths in a structured program. By reducing the use of control variables, the program is transformed into an unstructured variant and then restructured into a semantic equivalent of the original program with nontraversable paths eliminated. The authors demonstrate this procedure with an example and discuss its maintenance advantages.>

Abstract We prove that the set of all recursive functions cannot be inferred using first-order queries in the query language containing extra symbols [+ , <]. The proof of this theorem involves a new decidability result about Presburger arithmetic which is of independent interest. Using our machinery, we show that the set of all primitive recursive functions cannot be inferred with a bounded number of mind changes, again using queries in [+, <]. Additionally, we resolve an open question in [7] about passive versus active learning.