Inductive Decidability Using Implicit Induction

Abstract

Decision procedures are widely used in automated reasoning tools in order to reason about data structures. In applications, many conjectures fall outside the theory handled by a decision procedure. Often, reasoning about user-defined functions on those data structures is needed. For this, inductive reasoning has to be employed. In this work, classes of function definitions and conjectures are identified for which inductive validity can be automatically decided using implicit induction methods and decision procedures for an underlying theory. The class of equational conjectures considered in this paper significantly extends the results of Kapur & Subramaniam (CADE, 2000) [15], which were obtained using explicit induction schemes. Firstly, nonlinear conjectures can be decided automatically. Secondly, function definitions can use other defined functions in their definitions, thus allowing mutually recursive functions and decidable conjectures about them. Thirdly, conjectures can have general terms from the decidable theory on inductive positions. These contributions are crucial for successfully integrating inductive reasoning into decision procedures, thus enabling their use in push-button mode in applications including verification and program analysis.