Recent years have seen considerable interest in procedures for computing finite models of first-order logic specifications. One of the major paradigms, MACE-style model building, is based on reducing model search to a sequence of propositional satisfiability problems and applying (efficient) SAT solvers to them. A problem with this method is that it does not scale well because the propositional formulas to be considered may become very large. We propose instead to reduce model search to a sequence of satisfiability problems consisting of function-free first-order clause sets, and to apply (efficient) theorem provers capable of deciding such problems. The main appeal of this method is that first-order clause sets grow more slowly than their propositional counterparts, thus allowing for more space efficient reasoning. In this paper we describe our proposed reduction in detail and discuss how it is integrated into the Darwin prover, our implementation of the Model Evolution calculus. The results are general, however, as our approach can be used in principle with any system that decides the satisfiability of function-free first-order clause sets. To demonstrate its practical feasibility, we tested our approach on all satisfiable problems from the TPTP library. Our methods can solve a significant subset of these problems, which overlaps but is not included in the subset of problems solvable by state-of-the-art finite model builders such as Paradox and Mace4.

We introduce a DPLL calculus that is a decision procedure for the Bernays-Schönfinkel class, also known as EPR. Our calculus allows combining techniques for efficient propositional search with datastructures, such as Binary Decision Diagrams, that can efficiently and succinctly encode finite sets of substitutions and operations on these. In the calculus, clauses comprise of a sequence of literals together with a finite set of substitutions; truth assignments are also represented using substitution sets. The calculus works directly at the level of sets, and admits performing parallel constraint propagation and decisions, resulting in potentially exponential speedups over existing approaches.

Abstract. Local reasoning allows to handle SMT problems involving a certain class of universally quantified formulas in a complete way by instantiation to a finite set of ground formulas. We present a method to generate this set incrementally, in order to provide a more efficient way of solving these satisfiability problems. The incremental instantiation is guided semantically, inspired by the instance generation approach to first-order theorem proving. Our method is sound and complete, and terminates on both satisfiable and unsatisfiable input after generating a subset of the instances needed in standard local reasoning. 1

Abstract. The clause linking technique of Lee and Plaisted proves the unsatisfiability of a set of first-order clauses by generating a sufficiently large set of instances of these clauses that can be shown to be propositionally unsatisfiable. In recent years, this approach has been refined in several directions, leading to both tableau-based methods, such as the Disconnection Tableau Calculus, and saturation-based methods, such as Primal Partial Instantiation and Resolution-based Instance Generation. We investigate the relationship between these calculi and answer the question to what extent refutation or consistency proofs in one calculus can be simulated in another one. 1

Mathematical logicians had developed the art of formalizing declarative knowledge long before the advent of the computer age. But they were interested primarily in formalizing mathematics. Because of the important role of nonmathematical knowledge in AI, their emphasis was too narrow from the perspective of knowledge representation, their formal languages were not sufficiently expressive. On the other hand, most logicians were not concerned about the possibility of automated reasoning; from the perspective of knowledge representation, they were often too generous in the choice of syntactic constructs. In spite of these differences, classical mathematical logic has exerted significant influence on knowledge representation research, and it is appropriate to begin this handbook with a discussion of the relationship between these fields. The language of classical logic that is most widely used in the theory of knowledge representation is the language of first-order (predicate) formulas. These are the formulas that John McCarthy proposed to use for representing declarative knowledge in his advice taker paper [176], and Alan Robinson proposed to prove automatically using resolution [236]. Propositional logic is, of course, the most important subset of first-order logic; recent

Abstract. iProver-Eq is an implementation of an instantiation-based calculus Inst-Gen-Eq which is complete for first-order logic with equality. iProver-Eq extends the iProver system with superposition-based equational reasoning and maintains the distinctive features of the Inst-Gen method. In particular, firstorder reasoning is combined with efficient ground satisfiability checking where the latter is delegated in a modular way to any state-of-the-art SMT solver. The first-order reasoning employs a saturation algorithm making use of redundancy elimination in form of blocking and simplification inferences. We describe the equational reasoning as it is implemented in iProver-Eq, the main challenges and techniques that are essential for efficiency. 1

Workshop Proceedings

Abstract. iProver is an instantiation-based theorem prover which is based on Inst-Gen calculus, complete for first-order logic. One of the distinctive features of iProver is a modular combination of instantiation and propositional reasoning. In particular, any state-of-the art SAT solver can be integrated into our framework. iProver incorporates state-of-the-art implementation techniques such as indexing, redundancy elimination, semantic selection and saturation algorithms. Redundancy elimination implemented in iProver include: dismatching constraints, blocking non-proper instantiations and propositional-based simplifications. In addition to instantiation, iProver implements ordered resolution calculus and a combination of instantiation and ordered resolution. In this paper we discuss the design of iProver and related implementation issues. 1

Automated theorem proving is a method to establish or disprove logical theorems. While these can be theorems in the classical mathematical sense, we are more concerned with logical encodings of properties of algorithms, hardware and software. Especially in the area of hardware verification, propositional logic is used widely in industry. Satisfiability Module Theories (SMT) is a set of logics which extend propositional logic with theories relevant for specific application domains. In particular, software verification has received much attention, and efficient algorithms have been devised for reasoning over arithmetic and data types. Built-in support for theories by decision procedures is often significantly more efficient than reductions to propositional logic (SAT). Most efficient SAT solvers are based on the DPLL architecture, which is also the basis for most efficient SMT solvers. The main shortcoming of both kinds of logics is the weak support for non-ground reasoning, which noticeably limits the applicability to real world systems. The Model

In many theorem proving applications, a proper treatment of equational theories or equality is mandatory. In this paper we show how to integrate a modern treatment of equality in the Model Evolution calculus (ME), a first-order version of the propositional DPLL procedure. The new calculus, MEE, is a proper extension of the ME calculus without equality. Like ME it maintains an explicit candidate model, which is searched for by DPLL-style splitting. For equational reasoning MEE uses an adapted version of the superposition inference rule, where equations used for superposition are drawn (only) from the candidate model. The calculus also features a generic, semantically justified simplification rule which covers many simplification techniques known from superposition-style theorem proving. Our main theoretical result is the correctness of the MEE calculus in the presence of very general redundancy elimination criteria. We also describe our implementation of the calculus, the E-Darwin system, and we report on practical experiments with it on the TPTP problems library.