Instance-Based Hyper-Tableaux for Coherent Logic

We consider a fragment of first-order logic known as coherent logic or geometric logic. The essential difference to standard clausal form is that there may be existentially quantified variables in the positive literals of a clause, and only constants and variables are allowed as terms. Coherent logic is interesting because many problems naturally fall into the fragment. Furthermore, the simple term structure might allow for efficient implementations. We propose a calculus for this fragment that extends the `next-generation' hyper-tableaux calculus of Baumgartner, and prove it sound and complete. To our knowledge, this is the first instance-based method that works on a richer input than clause normal form

SMT Solving Modulo Tableau and Rewriting Theories

International audienceWe propose an automated theorem prover that combines an SMT solver with tableau calculus and rewriting. Tableau inference rules are used to unfold propositional content into clauses while atomic formulas are handled using satisfiability decision procedures as in traditional SMT solvers. To deal with quantified first order formulas, we use metavariables and perform rigid unification modulo equalities and rewriting, for which we introduce an algorithm based on superposition, but where all clauses contain a single atomic formula. Rewriting is introduced along the lines of deduction modulo theory, where axioms are turned into rewrite rules over both terms and propositions. Finally, we assess our approach over a benchmark of problems in the set theory of the B method

SGGS theorem proving: an exposition

We present in expository style the main ideas in SGGS, which stands for Semantically-Guided Goal-Sensitive theorem proving. SGGS uses sequences of constrained clauses to represent models, instance generation to go from a candidate model to the next, and resolution as well as other inferences to repair the model. SGGS is refutationally complete for first-order logic, model based, semantically guided, proof confluent, and goal sensitive, which appears to be a rare combination of features. In this paper we describe the core of SGGS in a narrative style, emphasizing ideas and trying to keep technicalities to a minimum, in order to advertise it to builders and users of theorem provers

05431 Abstracts Collection -- Deduction and Applications

From 23.10.05 to 28.10.05, the Dagstuhl Seminar 05431 ``Deduction and Applications\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

The hyper Tableaux calculus with equality and an application to finite model computation

In most theorem proving applications, a proper treatment of equational theories or equality is mandatory. In this article we show how to integrate a modern treatment of equality in the hyper tableau calculus. It is based on splitting of positive clauses and an adapted version of the superposition inference rule, where equations used for superposition are drawn (only) from a set of positive unit clauses, and superposition inferences into positive literals is restricted into (positive) unit clauses only. The calculus also features a generic, semantically justified simplification rule which covers many redundancy elimination techniques known from superposition theorem proving. Our main results are soundness and completeness of the calculus, but we also show how to apply the calculus for finite model computation, and we briefly describe the implementation

Cardinality Constrained Optimization Problems

In this thesis, we examine optimization problems with a constraint that allows for only a certain number of variables to be nonzero. This constraint, which is called a cardinality constraint, has received considerable attention in a number of areas such as machine learning, statistics, computational finance, and operations management. Despite their practical needs, most optimization problems with a cardinality constraints are hard to solve due to their nonconvexity. We focus on constructing tight convex relaxations to such problems

Axiomatizing Discrete Spatial Relations

Qualitative spatial relations are used in artificial intelligence to model commonsense notions such as regions of space overlapping, touching only at their boundaries, or being separate. In this paper we extend earlier work on qualitative relations in discrete space by pre- senting a bi-intuitionistic modal logic with universal modalities, called UBiSKt. This logic has a semantics in which formulae are interpreted as subgraphs. We show how a variety of qualitative spatial relations can be defined in UBiSKt. We make essential use of a sound and complete axiomatisation of the logic and an implementation of a tableau based theorem prover to establish novel properties of these spatial relations. We also explore the role of UBiSKt in expressing spatial relations at more than one level of detail. The features of the logic allow it to rep- resent how a subgraph at a detailed level is approximated at a coarser level