104 research outputs found
Automated Deduction – CADE 28
This open access book constitutes the proceeding of the 28th International Conference on Automated Deduction, CADE 28, held virtually in July 2021. The 29 full papers and 7 system descriptions presented together with 2 invited papers were carefully reviewed and selected from 76 submissions. CADE is the major forum for the presentation of research in all aspects of automated deduction, including foundations, applications, implementations, and practical experience. The papers are organized in the following topics: Logical foundations; theory and principles; implementation and application; ATP and AI; and system descriptions
Proof-theoretic Semantics for Intuitionistic Multiplicative Linear Logic
This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic (IMLL). The starting point is a review of Sandqvist’s B-eS for intuitionistic propositional logic (IPL), for which we propose an alternative treatment of conjunction that takes the form of the generalized elimination rule for the connective. The resulting semantics is shown to be sound and complete. This motivates our main contribution, a B-eS for IMLL
, in which the definitions of the logical constants all take the form of their elimination rule and for which soundness and completeness are established
Automated Reasoning
This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book
Disproving in First-Order Logic with Definitions, Arithmetic and Finite Domains
This thesis explores several methods which enable a first-order
reasoner to conclude satisfiability of a formula modulo an
arithmetic theory. The most general method requires restricting
certain quantifiers to range over finite sets; such assumptions
are common in the software verification setting. In addition, the
use of first-order reasoning allows for an implicit
representation of those finite sets, which can avoid
scalability problems that affect other quantified reasoning
methods. These new techniques form a useful complement to
existing methods that are primarily aimed at proving validity.
The Superposition calculus for hierarchic theory combinations
provides a basis for reasoning modulo theories in a first-order
setting. The recent account of ‘weak abstraction’ and related
improvements make an mplementation of the calculus practical.
Also, for several logical theories of interest Superposition is
an effective decision procedure for the quantifier free fragment.
The first contribution is an implementation of that calculus
(Beagle), including an optimized implementation of Cooper’s
algorithm for quantifier elimination in the theory of linear
integer arithmetic. This includes a novel means of extracting
values
for quantified variables in satisfiable integer problems. Beagle
won an efficiency award at CADE Automated theorem prover System
Competition (CASC)-J7, and won the arithmetic non-theorem
category at CASC-25. This implementation is the start point for
solving the ‘disproving with theories’ problem.
Some hypotheses can be disproved by showing that, together with
axioms the hypothesis is unsatisfiable. Often this is relative to
other axioms that enrich a base theory by defining new functions.
In that case, the disproof is contingent on the satisfiability of
the enrichment.
Satisfiability in this context is undecidable. Instead, general
characterizations of definition formulas, which do not alter the
satisfiability status of the main axioms, are given. These
general criteria apply to recursive definitions, definitions over
lists, and to arrays. This allows proving some non-theorems which
are otherwise intractable, and justifies similar disproofs of
non-linear arithmetic formulas.
When the hypothesis is contingently true, disproof requires
proving existence of
a model. If the Superposition calculus saturates a clause set,
then a model exists,
but only when the clause set satisfies a completeness criterion.
This requires each
instance of an uninterpreted, theory-sorted term to have a
definition in terms of
theory symbols.
The second contribution is a procedure that creates such
definitions, given that a subset of quantifiers range over finite
sets. Definitions are produced in a counter-example driven way
via a sequence of over and under approximations to the clause
set. Two descriptions of the method are given: the first uses the
component solver modularly, but has an inefficient
counter-example heuristic. The second is more general, correcting
many of the inefficiencies of the first, yet it requires tracking
clauses through a proof. This latter method is shown to apply
also to lists and to problems with unbounded quantifiers.
Together, these tools give new ways for applying successful
first-order reasoning methods to problems involving interpreted
theories
Pseudo-contractions as Gentle Repairs
Updating a knowledge base to remove an unwanted consequence is a challenging task. Some of the original sentences must be either deleted or weakened in such a way that the sentence to be removed is no longer entailed by the resulting set. On the other hand, it is desirable that the existing knowledge be preserved as much as possible, minimising the loss of information. Several approaches to this problem can be found in the literature. In particular, when the knowledge is represented by an ontology, two different families of frameworks have been developed in the literature in the past decades with numerous ideas in common but with little interaction between the communities: applications of AGM-like Belief Change and justification-based Ontology Repair. In this paper, we investigate the relationship between pseudo-contraction operations and gentle repairs. Both aim to avoid the complete deletion of sentences when replacing them with weaker versions is enough to prevent the entailment of the unwanted formula. We show the correspondence between concepts on both sides and investigate under which conditions they are equivalent. Furthermore, we propose a unified notation for the two approaches, which might contribute to the integration of the two areas
An Infinite Needle in a Finite Haystack: Finding Infinite Counter-Models in Deductive Verification
First-order logic, and quantifiers in particular, are widely used in
deductive verification. Quantifiers are essential for describing systems with
unbounded domains, but prove difficult for automated solvers. Significant
effort has been dedicated to finding quantifier instantiations that establish
unsatisfiability, thus ensuring validity of a system's verification conditions.
However, in many cases the formulas are satisfiable: this is often the case in
intermediate steps of the verification process. For such cases, existing tools
are limited to finding finite models as counterexamples. Yet, some quantified
formulas are satisfiable but only have infinite models. Such infinite
counter-models are especially typical when first-order logic is used to
approximate inductive definitions such as linked lists or the natural numbers.
The inability of solvers to find infinite models makes them diverge in these
cases. In this paper, we tackle the problem of finding such infinite models.
These models allow the user to identify and fix bugs in the modeling of the
system and its properties. Our approach consists of three parts. First, we
introduce symbolic structures as a way to represent certain infinite models.
Second, we describe an effective model finding procedure that symbolically
explores a given family of symbolic structures. Finally, we identify a new
decidable fragment of first-order logic that extends and subsumes the
many-sorted variant of EPR, where satisfiable formulas always have a model
representable by a symbolic structure within a known family. We evaluate our
approach on examples from the domains of distributed consensus protocols and of
heap-manipulating programs. Our implementation quickly finds infinite
counter-models that demonstrate the source of verification failures in a simple
way, while SMT solvers and theorem provers such as Z3, cvc5, and Vampire
diverge
Programming with Specifications
This thesis explores the use of specifications for the construction of correct programs. We go beyond their standard use as run-time assertions, and present algorithms, techniques and implementations for the tasks of 1) program verification, 2) declarative programming and 3) software synthesis. These results are made possible by our advances in the domains of decision procedure design and implementation. In the first part of this thesis, we present a decidability result for a class of logics that support user-defined recursive function definitions. Constraints in this class can encode expressive properties of recursive data structures, such as sortedness of a list, or balancing of a search tree. As a result, complex verification conditions can be stated concisely and solved entirely automatically. We also present a new decision procedure for a logic to reason about sets and constraints over their cardinalities. The key insight lies in a technique to decompose con- straints according to mutual dependencies. Compared to previous techniques, our algorithm brings significant improvements in running times, and for the first time integrates reasoning about cardinalities within the popular DPLL(T ) setting. We integrated our algorithmic ad- vances into Leon, a static analyzer for functional programs. Leon can reason about constraints involving arbitrary recursive function definitions, and has the desirable theoretical property that it will always find counter-examples to assertions that do not hold. We illustrate the flexibility and efficiency of Leon through experimental evaluation, where we used it to prove detailed correctness properties of data structure implementations. We then illustrate how program specifications can be used as a high-level programming construct ; we present Kaplan, an extension of Scala with first-class logical constraints. Kaplan allows programmers to create, manipulate and combine constraints as they would any other data structure. Our implementation of Kaplan illustrates how declarative programming can be incorporated into an existing mainstream programming language. Moreover, we examine techniques to transform, at compile-time, program specifications into efficient executable code. This approach of software synthesis combines the correctness benefits of declarative programming with the efficiency of imperative or functional programming
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