Formalized Proof Systems for Propositional Logic

We have formalized a range of proof systems for classical propositional logic (sequent calculus, natural deduction, Hilbert systems, resolution) in Isabelle/HOL and have proved the most important meta-theoretic results about semantics and proofs: compactness, soundness, completeness, translations between proof systems, cut-elimination, interpolation and model existence

Foundations of fuzzy answer set programming

Answer set programming (ASP) is a declarative language that is tailored towards combinatorial search problems. Although ASP has been applied to many problems, such as planning, configuration and verification of software, and database repair, it is less suitable for describing continuous problems. In this thesis we therefore studied fuzzy answer set programming (FASP). FASP is a language that combines ASP with ideas from fuzzy logic -- a class of many-valued logics that are able to describe continuous problems. We study the following topics: 1. An important issue when modeling continuous optimization problems is how to cope with overconstrained problems. In many cases we can opt to allow imperfect solutions, i.e. solutions that do not satisfy all constraints, but are sufficiently acceptable. However, the question which one of these imperfect solutions is most suitable then arises. Current approaches to fuzzy answer set programming solve this problem by attaching weights to the rules of the program. However, it is often not clear how these weights should be chosen and moreover weights do not allow to order different solutions. We improve upon this technique by using aggregators, which eliminate the aforementioned problems. This allows a richer modeling language and bridges the gap between FASP and other techniques such as valued constraint satisfaction problems. 2. The wishes of users and implementers of a programming language are often in direct conflict with each other. Users prefer a rich language that is easy to model in, whereas implementers prefer a small language that is easy to implement. We reconcile these differences by identifying a core language for FASP, called core FASP (CFASP), that only consists of non-constraint rules with monotonically increasing functions and negators in the body. We show that CFASP is capable of simulating constraint rules, monotonically decreasing functions, aggregators, S-implicators and classical negation. Moreover we remark that the simulations of constraints and classical negation bear a great resemblance to their simulations in classical ASP, which provides further insight into the relationship between ASP and FASP. 3. As a first step towards the creation of an implementation method for FASP we research whether it is possible to translate a FASP program to a fuzzy SAT problem. We introduce the concept of the completion of a FASP program and show that for programs without loops the models of the completion coincide with the answer sets. Furthermore we show that if a program has loops, we can translate the program to a fuzzy SAT problem by generalizing the concept of loop formulas. We illustrate this on a continuous version of the k-center problem. Such a translation is important because it allows us to solve FASP programs by means of solvers for fuzzy SAT. Under the appropriate conditions it is for example possible to solve FASP programs by means of off-the-shelf solvers for mixed integer programming (MIP)

Logical and deep learning methods for temporal reasoning

In this thesis, we study logical and deep learning methods for the temporal reasoning of reactive systems. In Part I, we determine decidability borders for the satisfiability and realizability problem of temporal hyperproperties. Temporal hyperproperties relate multiple computation traces to each other and are expressed in a temporal hyperlogic. In particular, we identify decidable fragments of the highly expressive hyperlogics HyperQPTL and HyperCTL*. As an application, we elaborate on an enforcement mechanism for temporal hyperproperties. We study explicit enforcement algorithms for specifications given as formulas in universally quantified HyperLTL. In Part II, we train a (deep) neural network on the trace generation and realizability problem of linear-time temporal logic (LTL). We consider a method to generate large amounts of additional training data from practical specification patterns. The training data is generated with classical solvers, which provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions such that the neural network generalizes to the semantics of the logic. The neural network can predict solutions even for formulas from benchmarks from the literature on which the classical solver timed out. Additionally, we show that it solves a significant portion of problems from the annual synthesis competition (SYNTCOMP) and even out-of-distribution examples from a recent case study.Diese Arbeit befasst sich mit logischen Methoden und mehrschichtigen Lernmethoden für das zeitabhängige Argumentieren über reaktive Systeme. In Teil I werden die Grenzen der Entscheidbarkeit des Erfüllbarkeits- und des Realisierbarkeitsproblem von temporalen Hypereigenschaften bestimmt. Temporale Hypereigenschaften setzen mehrere Berechnungsspuren zueinander in Beziehung und werden in einer temporalen Hyperlogik ausgedrückt. Insbesondere werden entscheidbare Fragmente der hochexpressiven Hyperlogiken HyperQPTL und HyperCTL* identifiziert. Als Anwendung wird ein Enforcement-Mechanismus für temporale Hypereigenschaften erarbeitet. Explizite Enforcement-Algorithmen für Spezifikationen, die als Formeln in universell quantifiziertem HyperLTL angegeben werden, werden untersucht. In Teil II wird ein (mehrschichtiges) neuronales Netz auf den Problemen der Spurgenerierung und Realisierbarkeit von Linear-zeit Temporallogik (LTL) trainiert. Es wird eine Methode betrachtet, um aus praktischen Spezifikationsmustern große Mengen zusätzlicher Trainingsdaten zu generieren. Die Trainingsdaten werden mit klassischen Solvern generiert, die zu jeder Formel nur eine von vielen möglichen Lösungen liefern. Es wird gezeigt, dass es ausreichend ist, an diesen speziellen Lösungen zu trainieren, sodass das neuronale Netz zur Semantik der Logik generalisiert. Das neuronale Netz kann Lösungen sogar für Formeln aus Benchmarks aus der Literatur vorhersagen, bei denen der klassische Solver eine Zeitüberschreitung hatte. Zusätzlich wird gezeigt, dass das neuronale Netz einen erheblichen Teil der Probleme aus dem jährlichen Synthesewettbewerb (SYNTCOMP) und sogar Beispiele außerhalb der Distribution aus einer aktuellen Fallstudie lösen kann

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

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

Superposition modulo theory

This thesis is about the Hierarchic Superposition calculus SUP(T) and its application to reasoning in hierarchic combinations FOL(T) of the free first-order logic FOL with a background theory T where the hierarchic calculus is refutationally complete or serves as a decision procedure. Particular hierarchic combinations covered in the thesis are the combinations of FOL and linear and non-linear arithmetic, LA and NLA resp. Recent progress in automated reasoning has greatly encouraged numerous applications in soft- and hardware verification and the analysis of complex systems. The applications typically require to determine the validity/unsatisfiability of quantified formulae over the combination of the free first-order logic with some background theories. The hierarchic superposition leverages both (i) the reasoning in FOL equational clauses with universally quantified variables, like the standard superposition does, and (ii) powerful reasoning techniques in such theories as, e.g., arithmetic, which are usually not (finitely) axiomatizable by FOL formulae, like modern SMT solvers do. The thesis significantly extends previous results on SUP(T), particularly: we introduce new substantially more effective sufficient completeness and hierarchic redundancy criteria turning SUP(T) to a complete or a decision procedure for various FOL(T) fragments; instantiate and refine SUP(T) to effectively support particular combinations of FOL with the LA and NLA theories enabling a fully automatic mechanism of reasoning about systems formalized in FOL(LA) or FOL(NLA).Diese Arbeit befasst sich mit dem hierarchischen Superpositionskalkül SUP(T) und seiner Anwendung auf hierarchischen Kombinationen FOL(T) der freien Logik erste Stufe FOL und einer Hintergrundtheorie T, deren hierarchischer Kalkül widerlegungsvollständig ist oder als Entscheidungsverfahren dient. Die behandelten hierarchischen Kombinationen sind im Besonderen die Kombinationen von FOL und linearer und nichtlinearer Arithmetik, LA bzw. NLA. Die jüngsten Fortschritte in dem Bereich des automatisierten Beweisens haben zahlreiche Anwendungen in der Soft- und Hardwareverifikation und der Analyse komplexer Systeme hervorgebracht. Die Anwendungen erfordern typischerweise die Gültigkeit/Unerfüllbarkeit quantifizierter Formeln über Kombinationen der freien Logik erste Stufe mit Hintergrundtheorien zu bestimmen. Die hierarchische Superposition verbindet beides: (i) das Beweisen über FOL-Klauseln mit Gleichheit und allquantifizierten Variablen, wie in der Standardsuperposition, und (ii) mächtige Beweistechniken in Theorien, die üblicherweise nicht (endlich) axiomatisierbar durch FOL-Formeln sind (z. B. Arithmetik), wie in modernen SMT-Solvern. Diese Arbeit erweitert frühere Ergebnisse über SUP(T) signifikant, im Besonderen führen wir substantiell effektiverer Kriterien zur Bestimmung der hinreichenden Vollständigkeit und der hierarchischen Redundanz ein. Mit diesen Kriterien wird SUP(T) widerlegungsvollständig beziehungsweise ein Entscheidungsverfahren für verschiedene FOL(T)-Fragmente. Weiterhin instantiieren und verfeinern wir SUP(T), um effektiv die Kombinationen von FOL mit der LA- und der NLA-Theorie zu unterstützen, und erhalten eine vollautomatische Beweisprozedur auf Systemen, die in FOL(LA) oder FOL(NLA) formalisiert werden können

Prime implicate generation in equational logic

The work presented in this memoir deals with the generation of prime implicates in ground equational logic, i.e., of the most general consequences of formulae containing equations and disequations between ground terms.It is divided in three parts. First, two calculi that generate implicates are defined. Their deductive-completeness is proved, meaning they can both generate all the implicates up to redundancy of equational formulae.Second, a tree data structure to store the generated implicates is proposed along with algorithms to detect redundancies and prune the branches of the tree accordingly. This data structure is adapted to the different kinds of clauses (with and without function symbols, with and without constraints) and to the various formal definitions of redundancy used in the calculi since each calculus uses different -- although similar -- redundancy criteria. Termination and correction proofs are provided with each algorithm. Finally, an experimental evaluation of the different prime implicate generation methods based on research prototypes written in Ocaml is conducted including a comparison with state-of-the-art prime implicate generation tools. This experimental study is used to identify the most efficient variants of the proposed algorithms. These show promising results overstepping the state of the art.Ce mémoire présente le résultat de mon travail de thèse sur la génération d'impliqués premiers en logique équationnelle fermée, i.e., la génération des conséquences les plus générales de formules logiques contenants des équations et des disequations entre termes sans variables. Ce mémoire est divisé en trois parties. Tout d'abord, deux calculs de génération d'impliqués sont définis. Leur complétude pour la déduction est prouvée, ce qui signifie qu'ils sont tous deux capables de générer l'ensemble des impliqués modulo redondance d'une formule équationnelle fermée. Dans une deuxième partie, une structure de données arborescente est proposée pour stocker les impliqués générés, accompagnée d'algorithmes pour déceler les redondances et couper les branches de l'arbre lorsque c'est nécessaire. Cette structure de données est adaptée aux différents types de clauses (avec et sans symboles de fonctions, avec et sans contraintes) ainsi qu'aux différentes notions de redondance utilisées dans les calculs. En effet, chaque calcul utilise un critère de redondance légèrement différent des autres. Les preuves de correction et de terminaison des algorithmes sont fournies pour chaque algorithme. Enfin, une évaluation expérimentale des différentes méthodes de génération d'impliqués premiers est réalisée. Pour cela, un prototype de ces méthodes, écrit en Ocaml est comparé à des outils de génération d'impliqués premiers récents.Les résultats de ces expériences sont utilisés pour identifier les variantes les plus efficaces des algorithmes proposés. Les résultats sont prometteurs et dans la plupart des cas, meilleurs que ceux de l'état de l'art