Subtype satisfiability and entailment

Subtype constraints were introduced in advanced programming language research for designing subtype systems and program analysis algorithms. Two logical problems arise in this context: subtype satisfiability and subtype entailment. Subtype satisfiability underlies subtype inference; subtype entailment is for simplifying subtyping constraints in the same application. In this thesis, we investigate both problems systematically for a number of dialects of subtyping constraint languages that may vary in the following dimensions: types may be simple (finite) or recursive (infinite), type constants may be ordered in lattices or in general partially ordered sets, subtyping can be structural or non-structural, depending on whether least and greatest types are permitted. We use and develop new formal reasoning techniques based on automata, unification, and modal logic. Subtype satisfiability is well understood for all dialects with constants ordered in a lattice. Although cubic time algorithms are given by Palsberg and O\u27Keefe (1995), Pottier (1996), and Palsberg, Wand, and O\u27Keefe (1997), little is known about dialects where constants belong to arbitrary partially ordered sets. We present a uniform treatment to determine the complexities of all these classes. As a consequence, we settle a problem left open by Tiuryn and Wand in 1993 and also subsume complexity bounds given by Wand and Tiuryn (1993), Tiuryn (1992), and Frey (2002). Our results are based on a new connection between modal logic and subtype constraints that we present. Subtype entailment is known to be hard even for simple subtype constraint languages. Rehof and Henglein determined the complexity of structural subtype entailment with type constants ordered in a lattice. They proved coNP-completeness for simple types (1997) and PSPACE-completeness for recursive types (1998). Furthermore, they showed that non-structural subtype entailment is PSPACE-hard and is conjectured PSPACE-complete for the case with only two type constants for the least and greatest types respectively (1998). Yet the problem still remains open today. We argue that the difficulty occurs due to e ects linked to non-regular word languages. In order to do so, we precisely characterize subtype entailment by finite word automata with word equations. This characterization induces new results on non-structural subtype entailment, constituting a promising starting point for future investigation on decidability.Diese Arbeit untersucht zwei logische Probleme der programmiersprachlichen Typinferenz: Erfüllbarkeit und Subsumption von Teiltyp-Constraints. Wir untersuchen diese Probleme systematisch für eine Reihe von Constraintsprachen. Dabei greifen wir auf Methoden der computationalen Logik, Unifikations- und Automatentheorie zurück. Teiltyp-Erfüllbarkeit ist für den Fall wohl verstanden, dass die Typkonstanten in einem Verband angeordnet sind (Palsberg und O\u27Keefe (1995), Pottier (1996), Palsberg, Wand und O\u27Keefe (1997)). Der allgemeinere Fall mit beliebig angeordneten Konstanten wurde bislang weniger untersucht. Wir stellen einen ersten universellen Ansatz vor, indem wir erstmals einen Zusammenhang zwischen Teiltyp-Constraints und Modallogik aufzeigen. Dadurch lösen wir unter Anderem ein seit 1993 offenes Komplexitätsproblem von Wand und Tiuryn. Teiltyp-Subsumption ist selbst für einfachste Constraintsprachen von hoher Komplexität. Rehof und Henglein zeigten dies für den strukturellen Verbandsfall (mit zwei Typkonstanten 1997, 1998), ließen jedoch den nicht-strukturellen Fall offen. In dieser Arbeit betrachten wir den einfachsten nicht-strukturellen Fall. Hier zeigen wir, dass versteckte Wortgleichungen neue Schwierigkeiten verursachen. Hierzu charakterisieren wir Teiltyp-Subsumption durch spezielle endliche Automaten mit Wortgleichungen. Unsere Charakterisierung liefert partielle Entscheidbarkeitsresulte zur nichtstrukturellen Teiltyp-Subsumption und kann als Grundlage für künftige Untersuchungen dienen

The Algebraic Intersection Type Unification Problem

The algebraic intersection type unification problem is an important component in proof search related to several natural decision problems in intersection type systems. It is unknown and remains open whether the algebraic intersection type unification problem is decidable. We give the first nontrivial lower bound for the problem by showing (our main result) that it is exponential time hard. Furthermore, we show that this holds even under rank 1 solutions (substitutions whose codomains are restricted to contain rank 1 types). In addition, we provide a fixed-parameter intractability result for intersection type matching (one-sided unification), which is known to be NP-complete. We place the algebraic intersection type unification problem in the context of unification theory. The equational theory of intersection types can be presented as an algebraic theory with an ACI (associative, commutative, and idempotent) operator (intersection type) combined with distributivity properties with respect to a second operator (function type). Although the problem is algebraically natural and interesting, it appears to occupy a hitherto unstudied place in the theory of unification, and our investigation of the problem suggests that new methods are required to understand the problem. Thus, for the lower bound proof, we were not able to reduce from known results in ACI-unification theory and use game-theoretic methods for two-player tiling games

LiteMat: a scalable, cost-efficient inference encoding scheme for large RDF graphs

The number of linked data sources and the size of the linked open data graph keep growing every day. As a consequence, semantic RDF services are more and more confronted with various "big data" problems. Query processing in the presence of inferences is one them. For instance, to complete the answer set of SPARQL queries, RDF database systems evaluate semantic RDFS relationships (subPropertyOf, subClassOf) through time-consuming query rewriting algorithms or space-consuming data materialization solutions. To reduce the memory footprint and ease the exchange of large datasets, these systems generally apply a dictionary approach for compressing triple data sizes by replacing resource identifiers (IRIs), blank nodes and literals with integer values. In this article, we present a structured resource identification scheme using a clever encoding of concepts and property hierarchies for efficiently evaluating the main common RDFS entailment rules while minimizing triple materialization and query rewriting. We will show how this encoding can be computed by a scalable parallel algorithm and directly be implemented over the Apache Spark framework. The efficiency of our encoding scheme is emphasized by an evaluation conducted over both synthetic and real world datasets.Comment: 8 pages, 1 figur

First-order theory of subtyping constraints

We investigate the first-order theory of subtyping constraints. We show that the first-order theory of non-structural subtyping is undecidable, and we show that in the case where all constructors are either unary or nullary, the first-order theory is decidable for both structural and non-structural subtyping. The decidability results are shown by reduction to a decision problem on tree automata. This work is a step towards resolving long-standing open problems of the decidability of entailment for non-structural subtyping

Non-structural subtype entailment in automata theory

Decidability of non-structural subtype entailment is a long standing open problem in programming language theory. In this paper, we apply automata theoretic methods to characterize the problem equivalently by using regular expressions and word equations. This characterization induces new results on non-structural subtype entailment, constitutes a promising starting point for further investigations on decidability, and explains for the first time why the problem is so difficult. The difficulty is caused by implicit word equations that we make explicit

First-order theory of subtyping constraints

We investigate the first-order theory of subtyping constraints. We show that the first-order theory of non-structural subtyping is undecidable, and we show that in the case where all constructors are either unary or nullary, the first-order theory is decidable for both structural and non-structural subtyping. The decidability results are shown by reduction to a decision problem on tree automata. This work is a step towards resolving long-standing open problems of the decidability of entailment for non-structural subtyping

A contextual behavioral approach to the study of (persecutory) delusions

Throughout the past century the topic of delusions has mainly been studied by researchers operating at the mental level of analysis. According to this perspective, delusional beliefs, as well as their emergence and persistence, stem from an interplay between (dysfunctional) mental representations and processes. Our paper aims to provide a starting point for researchers and clinicians interested in examining the topic of delusions from a functional-analytic perspective. We begin with a brief review of the research literature with a particular focus on persecutory delusions. Thereafter we introduce Contextual Behavioral Science (CBS), Relational Frame Theory (RFT) and a behavioral phenomenon known as arbitrarily applicable relational responding (AARR). Drawing upon AARR, and recent empirical developments within CBS, we argue that (persecutory) delusions may be conceptualized, studied and influenced using a functional-analytic approach. We consider future directions for research in this area as well as clinical interventions aimed at influencing delusions and their expression

Making AI Meaningful Again

Artificial intelligence (AI) research enjoyed an initial period of enthusiasm in the 1970s and 80s. But this enthusiasm was tempered by a long interlude of frustration when genuinely useful AI applications failed to be forthcoming. Today, we are experiencing once again a period of enthusiasm, fired above all by the successes of the technology of deep neural networks or deep machine learning. In this paper we draw attention to what we take to be serious problems underlying current views of artificial intelligence encouraged by these successes, especially in the domain of language processing. We then show an alternative approach to language-centric AI, in which we identify a role for philosophy