Interpolable Formulas in Equilibrium Logic and Answer Set Programming
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Stable Models and Circumscription
Stable Models and Circumscription Paolo Ferraris, Joohyung Lee, and Vladimir Lifschitz Abstract The concept of a stable model provided a declarative semantics for Prolog programs with negation as failure and became a starting point for the development of answer set programming. In this paper we propose a new definition of that concept, which covers many constructs used in answer set programming and, unlike the original definition, refers neither to grounding nor to fixpoints. It is based on a syntactic transformation similar to parallel circumscription. 1 Introduction Answer set programming (ASP) is a form of declarative logic programming oriented towards knowledge-intensive search problems, such as product con- figuration and planning. It was identified as a new programming paradigm ten years ago [Marek and Truszczy´nski,1999, Niemel¨a,1999], and it has found by now a number of serious applications. An ASP program consists of rules that are syntactically similar to Prolog rules, but the computational mechanisms used in ASP are different: they use the ideas that have led to the creation of fast satisfiability solvers for propositional logic [Gomes et al., 2008]. ASP is based on the concept of a stable model [Gelfond and Lifschitz, 1988]. According to the definition, to decide which sets of ground atoms are \stable models" of a given set of rules we first replace each of the given rules by all its ground instances. Then we verify a fixpoint condition that is similar to the conditions employed in the semantics of default logic [Reiter, 1980] and autoepistemic logic [Moore, 1985] (see [Lifschitz, 2008, Sections 4, 5] for details). -
Here the Handbook of Abstracts
Handbook of the 4th World Congress and School on Universal Logic March 29 { April 07, 2013 Rio de Janeiro, Brazil UNILOG'2013 www.uni-log.org Edited by Jean-Yves B´eziau,Arthur Buchsbaum and Alexandre Costa-Leite Revised by Alvaro Altair Contents 1 Organizers of UNILOG'13 5 1.1 Scientific Committee . .5 1.2 Organizing Committee . .5 1.3 Supporting Organizers . .6 2 Aim of the event 6 3 4th World School on Universal Logic 8 3.1 Aim of the School . .8 3.2 Tutorials . .9 3.2.1 Why Study Logic? . .9 3.2.2 How to get your Logic Article or Book published in English9 3.2.3 Non-Deterministic Semantics . 10 3.2.4 Logic for the Blind as a Stimulus for the Design of Inno- vative Teaching Materials . 13 3.2.5 Hybrid Logics . 16 3.2.6 Psychology of Reasoning . 17 3.2.7 Truth-Values . 18 3.2.8 The Origin of Indian Logic and Indian Syllogism . 23 3.2.9 Logical Forms . 24 3.2.10 An Introduction to Arabic Logic . 25 3.2.11 Quantum Cognition . 27 3.2.12 Towards a General Theory of Classifications . 28 3.2.13 Connecting Logics . 30 3.2.14 Relativity of Mathematical Concepts . 32 3.2.15 Undecidability and Incompleteness are Everywhere . 33 3.2.16 Logic, Algebra and Implication . 33 3.2.17 Hypersequents and Applications . 35 3.2.18 Introduction to Modern Mathematics . 36 3.2.19 Erotetic Logics . 37 3.2.20 History of Paraconsistent Logic . 38 3.2.21 Institutions . -
Answer Set Programming: a Primer?
Answer Set Programming: A Primer? Thomas Eiter1, Giovambattista Ianni2, and Thomas Krennwallner1 1 Institut fur¨ Informationssysteme, Technische Universitat¨ Wien Favoritenstraße 9-11, A-1040 Vienna, Austria feiter,[email protected] 2 Dipartimento di Matematica, Universita´ della Calabria, I-87036 Rende (CS), Italy [email protected] Abstract. Answer Set Programming (ASP) is a declarative problem solving para- digm, rooted in Logic Programming and Nonmonotonic Reasoning, which has been gaining increasing attention during the last years. This article is a gentle introduction to the subject; it starts with motivation and follows the historical development of the challenge of defining a semantics for logic programs with negation. It looks into positive programs over stratified programs to arbitrary programs, and then proceeds to extensions with two kinds of negation (named weak and strong negation), and disjunction in rule heads. The second part then considers the ASP paradigm itself, and describes the basic idea. It shows some programming techniques and briefly overviews Answer Set solvers. The third part is devoted to ASP in the context of the Semantic Web, presenting some formalisms and mentioning some applications in this area. The article concludes with issues of current and future ASP research. 1 Introduction Over the the last years, Answer Set Programming (ASP) [1–5] has emerged as a declar- ative problem solving paradigm that has its roots in Logic Programming and Non- monotonic Reasoning. This particular way of programming, in a language which is sometimes called AnsProlog (or simply A-Prolog) [6, 7], is well-suited for modeling and (automatically) solving problems which involve common sense reasoning: it has been fruitfully applied to a range of applications (for more details, see Section 6). -
Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives
information Article Logic-Based Technologies for Intelligent Systems: State of the Art and Perspectives Roberta Calegari 1,* , Giovanni Ciatto 2 , Enrico Denti 3 and Andrea Omicini 2 1 Alma AI—Alma Mater Research Institute for Human-Centered Artificial Intelligence, Alma Mater Studiorum–Università di Bologna, 40121 Bologna, Italy 2 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 47522 Cesena, Italy; [email protected] (G.C.); [email protected] (A.O.) 3 Dipartimento di Informatica–Scienza e Ingegneria (DISI), Alma Mater Studiorum–Università di Bologna, 40136 Bologna, Italy; [email protected] * Correspondence: [email protected] Received: 25 February 2020; Accepted: 18 March 2020; Published: 22 March 2020 Abstract: Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future. -
Reducts of Propositional Theories, Satisfiability Relations, And
Reducts of propositional theories, satisfiability relations, and generalizations of semantics of logic programs Mirosław Truszczynski´ Department of Computer Science, University of Kentucky, Lexington, KY 40506-0046, USA [email protected] Abstract. Over the years, the stable-model semantics has gained a position of the correct (two-valued) interpretation of default negation in programs. How- ever, for programs with aggregates (constraints), the stable-model semantics, in its broadly accepted generalization stemming from the work by Pearce, Ferraris and Lifschitz, has a competitor: the semantics proposed by Faber, Leone and Pfeifer, which seems to be essentially different. Our goal is to explain the rela- tionship between the two semantics. Pearce, Ferraris and Lifschitz’s extension of the stable-model semantics is best viewed in the setting of arbitrary propositional theories. We propose here an extension of the Faber-Leone-Pfeifer semantics, or FLP semantics, for short, to the full propositional language, which reveals both common threads and differences between the FLP and stable-model semantics. We use our characterizations of FLP-stable models to derive corresponding re- sults on strong equivalence and on normal forms of theories under the FLP se- mantics. We apply a similar approach to define supported models for arbitrary propositional theories, and to study their properties. 1 Introduction The stable-model semantics, introduced by Gelfond and Lifschitz [1], is the founda- tion of answer-set programming [2–4], a paradigm for modeling and solving search problems. From its inception, developing theoretical underpinnings of the stable-model semantics has been a major research objective. In particular, a contribution by Pearce [5] explained the stable-model semantics in terms of models of theories in the logic here-and-there (HT, for short), introduced by Heyting [6]. -
Introduction to Formal Logic II (3 Credit
PHILOSOPHY 115 INTRODUCTION TO FORMAL LOGIC II BULLETIN INFORMATION PHIL 115 – Introduction to Formal Logic II (3 credit hrs) Course Description: Intermediate topics in predicate logic, including second-order predicate logic; meta-theory, including soundness and completeness; introduction to non-classical logic. SAMPLE COURSE OVERVIEW Building on the material from PHIL 114, this course introduces new topics in logic and applies them to both propositional and first-order predicate logic. These topics include argument by induction, duality, compactness, and others. We will carefully prove important meta-logical results, including soundness and completeness for both propositional logic and first-order predicate logic. We will also learn a new system of proof, the Gentzen-calculus, and explore first-order predicate logic with functions and identity. Finally, we will discuss potential motivations for alternatives to classical logic, and examine how those motivations lead to the development of intuitionistic logic, including a formal system for intuitionistic logic, comparing the features of that formal system to those of classical first-order predicate logic. ITEMIZED LEARNING OUTCOMES Upon successful completion of PHIL 115, students will be able to: 1. Apply, as appropriate, principles of analytical reasoning, using as a foundation the knowledge of mathematical, logical, and algorithmic principles 2. Recognize and use connections among mathematical, logical, and algorithmic methods across disciplines 3. Identify and describe problems using formal symbolic methods and assess the appropriateness of these methods for the available data 4. Effectively communicate the results of such analytical reasoning and problem solving 5. Explain and apply important concepts from first-order logic, including duality, compactness, soundness, and completeness 6. -
Intuitionistic Logic
Intuitionistic Logic Nick Bezhanishvili and Dick de Jongh Institute for Logic, Language and Computation Universiteit van Amsterdam Contents 1 Introduction 2 2 Intuitionism 3 3 Kripke models, Proof systems and Metatheorems 8 3.1 Other proof systems . 8 3.2 Arithmetic and analysis . 10 3.3 Kripke models . 15 3.4 The Disjunction Property, Admissible Rules . 20 3.5 Translations . 22 3.6 The Rieger-Nishimura Lattice and Ladder . 24 3.7 Complexity of IPC . 25 3.8 Mezhirov's game for IPC . 28 4 Heyting algebras 30 4.1 Lattices, distributive lattices and Heyting algebras . 30 4.2 The connection of Heyting algebras with Kripke frames and topologies . 33 4.3 Algebraic completeness of IPC and its extensions . 38 4.4 The connection of Heyting and closure algebras . 40 5 Jankov formulas and intermediate logics 42 5.1 n-universal models . 42 5.2 Formulas characterizing point generated subsets . 44 5.3 The Jankov formulas . 46 5.4 Applications of Jankov formulas . 47 5.5 The logic of the Rieger-Nishimura ladder . 52 1 1 Introduction In this course we give an introduction to intuitionistic logic. We concentrate on the propositional calculus mostly, make some minor excursions to the predicate calculus and to the use of intuitionistic logic in intuitionistic formal systems, in particular Heyting Arithmetic. We have chosen a selection of topics that show various sides of intuitionistic logic. In no way we strive for a complete overview in this short course. Even though we approach the subject for the most part only formally, it is good to have a general introduction to intuitionism. -
Axiomatization of Logic. Truth and Proof. Resolution
H250: Honors Colloquium – Introduction to Computation Axiomatization of Logic. Truth and Proof. Resolution Marius Minea [email protected] How many do we need? (best: few) Are they enough? Can we prove everything? Axiomatization helps answer these questions Motivation: Determining Truth In CS250, we started with truth table proofs. Propositional formula: can always do truth table (even if large) Predicate formula: can’t do truth tables (infinite possibilities) ⇒ must use other proof rules Motivation: Determining Truth In CS250, we started with truth table proofs. Propositional formula: can always do truth table (even if large) Predicate formula: can’t do truth tables (infinite possibilities) ⇒ must use other proof rules How many do we need? (best: few) Are they enough? Can we prove everything? Axiomatization helps answer these questions Symbols of propositional logic: propositions: p, q, r (usually lowercase letters) operators (logical connectives): negation ¬, implication → parentheses () Formulas of propositional logic: defined by structural induction (how to build complex formulas from simpler ones) A formula (compound proposition) is: any proposition (aka atomic formula or variable) (¬α) where α is a formula (α → β) if α and β are formulas Implication and negation suffice! First, Define Syntax We define a language by its symbols and the rules to correctly combine symbols (the syntax) Formulas of propositional logic: defined by structural induction (how to build complex formulas from simpler ones) A formula (compound proposition) is: any -
Stable Models Stable Models (‘Answer Sets’) for a Normal Logic Program, a Stable Model Is a Set of Atoms
491 Knowledge Representation Stable models Stable models (‘Answer sets’) For a normal logic program, a stable model is a set of atoms. Let P be a ground normal logic program, i.e., one without variables. If P is not ground Marek Sergot (contains variables) then replace it by all the ground instances of its clauses. (The resulting Department of Computing set of clauses may not be finite.) Imperial College, London Notation When r is a (ground) clause of the form: February 2006 v1.2, November 2016 v1.2c A B ,...,B , not C ,..., not C ← 1 m 1 n + head(r) = A, body (r) = B1,...,Bm , body−(r) = C1,...,Cn . When P is a set of A normal logic program (sometimes a ‘general logic program’) is a set of clauses of the clauses, heads(P ) = head({r) r P .} { } form: { | ∈ } Suppose we have a set X of atoms from the language of P . The idea is that we use X to A L ,...,L (n 0) ← 1 n ≥ simplify P by ‘partially evaluating’ all clauses with nbf-literals against X, and then we see whether the simplified program P X we are left with (the ‘reduct’) has a least Herbrand where A is an atom and each Li is either an atom or a nbf-literal of the form not A where A is an atom. not denotes negation by failure. model that coincides with X. A model of a normal logic program P is a set of atoms X such that T (X) X, or P ⊆ equivalently, such that X is an (ordinary, classical) model of the clauses P ¬ obtained by Definition replacing every occurrence of not in P by ordinary, truth-functional negation . -
On the Intermediate Logic of Open Subsets of Metric Spaces Timofei Shatrov
On the intermediate logic of open subsets of metric spaces Timofei Shatrov abstract. In this paper we study the intermediate logic MLO( ) of open subsets of a metric space . This logic is closely related to Medvedev’sX logic X of finite problems ML. We prove several facts about this logic: its inclusion in ML, impossibility of its finite axiomatization and indistinguishability from ML within some large class of propositional formulas. Keywords: intermediate logic, Medvedev’s logic, axiomatization, indistin- guishability 1 Introduction In this paper we introduce and study a new intermediate logic MLO( ), which we first define using Kripke semantics and then we will try to ratio-X nalize this definition using the concepts from prior research in this field. An (intuitionistic) Kripke frame is a partially ordered set (F, ). A Kripke model is a Kripke frame with a valuation θ (a function which≤ maps propositional variables to upward-closed subsets of the Kripke frame). The notion of a propositional formula being true at some point w F of a model M = (F,θ) is defined recursively as follows: ∈ For propositional letter p , M, w p iff w θ(p ) i i ∈ i M, w ψ χ iff M, w ψ and M, w χ ∧ M, w ψ χ iff M, w ψ or M, w χ ∨ M, w ψ χ iff for any w′ w, M, w ψ implies M, w′ χ → ≥ M, w 6 ⊥ A formula is true in a model M if it is true in each point of M. A formula is valid in a frame F if it is true in all models based on that frame. -
A Program for the Semantics of Science
MARIO BUNGE A PROGRAM FOR THE SEMANTICS OF SCIENCE I. PROBLEM, METHOD AND GOAL So far exact semantics has been successful only in relation to logic and mathematics. It has had little if anything to say about factual or empirical science. Indeed, no semantical theory supplies an exact and adequate elucidation and systematization of the intuitive notions of factual referen- ce and factual representation, or of factual sense and partial truth of fact, which are peculiar to factual science and therefore central to its philosophy. The semantics of first order logic and the semantics of mathematics (i.e., model theory) do not handle those semantical notions, for they are not interested in external reference and in partial satisfaction. On the other hand factual science is not concerned with interpreting a theory in terms of another theory but in interpreting a theory by reference to things in the real world and their properties. Surely there have been attempts to tackle the semantic peculiarities of factual science. However, the results are rather poor. We have either vigorous intuitions that remain half-baked and scattered, or rigorous formalisms that are irrelevant to real science. The failure to pass from intuition to theory suggests that semanticists have not dealt with genuine factual science but with some oversimplified images of it, such as the view that a scientific theory is just a special case of set theory, so that model theory accounts for factual meaning and for truth of fact. If we wish to do justice to the semantic peculiarities of factual science we must not attempt to force it into any preconceived Procrustean bed: we must proceed from within science. -
Intermediate Logic
Intermediate Logic Richard Zach Philosophy 310 Winter Term 2015 McGill University Intermediate Logic by Richard Zach is licensed under a Creative Commons Attribution 4.0 International License. It is based on The Open Logic Text by the Open Logic Project, used under a Creative Commons Attribution 4.0 In- ternational License. Contents Prefacev I Sets, Relations, Functions1 1 Sets2 1.1 Basics ................................. 2 1.2 Some Important Sets......................... 3 1.3 Subsets ................................ 4 1.4 Unions and Intersections...................... 5 1.5 Pairs, Tuples, Cartesian Products ................. 7 1.6 Russell’s Paradox .......................... 9 2 Relations 10 2.1 Relations as Sets........................... 10 2.2 Special Properties of Relations................... 11 2.3 Orders................................. 12 2.4 Graphs ................................ 14 2.5 Operations on Relations....................... 15 3 Functions 17 3.1 Basics ................................. 17 3.2 Kinds of Functions.......................... 19 3.3 Inverses of Functions ........................ 20 3.4 Composition of Functions ..................... 21 3.5 Isomorphism............................. 22 3.6 Partial Functions........................... 22 3.7 Functions and Relations....................... 23 4 The Size of Sets 24 4.1 Introduction ............................. 24 4.2 Enumerable Sets........................... 24 4.3 Non-enumerable Sets........................ 28 i CONTENTS 4.4 Reduction..............................