First Order Logic Beyond Propositional Logic
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
CS 512, Spring 2017, Handout 10 [1Ex] Propositional Logic: [2Ex
CS 512, Spring 2017, Handout 10 Propositional Logic: Conjunctive Normal Forms, Disjunctive Normal Forms, Horn Formulas, and other special forms Assaf Kfoury 5 February 2017 Assaf Kfoury, CS 512, Spring 2017, Handout 10 page 1 of 28 CNF L ::= p j :p literal D ::= L j L _ D disjuntion of literals C ::= D j D ^ C conjunction of disjunctions DNF L ::= p j :p literal C ::= L j L ^ C conjunction of literals D ::= C j C _ D disjunction of conjunctions conjunctive normal form & disjunctive normal form Assaf Kfoury, CS 512, Spring 2017, Handout 10 page 2 of 28 DNF L ::= p j :p literal C ::= L j L ^ C conjunction of literals D ::= C j C _ D disjunction of conjunctions conjunctive normal form & disjunctive normal form CNF L ::= p j :p literal D ::= L j L _ D disjuntion of literals C ::= D j D ^ C conjunction of disjunctions Assaf Kfoury, CS 512, Spring 2017, Handout 10 page 3 of 28 conjunctive normal form & disjunctive normal form CNF L ::= p j :p literal D ::= L j L _ D disjuntion of literals C ::= D j D ^ C conjunction of disjunctions DNF L ::= p j :p literal C ::= L j L ^ C conjunction of literals D ::= C j C _ D disjunction of conjunctions Assaf Kfoury, CS 512, Spring 2017, Handout 10 page 4 of 28 I A disjunction of literals L1 _···_ Lm is valid (equivalently, is a tautology) iff there are 1 6 i; j 6 m with i 6= j such that Li is :Lj. -
An Axiomatic Approach to Physical Systems
' An Axiomatic Approach $ to Physical Systems & % Januari 2004 ❦ ' Summary $ Mereology is generally considered as a formal theory of the part-whole relation- ship concerning material bodies, such as planets, pickles and protons. We argue that mereology can be considered more generally as axiomatising the concept of a physical system, such as a planet in a gravitation-potential, a pickle in heart- burn and a proton in an electro-magnetic field. We design a theory of sets and physical systems by extending standard set-theory (ZFC) with mereological axioms. The resulting theory deductively extends both `Mereology' of Tarski & L`esniewski as well as the well-known `calculus of individuals' of Leonard & Goodman. We prove a number of theorems and demonstrate that our theory extends ZFC con- servatively and hence equiconsistently. We also erect a model of our theory in ZFC. The lesson to be learned from this paper reads that not only is a marriage between standard set-theory and mereology logically respectable, leading to a rigorous vin- dication of how physicists talk about physical systems, but in addition that sets and physical systems interact at the formal level both quite smoothly and non- trivially. & % Contents 0 Pre-Mereological Investigations 1 0.0 Overview . 1 0.1 Motivation . 1 0.2 Heuristics . 2 0.3 Requirements . 5 0.4 Extant Mereological Theories . 6 1 Mereological Investigations 8 1.0 The Language of Physical Systems . 8 1.1 The Domain of Mereological Discourse . 9 1.2 Mereological Axioms . 13 1.2.0 Plenitude vs. Parsimony . 13 1.2.1 Subsystem Axioms . 15 1.2.2 Composite Physical Systems . -
An Algorithm for the Satisfiability Problem of Formulas in Conjunctive
Journal of Algorithms 54 (2005) 40–44 www.elsevier.com/locate/jalgor An algorithm for the satisfiability problem of formulas in conjunctive normal form Rainer Schuler Abt. Theoretische Informatik, Universität Ulm, D-89069 Ulm, Germany Received 26 February 2003 Available online 9 June 2004 Abstract We consider the satisfiability problem on Boolean formulas in conjunctive normal form. We show that a satisfying assignment of a formula can be found in polynomial time with a success probability − − + of 2 n(1 1/(1 logm)),wheren and m are the number of variables and the number of clauses of the formula, respectively. If the number of clauses of the formulas is bounded by nc for some constant c, − + this gives an expected run time of O(p(n) · 2n(1 1/(1 c logn))) for a polynomial p. 2004 Elsevier Inc. All rights reserved. Keywords: Complexity theory; NP-completeness; CNF-SAT; Probabilistic algorithms 1. Introduction The satisfiability problem of Boolean formulas in conjunctive normal form (CNF-SAT) is one of the best known NP-complete problems. The problem remains NP-complete even if the formulas are restricted to a constant number k>2 of literals in each clause (k-SAT). In recent years several algorithms have been proposed to solve the problem exponentially faster than the 2n time bound, given by an exhaustive search of all possible assignments to the n variables. So far research has focused in particular on k-SAT, whereas improvements for SAT in general have been derived with respect to the number m of clauses in a formula [2,7]. -
On the Boundary Between Mereology and Topology
On the Boundary Between Mereology and Topology Achille C. Varzi Istituto per la Ricerca Scientifica e Tecnologica, I-38100 Trento, Italy [email protected] (Final version published in Roberto Casati, Barry Smith, and Graham White (eds.), Philosophy and the Cognitive Sciences. Proceedings of the 16th International Wittgenstein Symposium, Vienna, Hölder-Pichler-Tempsky, 1994, pp. 423–442.) 1. Introduction Much recent work aimed at providing a formal ontology for the common-sense world has emphasized the need for a mereological account to be supplemented with topological concepts and principles. There are at least two reasons under- lying this view. The first is truly metaphysical and relates to the task of charac- terizing individual integrity or organic unity: since the notion of connectedness runs afoul of plain mereology, a theory of parts and wholes really needs to in- corporate a topological machinery of some sort. The second reason has been stressed mainly in connection with applications to certain areas of artificial in- telligence, most notably naive physics and qualitative reasoning about space and time: here mereology proves useful to account for certain basic relation- ships among things or events; but one needs topology to account for the fact that, say, two events can be continuous with each other, or that something can be inside, outside, abutting, or surrounding something else. These motivations (at times combined with others, e.g., semantic transpar- ency or computational efficiency) have led to the development of theories in which both mereological and topological notions play a pivotal role. How ex- actly these notions are related, however, and how the underlying principles should interact with one another, is still a rather unexplored issue. -
A Translation Approach to Portable Ontology Specifications
Knowledge Systems Laboratory September 1992 Technical Report KSL 92-71 Revised April 1993 A Translation Approach to Portable Ontology Specifications by Thomas R. Gruber Appeared in Knowledge Acquisition, 5(2):199-220, 1993. KNOWLEDGE SYSTEMS LABORATORY Computer Science Department Stanford University Stanford, California 94305 A Translation Approach to Portable Ontology Specifications Thomas R. Gruber Knowledge System Laboratory Stanford University 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how -
Automated Theorem Proving Introduction
Automated Theorem Proving Scott Sanner, Guest Lecture Topics in Automated Reasoning Thursday, Jan. 19, 2006 Introduction • Def. Automated Theorem Proving: Proof of mathematical theorems by a computer program. • Depending on underlying logic, task varies from trivial to impossible: – Simple description logic: Poly-time – Propositional logic: NP-Complete (3-SAT) – First-order logic w/ arithmetic: Impossible 1 Applications • Proofs of Mathematical Conjectures – Graph theory: Four color theorem – Boolean algebra: Robbins conjecture • Hardware and Software Verification – Verification: Arithmetic circuits – Program correctness: Invariants, safety • Query Answering – Build domain-specific knowledge bases, use theorem proving to answer queries Basic Task Structure • Given: – Set of axioms (KB encoded as axioms) – Conjecture (assumptions + consequence) • Inference: – Search through space of valid inferences • Output: – Proof (if found, a sequence of steps deriving conjecture consequence from axioms and assumptions) 2 Many Logics / Many Theorem Proving Techniques Focus on theorem proving for logics with a model-theoretic semantics (TBD) • Logics: – Propositional, and first-order logic – Modal, temporal, and description logic • Theorem Proving Techniques: – Resolution, tableaux, sequent, inverse – Best technique depends on logic and app. Example of Propositional Logic Sequent Proof • Given: • Direct Proof: – Axioms: (I) A |- A None (¬R) – Conjecture: |- ¬A, A ∨ A ∨ ¬A ? ( R2) A A ? |- A∨¬A, A (PR) • Inference: |- A, A∨¬A (∨R1) – Gentzen |- A∨¬A, A∨¬A Sequent (CR) ∨ Calculus |- A ¬A 3 Example of First-order Logic Resolution Proof • Given: • CNF: ¬Man(x) ∨ Mortal(x) – Axioms: Man(Socrates) ∀ ⇒ Man(Socrates) x Man(x) Mortal(x) ¬Mortal(y) [Neg. conj.] Man(Socrates) – Conjecture: • Proof: ∃y Mortal(y) ? 1. ¬Mortal(y) [Neg. conj.] 2. ¬Man(x) ∨ Mortal(x) [Given] • Inference: 3. -
Logic and Proof Computer Science Tripos Part IB
Logic and Proof Computer Science Tripos Part IB Lawrence C Paulson Computer Laboratory University of Cambridge [email protected] Copyright c 2015 by Lawrence C. Paulson Contents 1 Introduction and Learning Guide 1 2 Propositional Logic 2 3 Proof Systems for Propositional Logic 5 4 First-order Logic 8 5 Formal Reasoning in First-Order Logic 11 6 Clause Methods for Propositional Logic 13 7 Skolem Functions, Herbrand’s Theorem and Unification 17 8 First-Order Resolution and Prolog 21 9 Decision Procedures and SMT Solvers 24 10 Binary Decision Diagrams 27 11 Modal Logics 28 12 Tableaux-Based Methods 30 i 1 INTRODUCTION AND LEARNING GUIDE 1 1 Introduction and Learning Guide 2008 Paper 3 Q6: BDDs, DPLL, sequent calculus • 2008 Paper 4 Q5: proving or disproving first-order formulas, This course gives a brief introduction to logic, including • resolution the resolution method of theorem-proving and its relation 2009 Paper 6 Q7: modal logic (Lect. 11) to the programming language Prolog. Formal logic is used • for specifying and verifying computer systems and (some- 2009 Paper 6 Q8: resolution, tableau calculi • times) for representing knowledge in Artificial Intelligence 2007 Paper 5 Q9: propositional methods, resolution, modal programs. • logic The course should help you to understand the Prolog 2007 Paper 6 Q9: proving or disproving first-order formulas language, and its treatment of logic should be helpful for • 2006 Paper 5 Q9: proof and disproof in FOL and modal logic understanding other theoretical courses. It also describes • 2006 Paper 6 Q9: BDDs, Herbrand models, resolution a variety of techniques and data structures used in auto- • mated theorem provers. -
Boolean Algebra
Boolean Algebra Definition: A Boolean Algebra is a math construct (B,+, . , ‘, 0,1) where B is a non-empty set, + and . are binary operations in B, ‘ is a unary operation in B, 0 and 1 are special elements of B, such that: a) + and . are communative: for all x and y in B, x+y=y+x, and x.y=y.x b) + and . are associative: for all x, y and z in B, x+(y+z)=(x+y)+z, and x.(y.z)=(x.y).z c) + and . are distributive over one another: x.(y+z)=xy+xz, and x+(y.z)=(x+y).(x+z) d) Identity laws: 1.x=x.1=x and 0+x=x+0=x for all x in B e) Complementation laws: x+x’=1 and x.x’=0 for all x in B Examples: • (B=set of all propositions, or, and, not, T, F) • (B=2A, U, ∩, c, Φ,A) Theorem 1: Let (B,+, . , ‘, 0,1) be a Boolean Algebra. Then the following hold: a) x+x=x and x.x=x for all x in B b) x+1=1 and 0.x=0 for all x in B c) x+(xy)=x and x.(x+y)=x for all x and y in B Proof: a) x = x+0 Identity laws = x+xx’ Complementation laws = (x+x).(x+x’) because + is distributive over . = (x+x).1 Complementation laws = x+x Identity laws x = x.1 Identity laws = x.(x+x’) Complementation laws = x.x +x.x’ because + is distributive over . -
Normal Forms
Propositional Logic Normal Forms 1 Literals Definition A literal is an atom or the negation of an atom. In the former case the literal is positive, in the latter case it is negative. 2 Negation Normal Form (NNF) Definition A formula is in negation formal form (NNF) if negation (:) occurs only directly in front of atoms. Example In NNF: :A ^ :B Not in NNF: :(A _ B) 3 Transformation into NNF Any formula can be transformed into an equivalent formula in NNF by pushing : inwards. Apply the following equivalences from left to right as long as possible: ::F ≡ F :(F ^ G) ≡ (:F _:G) :(F _ G) ≡ (:F ^ :G) Example (:(A ^ :B) ^ C) ≡ ((:A _ ::B) ^ C) ≡ ((:A _ B) ^ C) Warning:\ F ≡ G ≡ H" is merely an abbreviation for \F ≡ G and G ≡ H" Does this process always terminate? Is the result unique? 4 CNF and DNF Definition A formula F is in conjunctive normal form (CNF) if it is a conjunction of disjunctions of literals: n m V Wi F = ( ( Li;j )), i=1 j=1 where Li;j 2 fA1; A2; · · · g [ f:A1; :A2; · · · g Definition A formula F is in disjunctive normal form (DNF) if it is a disjunction of conjunctions of literals: n m W Vi F = ( ( Li;j )), i=1 j=1 where Li;j 2 fA1; A2; · · · g [ f:A1; :A2; · · · g 5 Transformation into CNF and DNF Any formula can be transformed into an equivalent formula in CNF or DNF in two steps: 1. Transform the initial formula into its NNF 2. -
Solving the Boolean Satisfiability Problem Using the Parallel Paradigm Jury Composition
Philosophæ doctor thesis Hoessen Benoît Solving the Boolean Satisfiability problem using the parallel paradigm Jury composition: PhD director Audemard Gilles Professor at Universit´ed'Artois PhD co-director Jabbour Sa¨ıd Assistant Professor at Universit´ed'Artois PhD co-director Piette C´edric Assistant Professor at Universit´ed'Artois Examiner Simon Laurent Professor at University of Bordeaux Examiner Dequen Gilles Professor at University of Picardie Jules Vernes Katsirelos George Charg´ede recherche at Institut national de la recherche agronomique, Toulouse Abstract This thesis presents different technique to solve the Boolean satisfiability problem using parallel and distributed architec- tures. In order to provide a complete explanation, a careful presentation of the CDCL algorithm is made, followed by the state of the art in this domain. Once presented, two proposi- tions are made. The first one is an improvement on a portfo- lio algorithm, allowing to exchange more data without loosing efficiency. The second is a complete library with its API al- lowing to easily create distributed SAT solver. Keywords: SAT, parallelism, distributed, solver, logic R´esum´e Cette th`ese pr´esente diff´erentes techniques permettant de r´esoudre le probl`eme de satisfaction de formule bool´eenes utilisant le parall´elismeet du calcul distribu´e. Dans le but de fournir une explication la plus compl`ete possible, une pr´esentation d´etaill´ee de l'algorithme CDCL est effectu´ee, suivi d'un ´etatde l'art. De ce point de d´epart,deux pistes sont explor´ees. La premi`ereest une am´eliorationd'un algorithme de type portfolio, permettant d'´echanger plus d'informations sans perte d’efficacit´e. -
Semantics of First-Order Logic
CSC 244/444 Lecture Notes Sept. 7, 2021 Semantics of First-Order Logic A notation becomes a \representation" only when we can explain, in a formal way, how the notation can make true or false statements about some do- main; this is what model-theoretic semantics does in logic. We now have a set of syntactic expressions for formalizing knowledge, but we have only talked about the meanings of these expressions in an informal way. We now need to provide a model-theoretic semantics for this syntax, specifying precisely what the possible ways are of using a first-order language to talk about some domain of interest. Why is this important? Because, (1) that is the only way that we can verify that the facts we write down in the representation actually can mean what we intuitively intend them to mean; representations without formal semantics have often turned out to be incoherent when examined from this perspective; and (2) that is the only way we can judge whether proposed methods of making inferences are reasonable { i.e., that they give conclusions that are guaranteed to be true whenever the premises are, or at least are probably true, or at least possibly true. Without such guarantees, we will probably end up with an irrational \intelligent agent"! Models Our goal, then, is to specify precisely how all the terms, predicates, and formulas (sen- tences) of a first-order language can be interpreted so that terms refer to individuals, predicates refer to properties and relations, and formulas are either true or false. So we begin by fixing a domain of interest, called the domain of discourse: D. -
Extended Conjunctive Normal Form and an Efficient Algorithm For
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20) Extended Conjunctive Normal Form and An Efficient Algorithm for Cardinality Constraints Zhendong Lei1;2 , Shaowei Cai1;2∗ and Chuan Luo3 1State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China 2School of Computer Science and Technology, University of Chinese Academy of Sciences, China 3Microsoft Research, China fleizd, [email protected] Abstract efficient PMS solvers, including SAT-based solvers [Naro- dytska and Bacchus, 2014; Martins et al., 2014; Berg et Satisfiability (SAT) and Maximum Satisfiability al., 2019; Nadel, 2019; Joshi et al., 2019; Demirovic and (MaxSAT) are two basic and important constraint Stuckey, 2019] and local search solvers [Cai et al., 2016; problems with many important applications. SAT Luo et al., 2017; Cai et al., 2017; Lei and Cai, 2018; and MaxSAT are expressed in CNF, which is dif- Guerreiro et al., 2019]. ficult to deal with cardinality constraints. In this One of the most important drawbacks of these logical lan- paper, we introduce Extended Conjunctive Normal guages is the difficulty to deal with cardinality constraints. Form (ECNF), which expresses cardinality con- Indeed, cardinality constraints arise frequently in the encod- straints straightforward and does not need auxiliary ing of many real world situations such as scheduling, logic variables or clauses. Then, we develop a simple and synthesis or verification, product configuration and data min- efficient local search solver LS-ECNF with a well ing. For the above reasons, many works have been done on designed scoring function under ECNF. We also de- finding an efficient encoding of cardinality constraints in CNF velop a generalized Unit Propagation (UP) based formulas [Sinz, 2005; As´ın et al., 2009; Hattad et al., 2017; algorithm to generate the initial solution for local Boudane et al., 2018; Karpinski and Piotrow,´ 2019].