Cyclic Proofs for First-Order Logic with Inductive Definitions

2005 ◽  
pp. 78-92 ◽  
Author(s):  
James Brotherston
Keyword(s):  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Inductive Definitions

μ-definable sets of integers

1993 ◽  
Vol 58 (1) ◽  
pp. 291-313 ◽  
Author(s):  
Robert S. Lubarsky
Keyword(s):  
Fixed Point ◽  
Free Variable ◽  
Order Logic ◽  
First Order Logic ◽  
Inductive Definition ◽  
Natural Class ◽  
First Order ◽  
Inductive Definitions ◽  
Definable Sets ◽  
The Universe

Inductive definability has been studied for some time already. Nonetheless, there are some simple questions that seem to have been overlooked. In particular, there is the problem of the expressibility of the μ-calculus.The μ-calculus originated with Scott and DeBakker [SD] and was developed by Hitchcock and Park [HP], Park [Pa], Kozen [K], and others. It is a language for including inductive definitions with first-order logic. One can think of a formula in first-order logic (with one free variable) as defining a subset of the universe, the set of elements that make it true. Then “and” corresponds to intersection, “or” to union, and “not” to complementation. Viewing the standard connectives as operations on sets, there is no reason not to include one more: least fixed point.There are certain features of the μ-calculus coming from its being a language that make it interesting. A natural class of inductive definitions are those that are monotone: if X ⊃ Y then Γ (X) ⊃ Γ (Y) (where Γ (X) is the result of one application of the operator Γ to the set X). When studying monotonic operations in the context of a language, one would need a syntactic guarantor of monotonicity. This is provided by the notion of positivity. An occurrence of a set variable S is positive if that occurrence is in the scopes of exactly an even number of negations (the antecedent of a conditional counting as a negation). S is positive in a formula ϕ if each occurrence of S is positive. Intuitively, the formula can ask whether x ∊ S, but not whether x ∉ S. Such a ϕ can be considered an inductive definition: Γ (X) = {x ∣ ϕ(x), where the variable S is interpreted as X}. Moreover, this induction is monotone: as X gets bigger, ϕ can become only more true, by the positivity of S in ϕ. So in the μ-calculus, a formula is well formed by definition only if all of its inductive definitions are positive, in order to guarantee that all inductive definitions are monotone.

scholarly journals Compiling Input* FO(·) inductive definitions into tabled prolog rules for IDP3

2013 ◽  
Vol 13 (4-5) ◽  
pp. 691-704 ◽  
Author(s):  
JOACHIM JANSEN ◽  
ALBERT JORISSEN ◽  
GERDA JANSSENS
Keyword(s):  
Order Logic ◽  
Experimental Results ◽  
First Order Logic ◽  
Partial Functions ◽  
First Order ◽  
Inductive Definitions ◽  
Input Structure ◽  
Model Generator ◽  
Unit Propagation

AbstractFO(·)IDP3extends first-order logic with inductive definitions, partial functions, types and aggregates. Its model generator IDP3 first grounds the theory and then uses search to find the models. The grounder uses Lifted Unit Propagation (LUP) to reduce the size of the groundings of problem specifications in IDP3. LUP is in general very effective, but performs poorly on definitions of predicates whose two-valued interpretation can be computed from data in the input structure. To solve this problem, a preprocessing step is introduced that converts such definitions to Prolog code and usesXSBProlog to compute their interpretation. The interpretation of these predicates is then added to the input structure, their definitions are removed from the theory and further processing is done by the standard IDP3 system. Experimental results show the effectiveness of our method.

scholarly journals First Order Logic with Inductive Definitions for Model-Based Problem Solving

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 69-80 ◽  
Author(s):  
Maurice Bruynooghe ◽  
Marc Denecker ◽  
Miroslaw Truszczynski
Keyword(s):  
Problem Solving ◽  
Answer Set Programming ◽  
Order Logic ◽  
First Order Logic ◽  
Declarative Programming ◽  
First Order ◽  
Inductive Definitions ◽  
Trace Back ◽  
Programming Systems ◽  
Different Origins

In answer-set programming (ASP), programs can be viewed as specifications of finite Herbrand structures. Other logics can be (and, in fact, were) used towards the same end and can be taken as the basis of declarative programming systems of similar functionality as ASP. We discuss here one such logic, the logic FO(ID), and its implementation IDP3. The choice is motivated by notable similarities between ASP and FO(ID), even if both approaches trace back to different origins

scholarly journals A Complete Cyclic Proof System for Inductive Entailments in First Order Logic

10.29007/xgc6 ◽  
2018 ◽  
Author(s):  
Radu Iosif ◽  
Cristina Serban
Keyword(s):  
Order Logic ◽  
First Order Logic ◽  
Proof System ◽  
Inference Rules ◽  
Tree Automata ◽  
Top Down ◽  
Inductive System ◽  
First Order ◽  
Inductive Definitions ◽  
Small Set

In this paper we develop a cyclic proof system for the problem of inclusion between the least sets of models of mutually recursive predicates, when the ground constraints in the inductive definitions are quantifier-free formulae of first order logic. The proof system consists of a small set of inference rules, inspired by a top-down language inclusion algorithm for tree automata [9]. We show the proof system to be sound, in general, and complete, under certain semantic restrictions involving the set of constraints in the inductive system. Moreover, we investigate the computational complexity of checking these restrictions, when the function symbols in the logic are given the canonical Herbrand interpretation.

First-Order Logic Reasoning Support for the Semantic Web

2009 ◽  
Vol 19 (12) ◽  
pp. 3091-3099 ◽  
Author(s):  
Gui-Hong XU ◽  
Jian ZHANG
Keyword(s):  
Semantic Web ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Logic Reasoning

Boolean-valued structures

2018 ◽  
Author(s):  
Tim Button ◽  
Sean Walsh
Keyword(s):  
Model Theory ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Rules ◽  
Mathematical Concepts ◽  
Semantic Framework ◽  
First Order ◽  
Standard Models ◽  
Boolean Valued Model ◽  
Semantic Values

Chapters 6-12 are driven by questions about the ability to pin down mathematical entities and to articulate mathematical concepts. This chapter is driven by similar questions about the ability to pin down the semantic frameworks of language. It transpires that there are not just non-standard models, but non-standard ways of doing model theory itself. In more detail: whilst we normally outline a two-valued semantics which makes sentences True or False in a model, the inference rules for first-order logic are compatible with a four-valued semantics; or a semantics with countably many values; or what-have-you. The appropriate level of generality here is that of a Boolean-valued model, which we introduce. And the plurality of possible semantic values gives rise to perhaps the ‘deepest’ level of indeterminacy questions: How can humans pin down the semantic framework for their languages? We consider three different ways for inferentialists to respond to this question.

scholarly journals Extensions in graph normal form

2020 ◽  
Author(s):  
Michał Walicki
Keyword(s):  
Normal Form ◽  
Fixed Points ◽  
Propositional Logic ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Special Cases

Abstract Graph normal form, introduced earlier for propositional logic, is shown to be a normal form also for first-order logic. It allows to view syntax of theories as digraphs, while their semantics as kernels of these digraphs. Graphs are particularly well suited for studying circularity, and we provide some general means for verifying that circular or apparently circular extensions are conservative. Traditional syntactic means of ensuring conservativity, like definitional extensions or positive occurrences guaranteeing exsitence of fixed points, emerge as special cases.

Relational Chase Procedures Interpreted as Resolution with Paramodulation1

1991 ◽  
Vol 15 (2) ◽  
pp. 123-138
Author(s):  
Joachim Biskup ◽  
Bernhard Convent
Keyword(s):  
Alternative Interpretation ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Problem ◽  
First Order ◽  
Inference Problems ◽  
The One ◽  
The Relationship ◽  
Theoretical Foundations ◽  
Insight Into

In this paper the relationship between dependency theory and first-order logic is explored in order to show how relational chase procedures (i.e., algorithms to decide inference problems for dependencies) can be interpreted as clever implementations of well known refutation procedures of first-order logic with resolution and paramodulation. On the one hand this alternative interpretation provides a deeper insight into the theoretical foundations of chase procedures, whereas on the other hand it makes available an already well established theory with a great amount of known results and techniques to be used for further investigations of the inference problem for dependencies. Our presentation is a detailed and careful elaboration of an idea formerly outlined by Grant and Jacobs which up to now seems to be disregarded by the database community although it definitely deserves more attention.

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