1,750 research outputs found
Finite and infinite support in nominal algebra and logic: nominal completeness theorems for free
By operations on models we show how to relate completeness with respect to
permissive-nominal models to completeness with respect to nominal models with
finite support. Models with finite support are a special case of
permissive-nominal models, so the construction hinges on generating from an
instance of the latter, some instance of the former in which sufficiently many
inequalities are preserved between elements. We do this using an infinite
generalisation of nominal atoms-abstraction.
The results are of interest in their own right, but also, we factor the
mathematics so as to maximise the chances that it could be used off-the-shelf
for other nominal reasoning systems too. Models with infinite support can be
easier to work with, so it is useful to have a semi-automatic theorem to
transfer results from classes of infinitely-supported nominal models to the
more restricted class of models with finite support.
In conclusion, we consider different permissive-nominal syntaxes and nominal
models and discuss how they relate to the results proved here
Representation and duality of the untyped lambda-calculus in nominal lattice and topological semantics, with a proof of topological completeness
We give a semantics for the lambda-calculus based on a topological duality
theorem in nominal sets. A novel interpretation of lambda is given in terms of
adjoints, and lambda-terms are interpreted absolutely as sets (no valuation is
necessary)
From nominal sets binding to functions and lambda-abstraction: connecting the logic of permutation models with the logic of functions
Permissive-Nominal Logic (PNL) extends first-order predicate logic with
term-formers that can bind names in their arguments. It takes a semantics in
(permissive-)nominal sets. In PNL, the forall-quantifier or lambda-binder are
just term-formers satisfying axioms, and their denotation is functions on
nominal atoms-abstraction.
Then we have higher-order logic (HOL) and its models in ordinary (i.e.
Zermelo-Fraenkel) sets; the denotation of forall or lambda is functions on full
or partial function spaces.
This raises the following question: how are these two models of binding
connected? What translation is possible between PNL and HOL, and between
nominal sets and functions?
We exhibit a translation of PNL into HOL, and from models of PNL to certain
models of HOL. It is natural, but also partial: we translate a restricted
subsystem of full PNL to HOL. The extra part which does not translate is the
symmetry properties of nominal sets with respect to permutations. To use a
little nominal jargon: we can translate names and binding, but not their
nominal equivariance properties. This seems reasonable since HOL---and ordinary
sets---are not equivariant.
Thus viewed through this translation, PNL and HOL and their models do
different things, but they enjoy non-trivial and rich subsystems which are
isomorphic
The language of Stratified Sets is confluent and strongly normalising
We study the properties of the language of Stratified Sets (first-order logic
with and a stratification condition) as used in TST, TZT, and (with
stratifiability instead of stratification) in Quine's NF. We find that the
syntax forms a nominal algebra for substitution and that stratification and
stratifiability imply confluence and strong normalisation under rewrites
corresponding naturally to -conversion.Comment: arXiv admin note: text overlap with arXiv:1406.406
Consistency of Quine's New Foundations using nominal techniques
We build a model in nominal sets for TST+; typed set theory with typical
ambiguity. It is known that this is equivalent to the consistency of Quine's
New Foundations.
Nominal techniques are used to constrain the size of powersets and thus model
typical ambiguity
Equivariant ZFA with Choice: a position paper
We propose Equivariant ZFA with Choice as a foundation for nominal techniques
that is stronger than ZFC and weaker than FM, and why this may be particularly
helpful in the context of automated reasoning.Comment: In ARW 201
Semantics out of context: nominal absolute denotations for first-order logic and computation
Call a semantics for a language with variables absolute when variables map to
fixed entities in the denotation. That is, a semantics is absolute when the
denotation of a variable a is a copy of itself in the denotation. We give a
trio of lattice-based, sets-based, and algebraic absolute semantics to
first-order logic. Possibly open predicates are directly interpreted as lattice
elements / sets / algebra elements, subject to suitable interpretations of the
connectives and quantifiers. In particular, universal quantification "forall
a.phi" is interpreted using a new notion of "fresh-finite" limit and using a
novel dual to substitution.
The interest of this semantics is partly in the non-trivial and beautiful
technical details, which also offer certain advantages over existing
semantics---but also the fact that such semantics exist at all suggests a new
way of looking at variables and the foundations of logic and computation, which
may be well-suited to the demands of modern computer science
Closed nominal rewriting and efficiently computable nominal algebra equality
We analyse the relationship between nominal algebra and nominal rewriting,
giving a new and concise presentation of equational deduction in nominal
theories. With some new results, we characterise a subclass of equational
theories for which nominal rewriting provides a complete procedure to check
nominal algebra equality. This subclass includes specifications of the
lambda-calculus and first-order logic.Comment: In Proceedings LFMTP 2010, arXiv:1009.218
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Abductive reasoning in neural-symbolic learning systems
Abduction is or subsumes a process of inference. It entertains possible hypotheses and it chooses hypotheses for further scrutiny. There is a large literature on various aspects of non-symbolic, subconscious abduction. There is also a very active research community working on the symbolic (logical) characterisation of abduction, which typically treats it as a form of hypothetico-deductive reasoning. In this paper we start to bridge the gap between the symbolic and sub-symbolic approaches to abduction. We are interested in benefiting from developments made by each community. In particular, we are interested in the ability of non-symbolic systems (neural networks) to learn from experience using efficient algorithms and to perform massively parallel computations of alternative abductive explanations. At the same time, we would like to benefit from the rigour and semantic clarity of symbolic logic. We present two approaches to dealing with abduction in neural networks. One of them uses Connectionist Modal Logic and a translation of Horn clauses into modal clauses to come up with a neural network ensemble that computes abductive explanations in a top-down fashion. The other combines neural-symbolic systems and abductive logic programming and proposes a neural architecture which performs a more systematic, bottom-up computation of alternative abductive explanations. Both approaches employ standard neural network architectures which are already known to be highly effective in practical learning applications. Differently from previous work in the area, our aim is to promote the integration of reasoning and learning in a way that the neural network provides the machinery for cognitive computation, inductive learning and hypothetical reasoning, while logic provides the rigour and explanation capability to the systems, facilitating the interaction with the outside world. Although it is left as future work to determine whether the structure of one of the proposed approaches is more amenable to learning than the other, we hope to have contributed to the development of the area by approaching it from the perspective of symbolic and sub-symbolic integration
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