398 research outputs found
Proof-checking Euclid
We used computer proof-checking methods to verify the correctness of our
proofs of the propositions in Euclid Book I. We used axioms as close as
possible to those of Euclid, in a language closely related to that used in
Tarski's formal geometry. We used proofs as close as possible to those given by
Euclid, but filling Euclid's gaps and correcting errors. Euclid Book I has 48
propositions, we proved 235 theorems. The extras were partly "Book Zero",
preliminaries of a very fundamental nature, partly propositions that Euclid
omitted but were used implicitly, partly advanced theorems that we found
necessary to fill Euclid's gaps, and partly just variants of Euclid's
propositions. We wrote these proofs in a simple fragment of first-order logic
corresponding to Euclid's logic, debugged them using a custom software tool,
and then checked them in the well-known and trusted proof checkers HOL Light
and Coq.Comment: 53 page
Graph Representations for Higher-Order Logic and Theorem Proving
This paper presents the first use of graph neural networks (GNNs) for
higher-order proof search and demonstrates that GNNs can improve upon
state-of-the-art results in this domain. Interactive, higher-order theorem
provers allow for the formalization of most mathematical theories and have been
shown to pose a significant challenge for deep learning. Higher-order logic is
highly expressive and, even though it is well-structured with a clearly defined
grammar and semantics, there still remains no well-established method to
convert formulas into graph-based representations. In this paper, we consider
several graphical representations of higher-order logic and evaluate them
against the HOList benchmark for higher-order theorem proving
An Introduction to Mechanized Reasoning
Mechanized reasoning uses computers to verify proofs and to help discover new
theorems. Computer scientists have applied mechanized reasoning to economic
problems but -- to date -- this work has not yet been properly presented in
economics journals. We introduce mechanized reasoning to economists in three
ways. First, we introduce mechanized reasoning in general, describing both the
techniques and their successful applications. Second, we explain how mechanized
reasoning has been applied to economic problems, concentrating on the two
domains that have attracted the most attention: social choice theory and
auction theory. Finally, we present a detailed example of mechanized reasoning
in practice by means of a proof of Vickrey's familiar theorem on second-price
auctions
A Formal Proof of PAC Learnability for Decision Stumps
We present a formal proof in Lean of probably approximately correct (PAC)
learnability of the concept class of decision stumps. This classic result in
machine learning theory derives a bound on error probabilities for a simple
type of classifier. Though such a proof appears simple on paper, analytic and
measure-theoretic subtleties arise when carrying it out fully formally. Our
proof is structured so as to separate reasoning about deterministic properties
of a learning function from proofs of measurability and analysis of
probabilities.Comment: 13 pages, appeared in Certified Programs and Proofs (CPP) 202
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