Exponentially Huge Natural Deduction proofs are Redundant: Preliminary results on M⊃M_\supset

preprint2020arXivOpen access

Abstract

We estimate the size of a labelled tree by comparing the amount of (labelled) nodes with the size of the set of labels. Roughly speaking, a exponentially big labelled tree, is any labelled tree that has an exponential gap between its size, number of nodes, and the size of its labelling set. The number of sub-formulas of any formula is linear on the size of it, and hence any exponentially big proof has a size ana^n, where a>1a>1 and nn is the size of its conclusion. In this article, we show that the linearly height labelled trees whose sizes have an exponential gap with the size of their labelling sets posses at least one sub-tree that occurs exponentially many times in them. Natural Deduction proofs and derivations in minimal implicational logic (M⊃M_\supset) are essentially labelled trees. By the sub-formula principle any normal derivation of a formula αα from a set of formulas Γ={γ1,…,γn}Γ=\{γ_1,\ldots,γ_n\} in M⊃M_\supset, establishing Γ⊢M⊃αΓ\vdash_{M_\supset}α, has only sub-formulas of the formulas α,γ1,…,γnα,γ_1,\ldots,γ_n occurring in it. By this relationship between labelled trees and derivations in M⊃M_\supset, we show that any normal proof of a tautology in M⊃M_\supset that is exponential on the size of its conclusion has a sub-proof that occurs exponentially many times in it. Thus, any normal and linearly height bounded proof in M⊃M_\supset is inherently redundant. Finally, we briefly discuss how this redundancy provides us with a highly efficient compression method for propositional proofs. We also provide some examples that serve to convince us that exponentially big proofs are more frequent than one can imagine.

Explore this paper’s authors, topics and related work.

Building this map preview

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Reviews 0

Write a reviewWrite

No reviews yet.

Discussion 0

Add a commentWrite

No comments yet.