Solving computational learning problems of boolean formulae on DNA computers

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

We apply a DNA-based massively parallel exhaustive search to solving the computational learning problems of DNF (disjunctive normal form) Boolean formulae. Learning DNF formulae from examples is one of the most important open problems in computational learning theory and the problem of learning 3-term DNF formulae is known as intractable if RP 6 ≠ NP. We propose new methods to encode any k-term DNF formula to a DNA strand, evaluate the encoded DNF formula for a truth-value assignment by using hybridization and PCR, and find a consistent DNF formula with the given examples. By employing these methods, we show that the class of k-term DNF formulae (for any constant k) and the class of general DNF formulae are eficiently learnable on DNA computer.

Original languageEnglish
Title of host publicationDNA Computing - 6th International Workshop on DNA-Based Computers, DNA 2000 Leiden, The Netherlands, June 13-17, 2000 Revised Papers
EditorsGrzegorz Rozenberg, Anne Condon
PublisherSpringer Verlag
Pages220-230
Number of pages11
ISBN (Print)3540420762, 9783540420767
DOIs
Publication statusPublished - 2001 Jan 1
Externally publishedYes
Event6th International Workshop on DNA-Based Computers, DNA 2000 - Leiden, Netherlands
Duration: 2000 Jun 132000 Jun 17

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2054
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th International Workshop on DNA-Based Computers, DNA 2000
CountryNetherlands
CityLeiden
Period00/6/1300/6/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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