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The jobs puzzle: Taking on the challenge via controlled natural language processing

Published online by Cambridge University Press:  25 September 2013

ROLF SCHWITTER
ROLF SCHWITTER*
Affiliation:
Macquarie University, Department of Computing, Sydney NSW 2109, Australia (e-mail: Rolf.Schwitter@mq.edu.au)
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Abstract

In this paper we take on Stuart C. Shapiro's challenge of solving the Jobs Puzzle automatically and do this via controlled natural language processing. Instead of encoding the puzzle in a formal language that might be difficult to use and understand, we employ a controlled natural language as a high-level specification language that adheres closely to the original notation of the puzzle and allows us to reconstruct the puzzle in a machine-processable way and add missing and implicit information to the problem description. We show how the resulting specification can be translated into an answer set program and be processed by a state-of-the-art answer set solver to find the solutions to the puzzle.

Keywords

Controlled natural languageknowledge representationanswer set programming
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 13 , Issue 4-5: 29th International Conference on Logic Programming , July 2013 , pp. 487 - 501
Copyright
Copyright © 2013 [ROLF SCHWITTER] 

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