Representing and Learning Grammars in Answer Set Programming


  • Mark Law Imperial College London
  • Alessandra Russo Imperial College London
  • Elisa Bertino Purdue University
  • Krysia Broda Imperial College London
  • Jorge Lobo Universitat Pompeo Fabra



In this paper we introduce an extension of context-free grammars called answer set grammars (ASGs). These grammars allow annotations on production rules, written in the language of Answer Set Programming (ASP), which can express context-sensitive constraints. We investigate the complexity of various classes of ASG with respect to two decision problems: deciding whether a given string belongs to the language of an ASG and deciding whether the language of an ASG is non-empty. Specifically, we show that the complexity of these decision problems can be lowered by restricting the subset of the ASP language used in the annotations. To aid the applicability of these grammars to computational problems that require context-sensitive parsers for partially known languages, we propose a learning task for inducing the annotations of an ASG. We characterise the complexity of this task and present an algorithm for solving it. An evaluation of a (prototype) implementation is also discussed.




How to Cite

Law, M., Russo, A., Bertino, E., Broda, K., & Lobo, J. (2019). Representing and Learning Grammars in Answer Set Programming. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2919-2928.



AAAI Technical Track: Knowledge Representation and Reasoning