Law M, Russo A, Bertino A, Lobo J, Broda K. Representing and Learning Grammars in Answer Set Programming. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019)

In this paper we introduce an extension of context-free gram-mars 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 re-quire 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.