Efficiently Implementing GOLOG with Answer Set Programming

AAAI 2014 pp. 2352-2357

doi:10.1609/AAAI.V28I1.9026 /aaai/2014/ryan2014aaai-efficiently/

Abstract

In this paper we investigate three different approaches to encoding domain-dependent control knowledge for Answer-Set Planning. Starting with a standard imple- mentation of the action description language B, we add control knowledge expressed in the GOLOG logic pro- gramming language. A naive encoding, following the original definitions of Levesque et al., is shown to scale poorly. We examine two alternative codings based on the transition semantics of ConGOLOG. We show that a speed increase of multiple orders of magnitude can be obtain by compiling the GOLOG program into a finite- state machine representation.

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Text

Ryan. "Efficiently Implementing GOLOG with Answer Set Programming." AAAI Conference on Artificial Intelligence, 2014. doi:10.1609/AAAI.V28I1.9026

Markdown

[Ryan. "Efficiently Implementing GOLOG with Answer Set Programming." AAAI Conference on Artificial Intelligence, 2014.](https://mlanthology.org/aaai/2014/ryan2014aaai-efficiently/) doi:10.1609/AAAI.V28I1.9026

BibTeX

@inproceedings{ryan2014aaai-efficiently,
  title     = {{Efficiently Implementing GOLOG with Answer Set Programming}},
  author    = {Ryan, Malcolm},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2014},
  pages     = {2352-2357},
  doi       = {10.1609/AAAI.V28I1.9026},
  url       = {https://mlanthology.org/aaai/2014/ryan2014aaai-efficiently/}
}