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R. Goncalves, T. Janhunen, M. Knorr, J. Leite, S. Woltran:
"Forgetting in Modular Answer Set Programming";
Talk: AAAI 2019 - 33rd Conference on Artificial Intelligence, Honolulu, Hawaii; 2019-01-27 - 2019-02-01; in: "The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI} 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, {IAAI} 2019, The Ninth {AAAI} Symposium on Educational Advances in Artificial Intelligence, {EAAI} 2019", AAAI Press, (2019), ISBN: 978-1-57735-809-1; 2843 - 2850.

Modular programming facilitates the creation and reuse of large software, and has recently gathered considerable interest in the context of Answer Set Programming (ASP). In this setting, forgetting, or the elimination of middle variables no longer deemed relevant, is of importance as it allows one to, e.g., simplify a program, make it more declarative, or even hide some of its parts without affecting the consequences for those parts that are relevant. While forgetting in the context of ASP has been extensively studied, its known limitations make it unsuitable to be used in Modular ASP. In this paper, we present a novel class of forgetting operators and show that such operators can always be successfully applied in Modular ASP to forget all kinds of atoms - input, output and hidden - overcoming the impossibility results that exist for general ASP. Additionally, we investigate conditions under which this class of operators preserves the module theorem in Modular ASP, thus ensuring that answer sets of modules can still be composed, and how the module theorem can always be preserved if we further allow the reconfiguration of modules.

http://dx.doi.org/10.1609/aaai.v33i01.33012843

Project Head Stefan Woltran:
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