<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chris Martens</style></author><author><style face="normal" font="default" size="100%">Anne-Gwenn Bosser</style></author><author><style face="normal" font="default" size="100%">João F. Ferreira</style></author><author><style face="normal" font="default" size="100%">Marc Cavazza</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Linear Logic Programming for Narrative Generation</style></title><secondary-title><style face="normal" font="default" size="100%">Logic Programming and Nonmonotonic Reasoning (LNCS 8148)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this paper, we explore the use of Linear Logic programming for story generation. We use the language Celf to represent narrative knowledge, and its own querying mechanism to generate story instances, through a number of proof terms. Each proof term obtained is used, through a resource-flow analysis, to build a directed graph where nodes are narrative actions and edges represent inferred causality relationships. Such graphs represent narrative plots structured by narrative causality. This approach is a candidate technique for narrative generation which unifies declarative representations and generation via query and deduction mechanisms.&lt;/p&gt;
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