Martens C, Bosser A-G, Ferreira JF, Cavazza M.
2013.
Linear Logic Programming for Narrative Generation. Logic Programming and Nonmonotonic Reasoning (LNCS 8148).
AbstractIn 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.
Martens C, Ferreira JF, Bosser A-G, Cavazza M.
2014.
Generative Story Worlds as Linear Logic Programs. Intelligent Narrative Technologies 7 (INT7).
AbstractLinear logic programming languages have been identified in prior work as viable for specifying stories and analyzing their causal structure. We investigate the use of such a language for specifying story worlds, or settings where generalized narrative actions have uniform effects (not specific to a particular set of characters or setting elements), which may create emergent behavior through feedback loops. We show a sizable example of a story world specified in the language Celf and discuss its interpretation as a story-generating program, a simulation, and an interactive narrative. Further, we show that the causal analysis tools available by virtue of using a proof-theoretic language for specification can assist the author in reasoning about the structure and consequences of emergent stories.
