Linear Logic Programming for Narrative Generation

Chris Martens; Anne-Gwenn Bosser; João F. Ferreira; Marc Cavazza

doi:10.1007/978-3-642-40564-8_42

Linear Logic Programming for Narrative Generation

Chris Martens, Anne-Gwenn Bosser, João F. Ferreira, Marc Cavazza

SCEDT School Executive Team

Research output: Contribution to journal › Conference article › peer-review

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Abstract

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.

Original language	English
Pages (from-to)	427-432
Journal	Lecture Notes in Artificial Intelligence
Volume	8148
DOIs	https://doi.org/10.1007/978-3-642-40564-8_42
Publication status	Published - 2013
Event	12th International Conference - Logic Programming and Nonmonotonic Reasoning - Corunna, Spain Duration: 15 Sept 2013 → 19 Sept 2013 Conference number: 12

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10.1007/978-3-642-40564-8_42

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Cite this

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title = "Linear Logic Programming for Narrative Generation",

abstract = "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.",

author = "Chris Martens and Anne-Gwenn Bosser and Ferreira, {Jo{\~a}o F.} and Marc Cavazza",

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AU - Bosser, Anne-Gwenn

AU - Ferreira, João F.

AU - Cavazza, Marc

N1 - Conference code: 12

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-642-40564-8_42

DO - 10.1007/978-3-642-40564-8_42

M3 - Conference article

SN - 0302-9743

VL - 8148

SP - 427

EP - 432

JO - Lecture Notes in Artificial Intelligence

JF - Lecture Notes in Artificial Intelligence

T2 - 12th International Conference - Logic Programming and Nonmonotonic Reasoning

Y2 - 15 September 2013 through 19 September 2013

ER -

Linear Logic Programming for Narrative Generation

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