Efficient Intent-Based Narrative Generation Using Multiple Planning Agents

Teutenberg, J and Porteous, J 2013, 'Efficient Intent-Based Narrative Generation Using Multiple Planning Agents', in Takayuki Ito, Catholijn Jonker, Maria Gini, Onn Shehory (ed.) Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013), Saint Paul, Minnesota, United States, 6-10 May 2013, pp. 603-610.


Document type: Conference Paper
Collection: Conference Papers

Title Efficient Intent-Based Narrative Generation Using Multiple Planning Agents
Author(s) Teutenberg, J
Porteous, J
Year 2013
Conference name AAMAS 2013
Conference location Saint Paul, Minnesota, United States
Conference dates 6-10 May 2013
Proceedings title Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2013)
Editor(s) Takayuki Ito, Catholijn Jonker, Maria Gini, Onn Shehory
Publisher Association for Computing Machinery
Place of publication New York, United States
Start page 603
End page 610
Total pages 8
Abstract In Interactive Storytelling (IS) the prevailing approach for the automatic generation of plausible narratives that meet global author goals is intentional planning. However, existing approaches suffer from limited expressiveness and poor scalability. We address this by replacing single intentional planners with multiple agents representing the characters of a narrative, which can reason about the relevance of narrative actions given their individual intents. These are then combined using a state-based forward search procedure that results in a significantly smaller search space. Unlike other multiagent approaches, these agents calculate all reasonable plans in a state.This allows a search of a wide range of narrative possibilities prior to execution as in planner-based approaches, rather than agents making early plan commitments in a simulation. We demonstrate that this not only produces the same forms of narrative as single intentional planners but can be extended to generate narratives that are beyond their scope. We also present a search heuristic that exploits the agents' relevant actions to further reduce the size of the explored search space. Experimental results demonstrate system performance that makes it suitable for use in real-time applications such as IS.
Subjects Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) Interactive Storytelling
Agents in games and virtual environments
Narrative Modelling
Planning
Copyright notice Copyright © 2013 by the International Foundation for Autonomous Agents and Multiagent Systems All rights reserved.
ISBN 9781627487665
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