Automated Story Sifting Using Story Arcs
Pages 1669 - 1671
Abstract
Story sifting (or story recognition) allows for the exploration of events, stories and patterns that emerge from agent-based simulations. The goal of this work is to automate and reduce the authoring burden for writing sifting queries. In this paper, we use the event traces of agent-based simulations to create Dynamic Character Networks that track the changing relationship scores between every agent in a simulation. These networks allow for the fortunes between any two agents to be plotted against time as a story arc. Similarity scores between story arcs from the simulation and a user's query arc can be calculated using the Dynamic Time Warping technique. Events corresponding to the story arc that best matches the query arc can then be returned to the user, thus providing an intuitive means for users to sift a variety of stories without coding a search query. These components are implemented in our experimental prototype Arc Sift. The results of a user study support our expectation that Arc Sift is an intuitive and accurate tool that allows human users to sift stories out from a larger chronicle of events produced by an agent-based simulation.
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Index Terms
- Automated Story Sifting Using Story Arcs
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Published In
May 2022
1990 pages
ISBN:9781450392136
- General Chairs:
- Catherine Pelachaud,
- Matthew E. Taylor,
- Program Chairs:
- Piotr Faliszewski,
- Viviana Mascardi
Sponsors
Publisher
International Foundation for Autonomous Agents and Multiagent Systems
Richland, SC
Publication History
Published: 09 May 2022
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- Extended-abstract
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- This research was supported by funding from the Commonwealth of Australia.
Conference
AAMAS ' 22
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AAMAS ' 22: International Conference on Autonomous Agents and Multi-Agent Systems
May 9 - 13, 2022
Virtual Event, New Zealand
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Overall Acceptance Rate 1,155 of 5,036 submissions, 23%
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