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dc.contributor.author
Raymond, Henry
dc.contributor.author
Knobloch, Sven
dc.contributor.author
Zünd, Fabio
dc.contributor.author
Sumner, Robert W.
dc.contributor.author
Magnenat, Stéphane
dc.date.accessioned
2021-03-02T07:58:30Z
dc.date.available
2020-09-08T08:40:20Z
dc.date.available
2020-09-08T09:19:32Z
dc.date.available
2021-03-02T07:58:30Z
dc.date.issued
2020-10
dc.identifier.uri
http://hdl.handle.net/20.500.11850/439084
dc.identifier.doi
10.3929/ethz-b-000439084
dc.description.abstract
Emergent narrative has the ability to unlock the true potential of interactive media, moving beyond pre-scripted, fixed story- lines. Existing implementations of emergent narrative achieve their results through complex rule systems and agent representations, which entail high authoring workload that limit the feasible scope of storyworlds. In this paper, we propose an approach that instead aims at leveraging efficient planning to achieve similar results, using Monte Carlo Tree Search and efficient data structures. This allows for abstraction and modularization of agent behavior, and endows agents with a theory of mind by letting them plan for each other. This greatly simplifies agent definition and removes the need to explicitly encode intentions. We show that competitive, collaborative and sustainable behaviors emerge in our system, without the explicit definition of such behaviors. Based on these preliminary results, we discuss necessary steps to turn our approach into an applicable emergent narrative system.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich, Game Technology Center (GTC)
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
emergent storytelling
en_US
dc.subject
multi-agent simulation
en_US
dc.subject
theory of mind
en_US
dc.subject
Monte Carlo Tree Search (MCTS)
en_US
dc.subject
artificial intelligence
en_US
dc.subject
action planning
en_US
dc.title
Leveraging efficient planning and lightweight agent definition: a novel path towards emergent narrative
en_US
dc.type
Conference Paper
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.size
8 p. submitted version
en_US
ethz.version.deposit
submittedVersion
en_US
ethz.event
12th Intelligent Narrative Technolgies Workshop, held with the AIIDE Conference (INT10 2020)
en_US
ethz.event.location
online
en_US
ethz.event.date
October 19-20, 2020
en_US
ethz.notes
Conference lecture held on October 19, 2020. Due to the Coronavirus (COVID-19) the conference was conducted virtually.
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::08698 - Game Technology Center (GTC)
en_US
ethz.date.deposited
2020-09-08T08:40:29Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-03-02T07:58:39Z
ethz.rosetta.lastUpdated
2021-03-02T07:58:39Z
ethz.rosetta.versionExported
true
ethz.COinS
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