A Computational Model of Plan-Based Narrative Conflict at the Fabula Level

Ware, Stephen G.; Young, R. Michael; Harrison, Brent; Roberts, David L.

doi:10.1109/tciaig.2013.2277051

Cited by 23 publications

(10 citation statements)

References 24 publications

(32 reference statements)

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“…Since then, a branch of research has coalesced around using planning algorithms to model how people behave and reason about event sequences (Young et al 2013). These algorithms include computational models of important narrative phenomena such as time (Sacerdoti 1975), causality (McAllester and Rosenblitt 1991), intentionality (Riedl and Young 2010), conflict (Ware et al 2014), and belief (Teutenberg and Porteous 2015).…”

Section: Related Workmentioning

confidence: 99%

Narrative Planning for Belief and Intention Recognition

Farrell

Ware

2020

AIIDE

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Planning algorithms generate sequences of actions that achieve a goal, but they can also be used in reverse: to infer the goals that led to a sequence of actions. Traditional plan-based goal recognition assumes agents are rational and the environment is fully observable. Recent narrative planning models represent agents as believable rather than perfectly rational, meaning their actions need to be justified by their goals, but they may act in ways that are not optimal, and they may possess incorrect beliefs about the environment. In this work we propose a technique for inferring the goals and beliefs of agents in this context, where rationality and omniscience are not assumed. We present two evaluations that investigate the effectiveness of this approach. The first uses partial observation sequences and shows how this impacts the algorithm’s accuracy. The second uses human data and compares the algorithm’s inferences to those made by humans.

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Section: Related Workmentioning

confidence: 99%

Narrative Planning for Belief and Intention Recognition

Farrell

Ware

2020

AIIDE

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“…al. developed planning techniques to generate conflict in the plot [58]. This again involved creating a model of conflict, and then constraining the planner to enforce the presence of conflict in the resulting stories.…”

Section: B Constrained Plot and Manual Spacementioning

confidence: 99%

A Survey on Story Generation Techniques for Authoring Computational Narratives

Kybartas

Bidarra

2017

IEEE Trans. Comput. Intell. AI Games

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Computers are often used as tools to design, implement and even visualize a variety of narrative forms. Many researchers and artists are now further attempting to engage the computer actively throughout the development of the narrative itself. Any form of computational narrative authoring is at some level always mixed-initiative, meaning that the processing capabilities of the computer are utilized with a varying degree to automate certain features of the authoring process. We structure this survey by focusing on two key components of stories, plot and space, and more specifically the degree to which these are either automated by the computer or authored manually.By examining the successes of existing research, we identify potential new research directions in the field of computational narrative. We also identify the advantages of developing a standard model of narrative to allow for collaboration between plot and space automation techniques. This would likely benefit the field of automated space generation with the strengths in the field of automated plot generation.

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“…Story generators arose as the first AI planning algorithms were developed, such as TALE-SPIN (Meehan 1977) in which woodland creatures follow plans to satisfy basic needs. State-of-the-art planners solve multiagent coordination problems such that characters follow domain-independent rules to behave more believably (Riedl and Young 2010;Ware et al 2014).…”

Section: Introductionmentioning

confidence: 99%