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15th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2019

作者： Bisberg, Alexander J. Cardona-Rivera, Rogelio E. School of Computing University of Utah Salt Lake CityUT84112 United States Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577358190

It is crucial to develop reusable methods for explaining and evaluating esports given their popularity and diversity. Quantifying skill in an esport has the potential to improve win prediction, matchmaking, and storytelling for these games. Arpad Elo's skill modeling system for chess has been adapted to many games and sports. In each instance, the modeler is challenged with tuning parameters to optimize for some metric, usually accuracy. Often these approaches are one-off and lack consistency. We propose SCOPE, a framework that uses grid search cross-validation to select optimal parameters for Elo based on accuracy, calibration, or log loss. We demonstrate this method on a season of Call of Duty World League, a first-person shooter esport, and we demonstrate comparable performance to other more complex, state-of-the-art methods. Copyright © 2019, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Partial-Order, Partially-Seen Observations of Fluents or Actions for Plan Recognition as Planning

arXiv

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arXiv 2019年

作者： Nelson, Jennifer M. Cardona-Rivera, Rogelio E. School of Computing Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

This work aims to make plan recognition as planning more ready for real-world scenarios by adapting previous compilations to work with partial-order, half-seen observations of both fluents and actions. We first redefine what observations can be and what it means to satisfy each kind. We then provide a compilation from plan recognition problem to classical planning problem, similar to original work by Ramírez and Geffner, but accommodating these more complex observation types. This compilation can be adapted towards other planning-based plan recognition techniques. Lastly we evaluate this method against an "ignore complexity" strategy that uses the original method by Ramírez and Geffner. Our experimental results suggest that, while slower, our method is equally or more accurate than baseline methods;our technique sometimes significantly reduces the size of the solution to the plan recognition problem, i.e, the size of the optimal goal set. We discuss these findings in the context of plan recognition problem difficulty and present an avenue for future work. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Plan-based intention revision 32

Plan-based intention revision

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32nd AAAI Conference on Artificial Intelligence, AAAI 2018

作者： Amos-Binks, Adam Michael Young, R. Department of Computer Science NC State University RaleighNC27695 United States School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577358008

To extend the behavior range of character agents in story plans, we have defined plan-based intention revision with two key properties. The first is when an agent should reconsider an intention frame. We use a causal-link threat to operationalize the reconsider function from the BDI control loop. The second property describes how an agent should revise their intentions. Based on when a P-GOAL should be dropped, we allow an intention revision when no subplan exists for a reconsidered intention. Finally, we discuss evaluating these two properties using the QUEST cognitive model. Copyright © 2018, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Sketching a Generative Model of Intention Management for Characters In Stories: Adding Intention Management to a Belief-Driven Story Planning Algorithm 13

Sketching a Generative Model of Intention Management for Cha...

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13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Michael Young, R. School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577357926

Previous work on story planning has shown success in the generation of plans that are both intention-coherent and demonstrate aspects of inter-character conflict. However, the initial models of intention and conflict have been limited, in that they lack methods to generate story plots where characters drop sub-plans to achieve their goals in believably consistent and expressive ways and adopt new sub-plans in the face of plan failure. In current work, we have developed models of failed actions in stories that go hand in hand with erroneous belief models for character. Motivated by characterizations of rational agents' intentions as choice combined with commitment, we provide a framing of the plan generation process that is intended to show how characters form their own plans to achieve their own goals, act upon those plans until they feel that conditions no longer support their plans, and then re-plan in the face of adversity to achieve their goals. We show an example story plan that contains several types of character-based intention dynamics targeted by our approach. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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Sketching a Generative Model of Intention Management for Characters In Stories: Adding Intention Management to a Belief-Driven Story Planning Algorithm

Sketching a Generative Model of Intention Management for Cha...

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Workshops of the 13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Young, R. Michael School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

关键词： Generative adversarial networks

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Automated Screenplay Annotation for Extracting Storytelling Knowledge 13

Automated Screenplay Annotation for Extracting Storytelling ...

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13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Winer, David R. Michael Young, R. School of Computing University of Utah Salt Lake CityUT84112 United States Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577357926

Narrative screenplays follow a standardized format for their parts (e.g., stage direction, dialogue, etc.) including short descriptions for what, where, when, and how to film the events in the story (shot headings). We created a grammar based on the syntax of shot headings to extract this and other discourse elements for automatic screenplay annotation. We test our annotator on over a thousand raw screenplays from the IMSDb screenplay corpus and make the output available for narrative intelligence research. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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An empirical evaluation of a generative method for the expression of personality traits through action choice 13

An empirical evaluation of a generative method for the expre...

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13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Bahamón, Julio Cesár Young, R. Michael Department of Computer Science UNC Charlotte CharlotteNC28233 United States School of Computing and the Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577357919

The presence of interesting and compelling characters is an essential component of effective narrative. Well-developed characters have features that enable them to significantly enhance the believability and quality of a story. In this paper, we describe the results of an experiment to evaluate a planning-based narrative generation system that focuses on the generation of stories that express character. The system is designed to automatically produce narratives that show character personality traits through the choices characters make when selecting the means by which they achieve their goals. Results from our study support the hypothesis that an audience presented with stories generated by Mask will attribute personality traits to the story characters that have significant correlation with the computational model of personality used to drive the characters’ choices. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Military applications

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Merits of hierarchical story and discourse planning with merged languages 13

Merits of hierarchical story and discourse planning with mer...

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13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Winer, David R. Michael Young, R. School of Computing University of Utah Salt Lake CityUT United States School of Computing University of Utah Salt Lake CityUT United States Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT United States

ISBN: (纸本)9781577357919

A hierarchical, bipartite model can characterize many complex narrative phenomena associated with coordinating plot and communication in storytelling (e.g., cinematography), but the predominant pipeline-based strategy for generating narratives has inadvertently limited the expressiveness of storytelling systems. We introduce computational steps for merging story and discourse languages in plan-based storytelling systems with hierarchical knowledge which avoids this problem and motivates more expressive narrative discourse reasoning. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Human robot interaction

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Directing Intentional Superposition Manipulation 13

Directing Intentional Superposition Manipulation

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13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Robertson, Justus Amos-Binks, Adam Michael Young, R. Liquid Narrative Group Department of Computer Science NC State University RaleighNC27695 United States Liquid Narrative Group School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577357926

Strong story interactive narratives (IN) are stories that branch based on participant actions where all branches conform to a set of predefined constraints. However, participants in these systems may create branches where the constraints no longer hold. Strong story experience management, the process of generating IN trees, can be viewed as a game where the experience management agent wins if the story constraints hold during gameplay and loses if they are broken. In domains where the player has incomplete information of the story world, the experience manager can take action by shifting the player between alternate states that are consistent with the player's observations in order to maximize the probability that constraints will hold. This process is called superposition manipulation. In this paper we present a method of estimating the number of goal states reachable from different states in order to make informed decisions during superposition manipulation. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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Narrative Mediation as Probabilistic Planning 13

Narrative Mediation as Probabilistic Planning

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13th AAAI Conference on Artificial Intelligence and Interactive Digital entertainment, AIIDE 2017

作者： Robertson, Justus Michael Young, R. Liquid Narrative Group Department of Computer Science NC State University RaleighNC27695 United States Liquid Narrative Group School of Computing The Entertainment Arts and Engineering Program University of Utah Salt Lake CityUT84112 United States

ISBN: (纸本)9781577357926

Narrative mediation is a plan-based process that converts a planning problem into a mediation tree that models an interactive story. Probabilistic planning relaxes the classical planning constraint of deterministic action outcomes by containing domain operators with nondeterministic effects. This paper shows narrative mediation can be viewed as a probabilistic planning problem where the outcomes of user actions are modeled with nondeterministic effects in the domain. The paper gives a process for automatically converting a deterministic mediation planning problem into a probabilistic interactive narrative planning problem. Copyright © 2017, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Human robot interaction

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