Populating semantic virtual environments

Pelkey, Cameron D.; Allbeck, Jan M.

doi:10.1002/cav.1587

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…Our action set is based on the events of Shoulson et al [31], seen in Table 1. We automatically generate generalizations for objects using WordNet [36] and the method of Pelkey and Allbeck [27]. This creates a total of 21 actions and 31 objects.…”

Section: Experimentationmentioning

confidence: 99%

Understanding everything NPCs can do

Balint

Allbeck

Bidarra

2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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Non-Player Characters (NPCs) have actions that allow them to reason about what they can do in a game and how they can do it. The background information about what they can do are the components of the action, and how they can do it is the form or shape of an action, which may be built up from several sub-actions. The components and shape of an action must be fully de ned in a game, which can be tedious when several similar actions are needed. Furthermore, as the number of nuanced actions grows, more pressure is placed on an already constrained reasoning system. By discovering the similarities between actions an NPC can do, a given action set can be intelligently organized with similar components being generalized using an action taxonomy. To understand the similarity between actions we have developed measures of action similarity based on their constituent components and form. From this, we discover a metric to determine the generalization ability of an organization strategy for an NPC action set. We examine the use of our measures on a previously developed action set to show the nuances between those actions. Lastly, we nd that intelligently organizing actions has a positive e ect on virtual character reasoning abilities. CCS CONCEPTS •Computing methodologies → Knowledge representation and reasoning;

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Section: Experimentationmentioning

confidence: 99%

Understanding everything NPCs can do

Balint

Allbeck

Bidarra

2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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“…ALET uses large textual corpora, which due to their size, is pre‐processed (see Section 4.1). The processed data is then combined with our sets of objects and actions using the knowledge base FrameNet, (see Section 4.2).…”

Section: Semantic Generationmentioning

confidence: 99%

“…S ), the connections provide only descriptions of actions and objects. To specify the roles of each object in an action, we combine the results of our co‐occurrence algorithm with FrameNet in order to embed a deeper understanding of each virtual object's role. One component of the FrameNet database is Functional Elements ( F E s), which are functional (operational and semantic) information related to a verb.…”

Section: Semantic Generationmentioning

confidence: 99%

ALET: Agents Learning their Environment through Text

Balint

Allbeck

2017

Computer Animation & Virtual

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For intelligent virtual humans to interact in a rich environment, connections between the usable actions and objects must be defined. For scenarios consisting of hundreds of object types and many actions, having a simulation author create these connections by hand becomes and difficult. This limitation causes the simulation to have only a few object connections, whereas the rest of the objects are static and cannot be acted upon. Automated methods to connect objects to actions are promising but incomplete as they still miss key interactions and are unable to determine important characteristics of each action such as the manner of the action. We present Agents Learning their Environment through Text (ALET) to connect graphical objects to actions and provide an understanding of the actions. Observing that text contains descriptions of actions and connections to objects, ALET leverages large text corpora, and knowledge bases to provide candidate connections and descriptions for the actions and objects available in a simulation. The candidate connections and descriptions populate a representation that describes the graphical models and animations. We test ALET against other semantic generation methods, and find it more accurately determines object roles in actions, as well as determines semantic information about the action. KEYWORDS autonomous actors, intelligent virtual humans, semantic virtual environmentsComput Anim Virtual Worlds. 2017;28:e1759.wileyonlinelibrary.com/journal/cav

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“…Said interactions require a high level of domain knowledge, the creation of which is tedious and time consuming. Recent research has explored automating the creation of elements of domain knowledge [23,5], but additional effort is needed. The plausibility illusion also calls for researchers to avoid the Uncanny Valley.…”

Section: So What's Missing?mentioning

confidence: 99%

Feeling crowded yet?: crowd simulations for VR

Pelechano

Allbecky

2016

2016 IEEE Virtual Humans and Crowds for Immersive Environments (VHCIE)

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With advances in virtual reality technology and its multiple applications, the need for believable, immersive virtual environments is increasing. Even though current computer graphics methods allow us to develop highly realistic virtual worlds, the main element failing to enhance presence is autonomous groups of human inhabitants. A great number of crowd simulation techniques have emerged in the last decade, but critical details in the crowd's movements and appearance do not meet the standards necessary to convince VR participants that they are present in a real crowd. In this paper, we review recent advances in the creation of immersive virtual crowds and discuss areas that require further work to turn these simulations into more fully immersive and believable experiences.

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Populating semantic virtual environments

Cited by 9 publications

References 12 publications

Understanding everything NPCs can do

Understanding everything NPCs can do

ALET: Agents Learning their Environment through Text

Feeling crowded yet?: crowd simulations for VR

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