Coupling agent motivations and spatial behaviors for authoring multiagent narratives. (26th June 2019)

• Record Type: Journal Article
• Authors: Zhang, Xun; Schaumann, Davide; Haworth, Brandon; Faloutsos, Petros; Kapadia, Mubbasir
• Is Part Of: Computer animation and virtual worlds. Volume 30:Number 3/4(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-06-26
• Subjects: agent‐centric authoring -- behavior‐centric authoring -- multiagent behavior authoring -- multiagent behavior synthesis -- multiagent narratives -- space‐centric authoring; Computer animation -- Periodicals; Visualization -- Periodicals; 006.6
• DOI: 10.1002/cav.1898
• Languages: English
• ISSNs: 1546-4261

Abstract

Abstract: Authoring behavior narratives for heterogeneous multiagent virtual humans engaged in collaborative, localized, and task‐based behaviors can be challenging. Traditional behavior authoring frameworks are either space‐centric, where occupancy parameters are specified; behavior‐centric, where multiagent behaviors are defined; or agent‐centric, where desires and intentions drive agents' behavior. In this paper, we propose to integrate these approaches into a unique framework to author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations using a prioritized resource allocation system. This approach can generate progressively more complex and plausible narratives that satisfy spatial, behavioral, and social constraints. Possible applications of this system involve computer gaming and decision‐making in engineering and architectural design. Abstract : We propose a framework to help architects and game designers author behavior narratives that progressively satisfy time‐varying building‐level occupancy specifications, room‐level behavior distributions, and agent‐level motivations. A dynamic scheduling mechanism solves a spatiotemporal resource allocation problem by dynamically assigning agents to different behaviors to minimize the deviation from the expected occupancy patterns and behavior distributions while maximizing agent motivations. Results indicate that our framework produces plausible rich behaviors while maintaining a minimal solution error over time.