Alexander Shoulson

Parameterizing behavior trees

This paper introduces and motivates the application of parameterization to behavior trees. As a framework, behavior trees are becoming more commonly used for agent controllers in interactive game environments. We describe a way by which behavior trees can be authored for acting upon functions with arguments, as opposed to being limited to nonparametric tasks. We expand upon this idea to provide a method by which a subtree itself can be encapsulated with an exposed parameter interface through a lookup node, which enables code reuse in a manner already exploited by object oriented programming languages. Parameterization also allows us to recast Smart Events (a mechanism for co-opting agents to perform a desired activity) as behavior trees that can act generically upon groups of typed agents. Finally, we introduce a tool called Topiary, which enables the graphically-oriented authoring of behavior trees with this functionality as part of a broader testbed for agent simulation.

ADAPT: the agent development and prototyping testbed

We present ADAPT, a flexible platform for designing and authoring functional, purposeful human characters in a rich virtual environment. Our framework incorporates character animation, navigation, and behavior with modular interchangeable components to produce narrative scenes. The animation system provides locomotion, reaching, gaze tracking, gesturing, sitting, and reactions to external physical forces, and can easily be extended with more functionality due to a decoupled, modular structure. The navigation component allows characters to maneuver through a complex environment with predictive steering for dynamic obstacle avoidance. Finally, our behavior framework allows a user to fully leverage a characters animation and navigation capabilities when authoring both individual decision-making and complex interactions between actors using a centralized, event-driven model.

ADAPT: The Agent Developmentand Prototyping Testbed

We present ADAPT, a flexible platform for designing and authoring functional, purposeful human characters in a rich virtual environment. Our framework incorporates character animation, navigation, and behavior with modular interchangeable components to produce narrative scenes. The animation system provides locomotion, reaching, gaze tracking, gesturing, sitting, and reactions to external physical forces, and can easily be extended with more functionality due to a decoupled, modular structure. The navigation component allows characters to maneuver through a complex environment with predictive steering for dynamic obstacle avoidance. Finally, our behavior framework allows a user to fully leverage a characters animation and navigation capabilities when authoring both individual decision-making and complex interactions between actors using a centralized, event-driven model.

An Event-Centric Planning Approach for Dynamic Real-Time Narrative

In this paper, we propose an event-centric planning framework for directing interactive narratives in complex 3D environments populated by virtual humans. Events facilitate precise authorial control over complex interactions involving groups of actors and objects, while planning allows the simulation of causally consistent character actions that conform to an overarching global narrative. Events are defined by preconditions, postconditions, costs, and a centralized behavior structure that simultaneously manages multiple participating actors and objects. By planning in the space of events rather than in the space of individual character capabilities, we allow virtual actors to exhibit a rich repertoire of individual actions without causing combinatorial growth in the planning branching factor. Our system produces long, cohesive narratives at interactive rates, allowing a user to take part in a dynamic story that, despite intervention, conforms to an authored structure and accomplishes a predetermined goal.

Intelligent camera control using behavior trees

Automatic camera systems produce very basic animations for virtual worlds. Users often view environments through two types of cameras: a camera that they control manually, or a very basic automatic camera that follows their character, minimizing occlusions. Real cinematography features much more variety producing more robust stories. Cameras shoot establishing shots, close-ups, tracking shots, and birds eye views to enrich a narrative. Camera techniques such as zoom, focus, and depth of field contribute to framing a particular shot. We present an intelligent camera system that automatically positions, pans, tilts, zooms, and tracks events occurring in real-time while obeying traditional standards of cinematography. We design behavior trees that describe how a single intelligent camera might behave from low-level narrative elements assigned by “smart events”. Camera actions are formed by hierarchically arranging behavior sub-trees encapsulating nodes that control specific camera semantics. This approach is more modular and particularly reusable for quickly creating complex camera styles and transitions rather then focusing only on visibility. Additionally, our user interface allows a director to provide further camera instructions, such as prioritizing one event over another, drawing a path for the camera to follow, and adjusting camera settings on the fly. We demonstrate our method by placing multiple intelligent cameras in a complicated world with several events and storylines, and illustrate how to produce a well-shot “documentary” of the events constructed in real-time.

Event-Centric control for background agents

This paper describes a framework for controlling the varied activities of groups of background characters (representing extras or supernumeraries). Our platform is built upon an event-centric agent control model, which shifts behavior authoring from writing complex reactive agents to authoring particular activities. This approach allows us to achieve diverse, complex, and collaborative activities while the agents themselves stay simple and generic. An event is defined generically on agent roles, and can be dispatched to any set of agents that can fill those roles. This allows us to control macro-level group behavior with a centralized entity called the Group Coordinator that dispatches events to agents based on their situational and locational context (which can be controlled by an author). What results a structure for controlling macroscopic behavior for groups of background agents.

Planning approaches to constraint-aware navigation in dynamic environments

Path planning is a fundamental problem in many areas, ranging from robotics and artificial intelligence to computer graphics and animation. Although there is extensive literature for computing optimal, collision‐free paths, there is relatively little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified can include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce two hybrid environment representations that balance computational efficiency and search space density to provide a minimal, yet sufficient, discretization of the search graph for constraint‐aware navigation. An extended anytime dynamic planner is used to compute constraint‐aware paths, while efficiently repairing solutions to account for varying dynamic constraints or an updating world model. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft, attracting and repelling constraints, defined by both static obstacles and moving obstacles. Copyright

Constraint-Aware Navigation in Dynamic Environments

Path planning is a fundamental problem in many areas ranging from robotics and artificial intelligence to computer graphics and animation. While there is extensive literature for computing optimal, collision-free paths, there is little work that explores the satisfaction of spatial constraints between objects and agents at the global navigation layer. This paper presents a planning framework that satisfies multiple spatial constraints imposed on the path. The type of constraints specified could include staying behind a building, walking along walls, or avoiding the line of sight of patrolling agents. We introduce a hybrid environment representation that balances computational efficiency and discretization resolution, to provide a minimal, yet sufficient discretization of the search graph for constraint-aware navigation. An extended anytime-dynamic planner is used to compute constraint-aware paths, while efficiently repairing solutions to account for dynamic constraints. We demonstrate the benefits of our method on challenging navigation problems in complex environments for dynamic agents using combinations of hard and soft constraints, attracting and repelling constraints, on static obstacles and moving obstacles.

Authoring Multi-Actor Behaviors in Crowds With Diverse Personalities

Multi-actor simulation is critical to cinematic content creation, disaster and security simulation, and interactive entertainment. A key challenge is providing an appropriate interface for authoring high-fidelity virtual actors with feature-rich control mechanisms capable of complex interactions with the environment and other actors. In this chapter, we present work that addresses the problem of behavior authoring at three levels: Individual and group interactions are conducted in an event-centric manner using parameterized behavior trees, social crowd dynamics are captured using the OCEAN personality model, and a centralized automated planner is used to enforce global narrative constraints on the scale of the entire simulation. We demonstrate the benefits and limitations of each of these approaches and propose the need for a single unifying construct capable of authoring functional, purposeful, autonomous actors which conform to a global narrative in an interactive simulation.

What’s Next? The New Era of Autonomous Virtual Humans

This paper identifies several key limitations in the representation, control, locomotion, and authoring of autonomous virtual humans that must be addressed to enter the new age of interactive virtual world applications. These limitations include simplified particle representations of agents which decouples control and locomotion, the lack of multi-modal perception in virtual environments, the need for multiple levels of control granularity, homogeneity in character animation, and monolithic agent architectures which cannot scale to complex multi-agent interactions and global narrative constraints. We present this broad perspective with the objective of providing the stimulus for an exciting new era of virtual human research.