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Part of: Synthesis digital library of engineering and computer science.

Includes bibliographical references (pages 219-245).

1. Introduction --

Part I. Multi-agent collision avoidance -- 2. Background -- 2.1 Centralized approaches -- 2.2 Agent-based approaches -- 2.2.1 Data-driven approaches -- 2.2.2 Predictive approaches -- 2.3 Locomotion synthesis -- 2.4 Challenges and proposed solutions -- 2.4.1 Particle-based agent models -- 2.4.2 Decoupling between steering and locomotion -- 2.4.3 Generalization and applicability of data-driven approaches -- 3. Footstep-based navigation and animation for crowds -- 3.1 Introduction -- 3.2 Locomotion model -- 3.2.1 Inverted pendulum model -- 3.2.2 Footstep actions -- 3.2.3 Locomotion constraints -- 3.2.4 Cost function -- 3.3 Planning algorithm -- 3.4 Evaluation -- 3.4.1 Interfacing with motion synthesis -- 4. Following footstep trajectories in real time -- 4.1 Animating from footsteps -- 4.2 Framework overview -- 4.3 Footstep-based locomotion -- 4.3.1 Motion clip analysis -- 4.3.2 Footstep and root trajectories -- 4.3.3 Online selection -- 4.3.4 Interpolation -- 4.3.5 Inverse kinematics -- 4.4 Incorporating root movement fidelity -- 4.5 Results -- 4.5.1 Foot placement accuracy -- 4.5.2 Performance -- 5. Context-sensitive data-driven crowd simulation -- 5.1 Steering in context -- 5.2 Steering contexts -- 5.3 Initial implementation -- 5.3.1 Training data generation -- 5.3.2 Oracle algorithm -- 5.3.3 Decision trees -- 5.3.4 Steering at runtime -- 5.4 Results -- 5.4.1 Classifier accuracy -- 5.4.2 Runtime -- 5.4.3 Collisions -- 6. Conclusion -- 6.1 Footstep-based collision avoidance -- 6.2 Footstep-based locomotion -- 6.3 Context-based steering --

Part II. Multi-agent navigation -- 7. Background -- 7.1 Navigation meshes -- 7.2 Planning -- 8. Navigation meshes -- 8.1 NavMeshes from 3D geometry: NEOGEN -- 8.1.1 GPU coarse voxelization -- 8.1.2 Layer extraction and labeling -- 8.1.3 Layer refinement -- 8.1.4 NavMesh generation -- 8.2 Results -- 8.3 Limitations and discussion -- 9. Multi-domain planning in dynamic environments -- 9.1 Multi-domain planning -- 9.2 Overview -- 9.3 Planning domains -- 9.3.1 Multiple domains of control -- 9.4 Problem decomposition and multi-domain planning -- 9.4.1 Planning tasks and events -- 9.5 Relationship between domains -- 9.5.1 Domain mapping -- 9.5.2 Mapping successive waypoints to independent planning tasks -- 9.6 Results -- 9.6.1 Comparative evaluation of domain relationships -- 9.6.2 Performance -- 9.6.3 Scenarios -- 10. Conclusion --

Part III. Perception -- 11. Background -- 12. Sound propagation and perception for autonomous agents -- 12.1 Sound categorization and representation -- 12.1.1 Sound feature selection and categorization -- 12.1.2 Sound packet representation (SPR) -- 12.1.3 SPR selection for hierarchical cluster analysis -- 12.2 Sound packet propagation -- 12.2.1 Transmission line matrix using uniform grids -- 12.2.2 Pre-computation for TLM using a quad-tree -- 12.3 Sound perception and behaviors -- 12.3.1 Effect of sound degradation on perception -- 12.3.2 Hierarchical sound perception model -- 12.3.3 Sound attention and behavior model -- 12.4 Experiment results -- 12.4.1 Applications -- 13. Multi-sense attention for autonomous agents -- 13.1 Introduction -- 13.2 Methodology -- 13.2.1 Object and action representations -- 13.2.2 Sense preprocessing -- 13.2.3 Sensing -- 13.3 Hierarchical aggregate clustering -- 13.3.1 Environment-centric clustering -- 13.3.2 Agent-centric clustering -- 13.3.3 Aggregate properties -- 13.4 Analysis and results -- 14. Semantics in virtual environments -- 14.1 Incorporating semantics -- 14.1.1 Lexical databases -- 14.1.2 Modularized smart objects -- 14.2 Semantic generation -- 14.2.1 Hierarchy generation -- 14.2.2 Semantic modularization -- 14.2.3 Runtime performance -- 14.3 Limitations -- 15. Conclusion --

Part IV. Agent-object interactions and crowd heterogeneity -- 16. Background -- 17. Parameterized memory models -- 17.1 Memory system -- 17.1.1 Memory representation -- 17.1.2 Sensory memory -- 17.1.3 Working memory -- 17.1.4 Long-term memory -- 17.2 Example and analysis -- 17.3 Future work -- 18. Individual differences -- 18.1 Personality -- 18.1.1 Personality-to-behavior mapping -- 18.1.2 User studies on personality -- 18.2 Roles and needs -- 18.2.1 Approach -- 18.2.2 Implementation -- 19. Conclusion --

Part V. Behavior and narrative -- 20. Background -- 21. An open source platform for authoring functional crowds -- 21.1 ADAPT -- 21.2 Framework -- 21.2.1 Full-body character control -- 21.2.2 Steering and path-finding -- 21.2.3 Behavior -- 21.3 Shadows in full-body character animation -- 21.3.1 Choreographers -- 21.3.2 The coordinator -- 21.3.3 Using choreographers and the coordinator -- 21.3.4 Example choreographers -- 21.4 Character behavior -- 21.4.1 The ADAPT character stack -- 21.4.2 Body capabilities -- 21.5 Character interactions -- 21.5.1 Characters interacting with each other -- 21.5.2 Characters interacting with the environment -- 21.6 Results -- 21.6.1 Multi-actor simulations -- 21.6.2 Computational performance -- 22. Event-centric planning for narrative synthesis -- 22.1 Problem domain and formulation -- 22.1.1 State space -- 22.1.2 Action space -- 22.1.3 Goal specification -- 22.2 Planning in event space -- 22.3 Runtime and simulation -- 22.3.1 Event loading and dispatch -- 22.3.2 Handling dynamic world changes -- 22.3.3 Intelligent ambient character behavior -- 22.4 Results -- 22.4.1 Environment design -- 22.4.2 Object state description -- 22.4.3 Authored events -- 22.4.4 Generated narrative -- 22.4.5 Reacting to user intervention -- 23. Conclusion -- 24. Epilogue --

Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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This volume presents novel computational models for representing digital humans and their interactions with other virtual characters and meaningful environments. In this context, we describe efficient algorithms to animate, control, and author human-like agents having their own set of unique capabilities, personalities, and desires. We begin with the lowest level of footstep determination to steer agents in collision-free paths. Steering choices are controlled by navigation in complex environments, including multi-domain planning with dynamically changing situations. Virtual agents are given perceptual capabilities analogous to those of real people, including sound perception, multi-sense attention, and understanding of environment semantics which affect their behavior choices. The roles and impacts of individual attributes, such as memory and personality are explored. The animation challenges of integrating a number of simultaneous behavior and movement demands on an agent are addressed through an open source software system. Finally, the creation of stories and narratives with groups of agents subject to planning and environmental constraints culminates the presentation.

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Title from PDF title page (viewed on November 24, 2015).