Preface |
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xix | |
Acknowledgments |
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xxi | |
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1 | (4) |
Part I Multi-Agent Collision Avoidance |
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5 | (48) |
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7 | (6) |
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2.1 Centralized Approaches |
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7 | (1) |
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2.2 Agent-based Approaches |
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7 | (2) |
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2.2.1 Data-driven Approaches |
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8 | (1) |
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2.2.2 Predictive Approaches |
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8 | (1) |
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9 | (1) |
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2.4 Challenges and Proposed Solutions |
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9 | (4) |
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2.4.1 Particle-based Agent Models |
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10 | (1) |
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2.4.2 Decoupling between Steering and Locomotion |
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10 | (1) |
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2.4.3 Generalization and Applicability of Data-driven Approaches |
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11 | (2) |
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3 Footstep-based Navigation and Animation for Crowds |
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13 | (10) |
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13 | (2) |
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15 | (4) |
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3.2.1 Inverted Pendulum Model |
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15 | (1) |
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16 | (1) |
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3.2.3 Locomotion Constraints |
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17 | (1) |
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18 | (1) |
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19 | (1) |
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20 | (3) |
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3.4.1 Interfacing with Motion Synthesis |
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22 | (1) |
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4 Following Footstep Trajectories in Real Time |
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23 | (14) |
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4.1 Animating from Footsteps |
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23 | (1) |
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24 | (1) |
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4.3 Footstep-based Locomotion |
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25 | (4) |
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4.3.1 Motion Clip Analysis |
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25 | (2) |
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4.3.2 Footstep and Root Trajectories |
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27 | (1) |
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27 | (1) |
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28 | (1) |
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29 | (1) |
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4.4 Incorporating Root Movement Fidelity |
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29 | (4) |
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33 | (4) |
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4.5.1 Foot Placement Accuracy |
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33 | (1) |
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34 | (3) |
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5 Context-sensitive Data-driven Crowd Simulation |
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37 | (12) |
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37 | (1) |
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38 | (1) |
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5.3 Initial Implementation |
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39 | (5) |
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5.3.1 Training Data Generation |
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39 | (2) |
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41 | (1) |
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42 | (2) |
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5.3.4 Steering at Runtime |
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44 | (1) |
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44 | (5) |
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5.4.1 Classifier Accuracy |
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44 | (1) |
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45 | (1) |
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46 | (3) |
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49 | (4) |
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6.1 Footstep-based Collision Avoidance |
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49 | (1) |
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6.2 Footstep-based Locomotion |
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49 | (1) |
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6.3 Context-based Steering |
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50 | (3) |
Part II Multi-agent Navigation |
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53 | (38) |
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55 | (4) |
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55 | (2) |
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57 | (2) |
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59 | (16) |
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8.1 NavMeshes from 3D Geometry: NEOGEN |
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59 | (10) |
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8.1.1 GPU Coarse Voxelization |
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60 | (2) |
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8.1.2 Layer Extraction and Labeling |
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62 | (1) |
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63 | (4) |
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67 | (2) |
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69 | (3) |
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8.3 Limitations and Discussion |
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72 | (3) |
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9 Multi-domain Planning in Dynamic Environments |
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75 | (14) |
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9.1 Multi-domain Planning |
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75 | (1) |
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76 | (1) |
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77 | (2) |
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9.3.1 Multiple Domains of Control |
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77 | (2) |
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9.4 Problem Decomposition and Multi-domain Planning |
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79 | (3) |
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9.4.1 Planning Tasks and Events |
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81 | (1) |
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9.5 Relationship between Domains |
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82 | (1) |
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83 | (1) |
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9.5.2 Mapping Successive Waypoints to Independent Planning Tasks |
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83 | (1) |
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83 | (14) |
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9.6.1 Comparative Evaluation of Domain Relationships |
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83 | (2) |
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85 | (2) |
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87 | (2) |
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89 | (2) |
Part III Perception |
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91 | (52) |
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93 | (4) |
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12 Sound Propagation and Perception for Autonomous Agents |
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97 | (20) |
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12.1 Sound Categorization and Representation |
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99 | (6) |
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12.1.1 Sound Feature Selection and Categorization |
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100 | (1) |
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12.1.2 Sound Packet Representation (SPR) |
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100 | (2) |
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12.1.3 SPR Selection for Hierarchical Cluster Analysis |
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102 | (3) |
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12.2 Sound Packet Propagation |
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105 | (4) |
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12.2.1 Transmission Line Matrix Using Uniform Grids |
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105 | (1) |
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12.2.2 Pre-computation for TLM using a Quad Tree |
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106 | (3) |
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12.3 Sound Perception and Behaviors |
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109 | (2) |
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12.3.1 Effect of Sound Degradation on Perception |
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109 | (1) |
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12.3.2 Hierarchical Sound Perception Model |
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109 | (2) |
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12.3.3 Sound Attention and Behavior Model |
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111 | (1) |
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111 | (6) |
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114 | (3) |
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13 Multi-sense Attention for Autonomous Agents |
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117 | (14) |
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117 | (2) |
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119 | (3) |
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13.2.1 Object and Action Representations |
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119 | (1) |
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13.2.2 Sense Preprocessing |
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119 | (1) |
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120 | (2) |
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13.3 Hierarchical Aggregate Clustering |
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122 | (5) |
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13.3.1 Environment-centric Clustering |
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122 | (1) |
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13.3.2 Agent-centric Clustering |
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123 | (3) |
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13.3.3 Aggregate Properties |
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126 | (1) |
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13.4 Analysis and Results |
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127 | (4) |
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14 Semantics in Virtual Environments |
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131 | (10) |
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14.1 Incorporating Semantics |
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131 | (1) |
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132 | (1) |
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14.1.2 Modularized Smart Objects |
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132 | (1) |
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132 | (7) |
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14.2.1 Hierarchy Generation |
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133 | (3) |
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14.2.2 Semantic Modularization |
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136 | (1) |
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14.2.3 Runtime Performance |
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136 | (3) |
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139 | (2) |
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141 | (2) |
Part IV Agent-Object Interactions and Crowd Heterogeneity |
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143 | (32) |
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145 | (4) |
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17 Parameterized Memory Models |
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149 | (10) |
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150 | (3) |
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17.1.1 Memory Representation |
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150 | (1) |
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151 | (1) |
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151 | (1) |
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152 | (1) |
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17.2 Example and Analysis |
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153 | (3) |
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156 | (3) |
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18 Individual Differences |
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159 | (14) |
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159 | (7) |
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18.1.1 Personality-to-Behavior Mapping |
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160 | (3) |
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18.1.2 User Studies on Personality |
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163 | (3) |
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166 | (15) |
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166 | (2) |
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168 | (5) |
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173 | (2) |
Part V Behavior and Narrative |
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175 | (44) |
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177 | (4) |
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21 An Open Source Platform for Authoring Functional Crowds |
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181 | (18) |
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181 | (1) |
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182 | (3) |
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21.2.1 Full-body Character Control |
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182 | (2) |
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21.2.2 Steering and Path-finding |
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184 | (1) |
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184 | (1) |
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21.3 Shadows in Full-body Character Animation |
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185 | (6) |
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185 | (1) |
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186 | (2) |
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21.3.3 Using Choreographers and the Coordinator |
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188 | (1) |
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21.3.4 Example Choreographers |
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189 | (2) |
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191 | (3) |
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21.4.1 The ADAPT Character Stack |
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191 | (1) |
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192 | (2) |
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21.5 Character Interactions |
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194 | (2) |
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21.5.1 Characters Interacting with Each Other |
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194 | (2) |
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21.5.2 Characters Interacting with the Environment |
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196 | (1) |
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196 | (3) |
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21.6.1 Multi-actor Simulations |
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196 | (1) |
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21.6.2 Computational Performance |
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197 | (2) |
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22 Event-centric Planning for Narrative Synthesis |
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199 | (16) |
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22.1 Problem Domain and Formulation |
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200 | (4) |
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201 | (1) |
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202 | (1) |
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22.1.3 Goal Specification |
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203 | (1) |
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22.2 Planning in Event Space |
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204 | (1) |
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22.3 Runtime and Simulation |
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204 | (3) |
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22.3.1 Event Loading and Dispatch |
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204 | (1) |
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22.3.2 Handling Dynamic World Changes |
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205 | (2) |
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22.3.3 Intelligent Ambient Character Behavior |
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207 | (1) |
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207 | (8) |
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22.4.1 Environment Design |
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208 | (1) |
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22.4.2 Object State Description |
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208 | (1) |
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209 | (1) |
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22.4.4 Generated Narrative |
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209 | (3) |
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22.4.5 Reacting to User Intervention |
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212 | (3) |
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215 | (2) |
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217 | (2) |
Bibliography |
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219 | (28) |
Authors' Biographies |
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247 | |