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Behavioural Simulation Models & Applications

S. Paris’ research activities summary, focusing on. Behavioural Simulation Models & Applications. by Sébastien Paris. Introduction. Why replicate human behaviours? Animation Entertainment: video games, movies Visual realism essential Simulation

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Behavioural Simulation Models & Applications

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  1. S. Paris’ research activities summary, focusing on Behavioural SimulationModels & Applications by Sébastien Paris

  2. Introduction • Why replicate human behaviours? • Animation • Entertainment: video games, movies • Visual realism essential • Simulation • Validation: security, exploitation of public areas • Behavioural realism essential • Why difficult? • Many topics to address from various domains • Environment: geomatic (GIS), graphics, urbanism • Human: sociology, psychology, cognitive sciences, biophysics S. Paris research activities

  3. Outline • The models • Virtual environment • Interactive objects • Virtual human • The applications • SIMULEM: Interacting crowds • Crowd-MAGS: Demonstrating crowds • Metropolis: Animated crowds S. Paris research activities

  4. Overview of the models Microscopic model • Autonomous agents:each entity has its own behaviour • Theory of control [Lord94]:the PerceptionDecisionAction loop • Behavioural pyramid[Newel90, Donikian04, Paris07b] S. Paris research activities

  5. Virtual Environment 1/6 • Why? To situate behavioural components. • How: Discrete approaches (grid, quadtree): too problematic!(precision, memory complexity, exploitation) Exact approaches (Constrained Delaunay Triangulation):harder to implement, but many advantages... • little/no approximation (exact objects positioning) • complexity does not depend on scale / precision • allow very fast exploitation (can be abstracted) [Paris05,06,09,09b] S. Paris research activities

  6. Virtual Environment2/6 From Geographic Information System (GIS) to Informed Virtual Geographic Environment (IVGE) Spatial decomposition Elevation map (2.5D) Merged semantics map (2D) GIS Vector layers selection Maps unification Elevated merged semantics map IVGE Abstraction / Enhancement GIS data of Quebec city S. Paris research activities

  7. Virtual Environment3/6 From Geographic Information System (GIS) to Informed Virtual Geographic Environment (IVGE) Spatial decomposition Elevation map (2.5D) Merged semantics map (2D) GIS Vector layers selection Maps unification Elevated merged semantics map IVGE • Abstraction / Enhancement Selected layers S. Paris research activities

  8. Virtual Environment4/6 From Geographic Information System (GIS) to Informed Virtual Geographic Environment (IVGE) Spatial decomposition Elevation map (2.5D) Merged semantics map (2D) GIS Vector layers selection Elevationmap Maps unification Elevated merged semantics map Mergedsemanticsmap IVGE • Abstraction / Enhancement S. Paris research activities

  9. Virtual Environment5/6 From Geographic Information System (GIS) to Informed Virtual Geographic Environment (IVGE) Spatial decomposition Elevation map (2.5D) Merged semantics map (2D) GIS Vector layers selection Maps unification Elevated merged semantics map IVGE • Abstraction / Enhancement Unified map S. Paris research activities

  10. Virtual Environment6/6 From Geographic Information System (GIS) to Informed Virtual Geographic Environment (IVGE) Spatial decomposition Elevation map (2.5D) Merged semantics map (2D) GIS Vector layers selection Maps unification Elevated merged semantics map IVGE Elevation semantics extraction • Abstraction / Enhancement S. Paris research activities

  11. Interactive Objects [Paris06b,09c] • BIIO: Behavioural Interactive Introspective Objects • Addresses the symbol grounding problem(situate interactions in the environment) • Allows interaction to play a role in decision(using affordances [Gibson87] ) • Highly upgradable: • High level concepts linked tointeraction (can be specialised) • Generic definition of themechanisms of interaction S. Paris research activities

  12. Virtual Human1/4 • Perception abilities • Only visual (other manageable) • Topology: generic algorithm using semantic filters • Neighbourhood • Grid based:fast recording / retrieval • Visibility graph:slower but visibility occlusion management S. Paris researchactivities

  13. Virtual Human2/4 • Low level decision • Biomechanical: locomotion/animation • Reactive: • Path following & visual optimisation • Perception management [Paris05] • Navigation (collision avoidance)[Paris07c] • Predictive geometrical model

  14. Virtual Human3/4 • High level decision [Paris09c] • Rational: Behavioural planning similar to BDI • Cognitive: • Memory: topology, internal states • Situated decision based on path planning:selection of the best feasible interaction in the environment • Interaction management: reaching object, waiting, using

  15. Virtual Human4/4 • Social behaviours • Social identity(SI): goals selection • Fundamental SI: human archetype (policeman, workman) • Adopted SI: current human behaviour (squad member, demonstrator) • Social influence: influences SI • Social structure: family, friends • Group formation: flows, waiting queues

  16. Outline • The models • Virtual environment • Interactive objects • Virtual human • The applications • SIMULEM: Interacting crowds • Crowd-MAGS: Demonstrating crowds • Metropolis: Animated crowds S. Paris research activities

  17. SIMULEM [Paris08b] • Train station simulation: • 3D output • Interactive control • Pause, Play, Accelerate • Navigate through the environment • View / Change the internal state of any BIIO object

  18. SIMULEM [Paris08b] • Results analysis: • Maps: Number or Densities of people, Moving speed • Graphs: Flows, Time to traverse, Number of people Densities of people Average speed S. Paris research activities

  19. Crowd-MAGS [Paris09b] • Feasibility study: • Environment processing tool • Automatic processing of any GIS source • Social models design / demonstration • Video / statistics analysis • 3D simulation

  20. Metropolis • Huge crowds animation for entertainment: • Environment processing tool • Environment loading / annotation • Algorithms unit tests • Crowd simulation [Paris09d] • Low level behaviours • Unified LOD: Behaviour, Animation, Graphic • Huge crowds (10K) in real time

  21. http://parissebastien.free.fr Online resources Download this presentation S. Paris research activities

  22. References [Paris05] S. Paris, S. Donikian, and N. Bonvalet. Towards more Realistic and Efficient Virtual Environment Description and Usage. First International Workshop on Crowd Simulation (V-Crowds'05), 2005 [Paris06] S. Paris, S. Donikian, and N. Bonvalet. Environmental abstraction and path planning techniques for realistic crowd simulation. Computer Animation and Virtual Worlds, 17:325–335, 2006 [Paris06b] S. Paris, S. Donikian, and N. Bonvalet. BIIO for crowd simulation. In ACM SIGGRAPH / Eurographics Symposium on Computer Animation (poster session), 2006. [Paris07] S. Paris, S. Donikian, and N. Bonvalet. Versune architecture pour la simulation microscopique de foule. Revue Electronique Francophone d’InformatiqueGraphique(REFIG), 1(1):33–43, 2007 [Paris07b] S. Paris. Caractérisation des niveaux de services and modélisation des circulations de personnesdans les lieuxd’échanges, Thèse de l’Université de Rennes I, IRISA, 2007 [Paris07c] S. Paris, J. Pettré, and S. Donikian. Pedestrian reactive navigation for crowd simulation: a predictive approach. Eurographics’07, IEEEComputer Graphics Forum, 2007 [Paris08] S. Donikian and S. Paris. Towards embodied and situated virtual humans. DansMotion in Games 2008 (MIG08), vol. 5277 of Lecture Notes in Computer Science LNCS, p. 51–62. Springer, 2008 [Paris08b] S. Paris, D. Lefebvre, and S. Donikian. Simulem: (…) goal oriented behaviours in crowd simulation. In 4th International Conference on Pedestrian and Evacuation Dynamics. 2008 [Paris09] S. Paris, M. Mekni, and B. Moulin. IVGE: an accurate topological approach. In The International Conference on Advanced GIS & Web Services (GEOWS). IEEE Computer Society Press, 2009 [Paris09b] M. Mekni, B. Moulin, and S. Paris. Advanced Geo-Simulation Book, chapter Semantically-Enhanced Virtual Geographic Environments for Multi-Agent Geo-Simulation. Bentham, 2009 [Paris09c] S. Paris and S. Donikian. Activity-driven populace: a cognitive approach for crowd simulation. IEEEComputer Graphics and Applications (CGA) special issue Virtual Populace, 2009 [Paris09d] S. Paris, A. Gerdelan, C. O’Sullivan. CA-LOD: Collision Avoidance Level of Detail for Scalable, Controllable Crowds.Motion in Games’09, Lecture Notes in Computer Science S. Paris research activities

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