Hengchin Yeh, Sean Curtis, Sachin Patil, Jur van den Berg, Dinesh Manocha, Ming Lin University of North Carolina at Chapel Hill ACM 2008 Walter Kerrebijn

Introduction Increase of agent-based methods to model virtual crowds: off-line (movies) real-time (games, virtual environments)

Introduction Agent-based approach pros: independent decisions different simulation parameters Agent-based approach contras: emergent realism from behavioral rules hard to ensure computationally expensive distinction between global and local path-planning

Introduction Proposal: Use composite agents to model different emergent behaviors: - embody intangible factors (social, psychological) - use pre-existing collision avoidance

Related Work Rule-based systems Social Forces models Continuum Crowd theory Claim: All these can be combined with Composite Agents approach

Composite Agents General multi-agent system (SIMULATOR): environment Φ Env set of Agents = {A 1,A 2,…,A n } with states φ i external state ε i position p i velocity v i geometric representation G i internal state ι i goal position, memory, mental state Definitions

Composite Agents General multi-agent system (SIMULATOR): Algorithm for each agent: GatherNeighbors() field of view, nearest-k neighbors E Nbr = {ε k | A k є GatherNeighbors(A i )} Update() φ i ← Update(φ i,E Nbr,Φ Env ) Definitions

Composite Agents Definitions Composite Agent: Basic Agent standard agent A i from SIMULATOR contains a set of Proxy Agents P i,j Proxy Agent “hands extended from the basic agent […], encouraging [other agents] to step away to avoid collision”

Composite Agents Definitions Proxy Agent P i,j ε i,j ι i,j acces to ι i

Composite Agents Definitions

Composite Agents Types Different kinds of intangible factors: Aggression Social Priority Authority Protection and Guidance

Composite Agents Types Aggression: Urgency modeled as property Urgency Expression of that urgency modeled by adding aggression proxy P i,1

Composite Agents Types Urgency: constant dynamic (velocity-based, distance-based)

Composite Agents Types Example Urgency

Composite Agents Types Social Priority: Priority modeled as property Priority Expression of that priority modeled by adding priority proxy P i,1

Composite Agents Types Example Social Priority

Composite Agents Types Authority: Trailblazer modeled as property Trail Identifier Expression of that trailblazer modeled by adding trail proxies P i,1,P i,2,…,P i,m

Composite Agents Types Example Authority

Composite Agents Types Protection and Guidance: Mother M and Child K M maintains information about K M provides protection and guidance for K Expression of M’s behavior modeled by adding a protection or guidance proxie P i,1

Composite Agents Types Protection: Guidance:

Composite Agents Types Example Protection and Guidance

Implementation

Proxy Updates information contained in proxy Dynamic States Conditional Neighbors proxies not in neighbor set of parent agent, trail proxies not in neighbor sets of group members Visualization 2D and 3D

Experiment Office Evacuation, Subway Station, Embassy [Movie]

Results

Conclusion Composite agents can be succesfully used to model emergent crowd behaviors This yields little computational overhead

Assessment (Almost) good paper length, but lacking information almost everywhere Experiments barely compare between methods or even sufficiently in the same method Ending seems too short, incomplete, or superficial Conclusion is not epic, and maybe too bold

Assessment The ‘math’ section seems misplaced and arbitrary, also too compact to really check its use and correctness Almost nothing is mentioned about goal selection, map creation, or the selection of locations of proxy agents Accompanying website ( has very little informationhttp://gamma.cs.unc.edu/CompAgent/

Assessment The notion of ‘groups’ is not really explored ‘Any geometrical shape’ is not explained ‘Future work’ should be current work