Hengchin Yeh, Sean Curtis, Sachin Patil, Jur van den Berg, Dinesh Manocha, Ming Lin University of...

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Hengchin Yeh, Sean Curtis, Sachin Patil, Jur van den Berg,

Dinesh Manocha, Ming LinUniversity of North Carolina at Chapel Hill

ACM 2008

Walter Kerrebijn045837621-06-2011

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 theoryClaim: All these can be combined with Composite Agents approach

Composite Agents

General multi-agent system (SIMULATOR):• environment ΦEnv

• set of Agents = {A1,A2,…,An}• with states φi

• external state εi• position pi• velocity vi• geometric representation Gi

• internal state ιi• goal position, memory, mental state

Definitions

Composite Agents

General multi-agent system (SIMULATOR):• Algorithm for each agent:

• GatherNeighbors()• field of view, nearest-k neighbors• ENbr = {εk | Ak є GatherNeighbors(Ai)}

• Update()• φi ← Update(φi,ENbr,ΦEnv)

Definitions

Composite AgentsDefinitions

Composite Agent:• Basic Agent

• standard agent Ai from SIMULATOR• contains a set of Proxy Agents Pi,j

• Proxy Agent• “hands extended from the basic agent […], encouraging [other agents] to step away to avoid collision”

Proxy Agent Pi,j• εi,j• ιi,j• acces to ιi

Composite AgentsTypes

Different kinds of intangible factors:• Aggression• Social Priority• Authority• Protection and Guidance

Aggression:• Urgency

• modeled as property Urgency• Expression of that urgency

• modeled by adding aggression proxy Pi,1

Urgency:• constant

• dynamic (velocity-based, distance-based)

Example Urgency

Social Priority:• Priority

• modeled as property Priority• Expression of that priority

• modeled by adding priority proxy Pi,1

Example Social Priority

Authority:• Trailblazer

• modeled as property Trail Identifier• Expression of that trailblazer

• modeled by adding trail proxies Pi,1,Pi,2,…,Pi,m

Example Authority

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 Pi,1

Protection:

Guidance:

Example Protection and Guidance

Implementation

Proxy Updates• information contained in proxy

Dynamic StatesConditional 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 (http://gamma.cs.unc.edu/CompAgent/) has very little information

Assessment

• The notion of ‘groups’ is not really explored

• ‘Any geometrical shape’ is not explained

• ‘Future work’ should be current work

Hengchin Yeh, Sean Curtis, Sachin Patil, Jur van den Berg, Dinesh Manocha, Ming Lin University of...

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