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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 Lin University of North Carolina at Chapel Hill ACM 2008 Walter Kerrebijn 0458376 21-06-2011
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Page 1: 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.

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

Page 2: 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)

Page 3: 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

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

Page 4: 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

Proposal:• Use composite agents to model different emergent behaviors:

- embody intangible factors (social, psychological)- use pre-existing collision avoidance

Page 5: 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.

Related Work

• Rule-based systems• Social Forces models• Continuum Crowd theoryClaim: All these can be combined with Composite Agents approach

Page 6: 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.

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

Page 7: 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.

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

Page 8: 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.

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”

Page 9: 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.

Composite AgentsDefinitions

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

Page 10: 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.

Composite AgentsDefinitions

Page 11: 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.

Composite AgentsTypes

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

Page 12: 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.

Composite AgentsTypes

Aggression:• Urgency

• modeled as property Urgency• Expression of that urgency

• modeled by adding aggression proxy Pi,1

Page 13: 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.

Composite AgentsTypes

Urgency:• constant

• dynamic (velocity-based, distance-based)

Page 14: 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.

Composite AgentsTypes

Example Urgency

Page 15: 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.

Composite AgentsTypes

Social Priority:• Priority

• modeled as property Priority• Expression of that priority

• modeled by adding priority proxy Pi,1

Page 16: 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.

Composite AgentsTypes

Example Social Priority

Page 17: 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.

Composite AgentsTypes

Authority:• Trailblazer

• modeled as property Trail Identifier• Expression of that trailblazer

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

Page 18: 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.

Composite AgentsTypes

Example Authority

Page 19: 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.

Composite AgentsTypes

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

Page 20: 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.

Composite AgentsTypes

Protection:

Guidance:

Page 21: 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.

Composite AgentsTypes

Example Protection and Guidance

Page 22: 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.

Implementation

Page 23: 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.

Implementation

Page 24: 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.

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

Page 25: 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.

Experiment

Office Evacuation, Subway Station, Embassy

[Movie]

Page 26: 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.

Results

Page 27: 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.

Results

Page 28: 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.

Results

Page 29: 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.

Conclusion

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

Page 30: 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.

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

Page 31: 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.

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

Page 32: 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.

Assessment

• The notion of ‘groups’ is not really explored

• ‘Any geometrical shape’ is not explained

• ‘Future work’ should be current work


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