Complex systems modeling design engineering for massively multiplayer games
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Complex Systems Modeling, Design & Engineering for Massively Multiplayer Games by Viknashvaran Narayanasamy Overview What makes a successful game ? Problem Statement Game Industry ’ s Direction Objectives Approach Methodologies & Techniques

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Complex systems modeling design engineering for massively multiplayer games l.jpg

Complex Systems Modeling, Design & Engineering for Massively Multiplayer Games

by Viknashvaran Narayanasamy


Overview l.jpg
Overview Massively Multiplayer Games

  • What makes a successful game ?

  • Problem Statement

  • Game Industry’s Direction

  • Objectives

  • Approach

  • Methodologies & Techniques


What makes a successful game l.jpg

What makes a successful game ? Massively Multiplayer Games


What makes a successful game4 l.jpg
What makes a successful game ? Massively Multiplayer Games

  • Fun to play


Taxonomy of fun l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun6 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun7 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun8 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

4. Challenge

Game as obstacle course

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun9 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

4. Challenge

Game as obstacle course

5. Fellowship

Game as social framework

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun10 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

4. Challenge

Game as obstacle course

5. Fellowship

Game as social framework

6. Discovery

Game as uncharted territory

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun11 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

4. Challenge

Game as obstacle course

5. Fellowship

Game as social framework

6. Discovery

Game as uncharted territory

7. Expression

Game as self-discovery

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun12 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

4. Challenge

Game as obstacle course

5. Fellowship

Game as social framework

6. Discovery

Game as uncharted territory

7. Expression

Game as self-discovery

8. Masochism

Game as submission

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun13 l.jpg

1. Sensation Massively Multiplayer Games

Game as sense-pleasure

2. Fantasy

Game as make-believe

3. Narrative

Game as drama

4. Challenge

Game as obstacle course

5. Fellowship

Game as social framework

6. Discovery

Game as uncharted territory

7. Expression

Game as self-discovery

8. Masochism

Game as submission

Taxonomy of “Fun”

- Marc Leblanc


Problem statement l.jpg

Problem Statement Massively Multiplayer Games


Players expectations technology l.jpg
Players Massively Multiplayer Games’ Expectations & Technology

Complexity of Game Design & Development

Players’ Expectations

Technology

Time


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Content-Value Curve Massively Multiplayer Games

Complexity/Cost of Content Development

Perceived Value of Content

Content


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Features of MMP Games Massively Multiplayer Games

  • Highly interactive

  • Large Persistent Worlds

  • Large number of human players

  • Process multiple unpredictable inputs

  • Player controls his own experience

  • Non-deterministic number of game states

  • Players from different socio-economical, geographical and cultural groups

  • Game governors used to tune in-game mechanics and economics over the lifetime of the game


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Game Industry Massively Multiplayer Games’s Direction


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Game industry Massively Multiplayer Games’s Direction

  • Game Industry’s direction to make MMP games more fun.

    • Procedural Generation

    • User-Content Creation

    • Content Ownership

    • Atomistic Generation

    • Worlds with infinite possibilities


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Procedural Generation Massively Multiplayer Games

Complexity of Game Design & Development

Game’s Appeal to players

Amount of Procedural Generation


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User-Content Creation Massively Multiplayer Games

Complexity of Game Design & Development

Game’s Appeal to players

Flexibility in User-Content Creation


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Atomistic Generation Massively Multiplayer Games

Complexity of Game Design & Development

Game’s Appeal to players

Detail of Atomistic Generation


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Industry Massively Multiplayer Games’s Solution

  • Industry’s Solution to rising level of complexity in development of MMP games

    • Automation

      • Build more tools

    • More advanced middleware

    • More computational power

    • More …


Automation l.jpg
Automation Massively Multiplayer Games

Complexity of Game Design & Development

Game’s Appeal to players

Amount of Automation


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Aims & Deliverables Massively Multiplayer Games


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Aims Massively Multiplayer Games

  • Resolve the mentioned limitations in MMP games

  • To develop a high-level framework or series of frameworks for designing fun MMP games

  • Manage the complexity in game development

  • Methodologies & Processes to improve

    • Performance

    • Game play

    • Interactivity

  • Possibly speed up MMP game development process


Deliverables l.jpg

Complete MMP Game Massively Multiplayer GamesFramework

MMP GameDesign

Complete MMP Game

MMP GameModel

Deliverables

RESEARCH

MMP GameModeling Framework

MMP GameArchitecture

DEVELOPMENT

MMP GameEngineering


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Title of the study Massively Multiplayer Games

  • Complex Systems

  • Modeling

  • Design

  • Engineering

  • Massively Multiplayer Games


Approach l.jpg

Approach Massively Multiplayer Games


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Why Complex Systems Massively Multiplayer GamesModeling ?

  • Complexity in MMP games are approaching complex real-time industrial systems

  • Increased interaction needed for meaningful emergent behavior

  • Encourage decentralized control

  • Simpler agent-based rules

  • Reduces space-complexity of rule base

  • Can be tweaked with simple rules to handle unpredictable/random human input


Why complex systems modeling31 l.jpg
Why Complex Systems Massively Multiplayer GamesModeling ?

  • Emergence and Emergent behavior

    • Useful cumulative emergent structures

    • Game play less deterministic

    • Game play more unpredictable

    • Elements of Discovery, Challenge, Fellowship and Sensation

  • Bottom-up approach to designing the environment

    • Higher degrees of freedom in design

  • Open environment

    • Allows actions that were not originally intended for in design


Why emergence is desirable l.jpg
Why Emergence is desirable? Massively Multiplayer Games

  • New content generated

  • New challenges generated

  • Non-rigid game play

  • New behavior generated

  • Does not require additional content development

  • Improves Content-Value curve

  • Supports creation of truly infinite worlds

  • Supports self-organizing patterns within game objects


Methodology l.jpg

Methodology Massively Multiplayer Games


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MMP Game Architecture Massively Multiplayer Games

  • Multi-Tiered

  • Heterogeneous agents

  • Agent-Tier

    • Core logic of each agent

    • Micro game engine

    • Interacts with other game objects and the MMP game environment

    • Negotiate for resources

  • Environment-Tier

    • Handles in-game economics

    • Game rules for physics, graphics and other environmental data

    • Basic set of rules to define limitations and capabilities of the environment


Mmp game architecture35 l.jpg
MMP Game Architecture Massively Multiplayer Games

  • Environment-Agent bridging Interface

    • Facilitates interaction between agents and environments

    • Abstraction to allow heterogeneous agents to communicate

    • Abstraction to allow simple agent implementation

  • Evolution subsystem


Mmp game architecture36 l.jpg
MMP Game Architecture Massively Multiplayer Games

  • Overseer Tier

    • Overseers to facilitate emergent behavior

    • Governor agents

    • Exercise policy based control to tweak emergent properties of the system

    • Policies to influence agents to take a particular course of action

    • Multiple overseers allow different policies from different policy-makers to affect a different niche-market of players

    • Agents can be influenced by more than one overseer


Mmp game architecture37 l.jpg

Environment Massively Multiplayer Games

Overseer1

Player A

Player C

Player B

Overseer2

MMP Game Architecture


Challenges l.jpg
Challenges Massively Multiplayer Games

  • Absurd evolutionary paths

  • Unfaithful representation of real world objects

  • Exploitation of emergent flaws

  • Overly dominant correction systems

  • Stability

  • Robustness

  • Scalability


Robustness l.jpg
Robustness Massively Multiplayer Games

  • Environment must be able to adapt with unpredictably changing conditions and variables in the environment

  • Reduce propagation of latent emergent flaws

  • Introspection and Adaptation

  • Admission Control

  • Conservation of Resources

  • Contingency Planning


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Methodologies & Techniques being Investigated Massively Multiplayer Games

  • Collaborative Assignment Agents

  • Fuzzy Signatures

  • Discrete-Event Modeling

  • Feedback based control system


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Collaborative Assignment Agents Massively Multiplayer Games

  • Multi-Agent Assignment Algorithm

  • Investigate & Extend BDI Reasoning

    • Belief

    • Desire

    • Intention

  • Advertise resource Exchange

  • Arbitrating Agent performs arbitration with agent intentions to assign algorithms

  • Each agent attempts to achieve the common goal of maximizing resource allocation


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Environment Massively Multiplayer Games

Resource Y

GameObject B

GameObject A

GameObject C

Arbitrating

Agent X

Arbitrating

Agent Y

Resource X

Collaborative Assignment Agents


Fuzzy signatures l.jpg
Fuzzy Signatures Massively Multiplayer Games

  • Complex decisions based on partial knowledge of inputs can be made

  • Able to except vague, ambiguous, imprecise, missing information

  • Can be easily extended to support new variables and conditions

  • Structure data into vectors of fuzzy values

  • Reduce space complexity of rule base


Discrete event modeling l.jpg
Discrete-Event Modeling Massively Multiplayer Games

  • Simulation Events perfectly synchronized with simulation

  • Simulation executed the moment it happens

  • Only affected objects and frames rendered

  • Maximize performance of parallel hardware architectures

  • Graphics rendering rate independent of simulation speed.


Discrete event modeling45 l.jpg

4 Massively Multiplayer Games

3

1

Render

Initialize

Simulate

2

Execute Event Pooling Routine & Get Events

Discrete-Event Modeling

Initialize–Generate Initialization Events

Event Translation for Simulator

User Event Generation

1

QueueEvents

Pending Events ?

No

Sleep until next event

Yes

Pop an event from the queue

Render only when simulation has made an update

Send Event to destination object

Object changes state

Simulate & Update Object. Generate events


Feedback control system l.jpg

Input (Player) Massively Multiplayer Games

Game

Rules

State

Feedback Control System


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Feedback Control System Massively Multiplayer Games

  • Agent behavior influenced by other agents

  • Other agents are influenced by other agents

  • Introduces Cross-term inducing features

  • Human Players will be substituted for agents

    • Introduces Natural randomness

  • Overseers only allow desirable agent behavior to propagate


Feedback control system48 l.jpg

Input Massively Multiplayer Games

Input (Player)

Entity A Rules

Entity B Rules

Game

Rules

Output

State

State

Input

Feedback Control System


References l.jpg
References Massively Multiplayer Games

  • Kirschbaum, D. – Introduction to Complex Systems, From http://www.calresco.org

  • LeBlanc, M., 2000, Formal Design Tools - Emergent Complexity & Emergent Narrative, In Proceedings of the Game Developer’s Conference 2000

  • Odell, J., Agents & Complex Systems, 2002. Journal of Object Technology 1(2), 35-45

  • Lindley, C. A., 2002. The gameplay gestalt, narrative and interactive storytelling, In the Proceedings of Computer Games and Digital Cultures Conference, Tampere, Finland, june 2002.

  • Diamante, V. GDC Report 2005 - Will Wright's - The Future of Content, In http://gamasutra.com

  • Gribble, S., Robustness in Complex Systems, From http://www.cs.washington.edu/homes/gribble/papers/robust.pdf

  • Brown, A., Oppenheimer, D., Keeton, K., Thomas, R., Kubiatowicz, J., & Patterson, D., A.. ISTORE: Introspective storage for data intensive network services. In Proceedings of the 7th Workshop on Hot Topics in Operating Systems (HotOSVII), March 1999.

  • Remondino, M., 2004. Multi-Agent Technology Applied to Real-Time Strategy Games, ERCIM News, 57, 19-20

  • IBM, STI Cell Processor, Next-Generation Processors, From http://www-1.ibm.com/businesscenter/venturedevelopment/us/en/featurearticle/gcl_xmlid/8649/nav_id/emerging

  • DIET Agents, http://diet-agents.sourceforge.net/

  • DirectIA®: Autonomous Behavior Kernel, http://www.masa-sci.com/directia.htm


References50 l.jpg
References Massively Multiplayer Games

  • DECAF – Distributed, Environment Centered Agent Framework, http://www.eecis.udel.edu/~decaf/

  • Kaehler, S. D., Fuzzy Logic Tutorial, Encoder, http://www.seattlerobotics.org/encoder/mar98/fuz/flindex.html

  • Mellon, L., Metrics Collection and Analysis, in Massively Multiplayer Game Development 2, T. Alexander, Editor. 2005, Charles River Media: Boston. p. 243-256.

  • Seow, K.T. & Wong, K.W. Collaborative Assignment: Using Arbitrated Self-Optimal Initializations for Faster Negotiation. 2002.

  • Geiss, W. Multiagent System : A Modern Approach to Distributed Artificial Intelligence, 1999, The MIT Press, London, U.K.

  • Wong K. W., Chong, A., Gedeon T. D., Kóczy L. T. and Vámos. T. Hierarchical Fuzzy Signature Structure for Complex Structured Data.

  • Garcia, I., Molla, R. & Camahort, E., Introducing Discrete Simulation into Games, http://www.ercim.org/publication/Ercim_News/enw57/garcia.html

  • Banks, J. & Carson J. S. II 1984. Discrete-Event System Simulation. New Jersey, Prentice-Hall.

  • Standish, K. R., On Complexity and Emergence, Complexity International, 9, http://www.complexity.org.au/vol09/

  • Green, B., Balancing Gameplay for Thousands of Your Harshest Critics, in Massively Multiplayer game Development 2, T. Alexander, Editor. 2005, Charles River Media: Boston. p. 35-55.

  • Ondrejka, C., Power by the People : User-Creation in Online Games, in Massively Multiplayer game Development 2, T. Alexander, Editor. 2005, Charles River Media: Boston. p. 57-84.


The end l.jpg

THE END Massively Multiplayer Games


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MMP Game Modeling Massively Multiplayer GamesMethodology

  • Complex aggregate behavioral modeling

  • Intelligent aggregate behavior

  • Bottom-Up approach

  • Natural Selection / Genetic Algo


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