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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

  • 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 ?


What makes a successful game4 l.jpg

What makes a successful game ?

  • Fun to play


Taxonomy of fun l.jpg

1. Sensation

Game as sense-pleasure

Taxonomy of “Fun”

- Marc Leblanc


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1. Sensation

Game as sense-pleasure

2. Fantasy

Game as make-believe

Taxonomy of “Fun”

- Marc Leblanc


Taxonomy of fun7 l.jpg

1. Sensation

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

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

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

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

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

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

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


Players expectations technology l.jpg

Players’ Expectations & Technology

Complexity of Game Design & Development

Players’ Expectations

Technology

Time


Content value curve l.jpg

Content-Value Curve

Complexity/Cost of Content Development

Perceived Value of Content

Content


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Features of MMP 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’s Direction


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Game industry’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

Complexity of Game Design & Development

Game’s Appeal to players

Amount of Procedural Generation


User content creation l.jpg

User-Content Creation

Complexity of Game Design & Development

Game’s Appeal to players

Flexibility in User-Content Creation


Atomistic generation l.jpg

Atomistic Generation

Complexity of Game Design & Development

Game’s Appeal to players

Detail of Atomistic Generation


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Industry’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

Complexity of Game Design & Development

Game’s Appeal to players

Amount of Automation


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Aims & Deliverables


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Aims

  • 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 GameFramework

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

  • Complex Systems

  • Modeling

  • Design

  • Engineering

  • Massively Multiplayer Games


Approach l.jpg

Approach


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Why Complex Systems Modeling ?

  • 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 Modeling ?

  • 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?

  • 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


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MMP Game Architecture

  • 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

  • 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

  • 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

Overseer1

Player A

Player C

Player B

Overseer2

MMP Game Architecture


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Challenges

  • Absurd evolutionary paths

  • Unfaithful representation of real world objects

  • Exploitation of emergent flaws

  • Overly dominant correction systems

  • Stability

  • Robustness

  • Scalability


Robustness l.jpg

Robustness

  • 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

  • Collaborative Assignment Agents

  • Fuzzy Signatures

  • Discrete-Event Modeling

  • Feedback based control system


Collaborative assignment agents l.jpg

Collaborative Assignment Agents

  • 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


Collaborative assignment agents42 l.jpg

Environment

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

  • 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

  • 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

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)

Game

Rules

State

Feedback Control System


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Feedback Control System

  • 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

Input (Player)

Entity A Rules

Entity B Rules

Game

Rules

Output

State

State

Input

Feedback Control System


References l.jpg

References

  • 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

  • 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


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MMP Game Modeling Methodology

  • Complex aggregate behavioral modeling

  • Intelligent aggregate behavior

  • Bottom-Up approach

  • Natural Selection / Genetic Algo


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