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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

Complex Systems Modeling, Design & Engineering for Massively Multiplayer Games

by Viknashvaran Narayanasamy

overview
Overview
  • What makes a successful game ?
  • Problem Statement
  • Game Industry’s Direction
  • Objectives
  • Approach
  • Methodologies & Techniques
taxonomy of fun
1. Sensation

Game as sense-pleasure

Taxonomy of “Fun”

- Marc Leblanc

taxonomy of fun6
1. Sensation

Game as sense-pleasure

2. Fantasy

Game as make-believe

Taxonomy of “Fun”

- Marc Leblanc

taxonomy of fun7
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
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
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
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
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
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
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

players expectations technology
Players’ Expectations & Technology

Complexity of Game Design & Development

Players’ Expectations

Technology

Time

content value curve
Content-Value Curve

Complexity/Cost of Content Development

Perceived Value of Content

Content

features of mmp games
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
game industry s direction19
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
procedural generation
Procedural Generation

Complexity of Game Design & Development

Game’s Appeal to players

Amount of Procedural Generation

user content creation
User-Content Creation

Complexity of Game Design & Development

Game’s Appeal to players

Flexibility in User-Content Creation

atomistic generation
Atomistic Generation

Complexity of Game Design & Development

Game’s Appeal to players

Detail of Atomistic Generation

industry s solution
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
Automation

Complexity of Game Design & Development

Game’s Appeal to players

Amount of Automation

slide26
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

Complete MMP GameFramework

MMP GameDesign

Complete MMP Game

MMP GameModel

Deliverables

RESEARCH

MMP GameModeling Framework

MMP GameArchitecture

DEVELOPMENT

MMP GameEngineering

title of the study
Title of the study
  • Complex Systems
  • Modeling
  • Design
  • Engineering
  • Massively Multiplayer Games
why complex systems modeling
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
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
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
mmp game architecture
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
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
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

Environment

Overseer1

Player A

Player C

Player B

Overseer2

MMP Game Architecture
challenges
Challenges
  • Absurd evolutionary paths
  • Unfaithful representation of real world objects
  • Exploitation of emergent flaws
  • Overly dominant correction systems
  • Stability
  • Robustness
  • Scalability
robustness
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
methodologies techniques being investigated
Methodologies & Techniques being Investigated
  • Collaborative Assignment Agents
  • Fuzzy Signatures
  • Discrete-Event Modeling
  • Feedback based control system
collaborative assignment agents
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

Environment

Resource Y

GameObject B

GameObject A

GameObject C

Arbitrating

Agent X

Arbitrating

Agent Y

Resource X

Collaborative Assignment Agents
fuzzy signatures
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
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

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 system47
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

Input

Input (Player)

Entity A Rules

Entity B Rules

Game

Rules

Output

State

State

Input

Feedback Control System
references
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
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.
mmp game modeling methodology
MMP Game Modeling Methodology
  • Complex aggregate behavioral modeling
  • Intelligent aggregate behavior
  • Bottom-Up approach
  • Natural Selection / Genetic Algo
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