Complex Systems Modeling, Design & Engineering for Massively Multiplayer Games

1. Complex Systems Modeling, Design & Engineering for Massively Multiplayer Games by Viknashvaran Narayanasamy

2. Overview • What makes a successful game ? • Problem Statement • Game Industry’s Direction • Objectives • Approach • Methodologies & Techniques

3. What makes a successful game ?

4. What makes a successful game ? • Fun to play

5. 1. Sensation Game as sense-pleasure Taxonomy of “Fun” - Marc Leblanc

6. 1. Sensation Game as sense-pleasure 2. Fantasy Game as make-believe Taxonomy of “Fun” - Marc Leblanc

7. 1. Sensation Game as sense-pleasure 2. Fantasy Game as make-believe 3. Narrative Game as drama Taxonomy of “Fun” - Marc Leblanc

8. 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

9. 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

10. 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

11. 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

12. 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

13. 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

14. Problem Statement

15. Players’ Expectations & Technology Complexity of Game Design & Development Players’ Expectations Technology Time

16. Content-Value Curve Complexity/Cost of Content Development Perceived Value of Content Content

17. 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

18. Game Industry’s Direction

19. 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

20. Procedural Generation Complexity of Game Design & Development Game’s Appeal to players Amount of Procedural Generation

21. User-Content Creation Complexity of Game Design & Development Game’s Appeal to players Flexibility in User-Content Creation

22. Atomistic Generation Complexity of Game Design & Development Game’s Appeal to players Detail of Atomistic Generation

23. 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 …

24. Automation Complexity of Game Design & Development Game’s Appeal to players Amount of Automation

25. Aims & Deliverables

26. 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

27. Complete MMP GameFramework MMP GameDesign Complete MMP Game MMP GameModel Deliverables RESEARCH MMP GameModeling Framework MMP GameArchitecture DEVELOPMENT MMP GameEngineering

28. Title of the study • Complex Systems • Modeling • Design • Engineering • Massively Multiplayer Games

29. Approach

30. 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

31. 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

32. 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

33. Methodology

34. 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

35. 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

36. 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

37. Environment Overseer1 Player A Player C Player B Overseer2 MMP Game Architecture

38. Challenges • Absurd evolutionary paths • Unfaithful representation of real world objects • Exploitation of emergent flaws • Overly dominant correction systems • Stability • Robustness • Scalability

39. 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

40. Methodologies & Techniques being Investigated • Collaborative Assignment Agents • Fuzzy Signatures • Discrete-Event Modeling • Feedback based control system

41. 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

42. Environment Resource Y GameObject B GameObject A GameObject C Arbitrating Agent X Arbitrating Agent Y Resource X Collaborative Assignment Agents

43. 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

44. 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.

45. 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

46. Input (Player) Game Rules State Feedback Control System

47. 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

48. Input Input (Player) Entity A Rules Entity B Rules Game Rules Output State State Input Feedback Control System

49. 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

50. 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.