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Games and Agents: Designing Intelligent Game Play

Games and Agents: Designing Intelligent Game Play. Contents. Agents for games? Connecting agent technology and game technology Challenges Infrastructural stance Conceptual stance Design stance Conclusions. Agents or scripted characters?. Do we need agents?. Agents for Games?.

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Games and Agents: Designing Intelligent Game Play

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  1. Games and Agents:Designing Intelligent Game Play

  2. Contents • Agents for games? • Connecting agent technology and game technology • Challenges • Infrastructural stance • Conceptual stance • Design stance • Conclusions

  3. Agents or scripted characters?

  4. Do we need agents?

  5. Agents for Games? • Assume that we want to use agents for creating “intelligent” characters in games. • Can we use Agent Technology to implement those agents in the games? • I.e. can we make use of all the tools, techniques and platforms that are developed to implement intelligent agents for the incorporation of agents in games? • If so, what do we need to do to couple the agent and game technologies? • Or do we have to start from scratch and develop everything again specially for the game environment?

  6. Game Engines and AgentsClient side approach Physics Engine Game logic/loop Input module Animation Engine Agents Graphics Engine Sound Engine

  7. Example: Gamebots

  8. Multi Agent Systems Agent Environment Agent Platform Environment Agent Communication Manager User Interface Environment Agent Environment Agent

  9. Game Engines and AgentsServer side approach Agentified Character Physics Engine Agentified Character Game Engine Animation Engine Graphics Engine Agentified Character Sound Engine

  10. Example: Quake

  11. Games plus Agents Input module GE Environment MAS Agent Physics Engine Game logic/loop Agent Platform Animation Engine Control? Agent Communication Manager User Interface Graphics Engine Agent Sound Engine Agent

  12. Challenges • Synchronization • Filtering • Communication

  13. Stances • Infrastructure • Conceptual • Design

  14. Solutions

  15. Infrastructural stance: issues • Information from GE to agents • Actions from agents to GE • Communication between agents through GE • Time

  16. Succeeded? Game Engine Agent Platform Requested action Infrastructure: middleware with event queues

  17. Conceptual stance: issues • Ontological mapping • Perception and Action • Communication

  18. Data representation • Problem: Different data concepts in GE and MAS • World state vs. strategic abstraction level • Solution: Translation-step during agent sensing on GE-side • Design issue: Suitable abstraction level (not too low, not too high) Conceptual Aspects Technical Aspects • efficiency • communication-costs - interpretability

  19. Control over Perception • Problem: Perceptual attention for agents • Cannot attend to all information from the environment • Filtering cannot be performed by GE or MAS alone • Solution: Subscription-based filtering mechanism • Agent controls sensing: what and when to sense • Design issue: Balance flow of sensory information (not too much, not too little) Conceptual Aspects Technical Aspects • performance MAS • performance GE • communication-costs • goal-directed/ • stimulus-driven

  20. Control over Action Realization • Problem: Different nature of actions in typical GE and MAS environments • Modality + Duration • Solution: Action mechanism for body control + feedback channel • Dispatch, abort, feedback about status • Define actions at functional level • Design issue: Suitable abstraction-level (not too low, not too high) Conceptual Aspects Technical Aspects • control • - individuality • efficiency • communication-costs

  21. Communication • Problem: Different communication in MAS and GE • Method: communicative intent (direct) vs. verbal and nonverbal communicative behavior (indirect) • Communication channel: reliable vs. unreliable • Solution: Communication mechanism. • Allow MAS-communication through simulation environment • Design issue: Choose method: behavior or intent Conceptual Aspects Technical Aspects • complexity • efficiency • interpretability

  22. Middleware Approach • Bridge conceptual gap using a middleware • Design problems not responsibility of GE or MAS • Middleware to provide technical facilities: • Translate data representations • Perception/action/communication mechanisms • Don’t restrict designers in their IVA design, but offer technical solutions to help them realizing their design • Performance determined by how the facilities are used • Middleware itself is not part of the IVA design! • CIGA Framework developed to follow this design approach

  23. CIGA Framework • Physical Interface: Connect to simulation environments • E.g. CORE, (UT, CryEngine, Ogre, Delta3D, etc) • Cognitive Interface: Connect to agent systems • E.g. Jadex, 2APL, BT-based MAS, etc • Connection Mechanism: Internal message-passing system • Introduced for flexibility and portability • E.g. TCP/IP, Java/C++ bridge • Ontology Model: contract between GE and MAS • E.g. Specify ontology using: Protégé, custom ontology editors

  24. Games plus Agents AGE Agent Physics Engine Ontological mapping Dynamic Event subscription Game logic/loop Agent Platform Animation Engine Agent Communication Manager User Interface Graphics Engine Agent Event queues Sound Engine Agent

  25. Design stance: issues • How intelligent can an agent behave (boundaries): • Story line • Game rules (including communication) • Environment (UI and look and feel) • Roles

  26. IVA Design Issues • IVA-design is distributed • Physical-layer + Cognitive-layer • Physical aspects vs. Cognitive aspects • Cannot design these layers independently

  27. Design games using OperA • OperA specifies the boundaries of the behavior of the roles in the game • OperA indicates landmarks that should be reached that can be used to specify the learning goals • Agents can fill in the roles in different ways: • Scripted character • BDI agent • …

  28. OperA example: storyline Save victim Emergency call Create team Get to location Evaluate situation Extinguish fire start Ambulance end Expert

  29. OperA example: Scene

  30. OperA example: Roles in a game

  31. Conclusions • Intelligence by design only • Several stances needed to cover the connection between games and agents • Need for a middleware between AT and GE • Infrastructure “easy” • Conceptual connection is domain dependent • Design using an OperA like methodology seems promising • What agent technology to use? • What should be done by the agent and what by the game engine? • Intelligence?

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