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Scalable Adaptive Serious Games using Agent Organizations

Scalable Adaptive Serious Games using Agent Organizations. Joost Westra, Frank Dignum,Virginia Dignum joostwestra@gmail.com. Overview. Introduction Adaptation to the trainee Organized adaptation of agents Scalability Conclusions. Serious Gaming. Dynamic Difficulty Adjustment.

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Scalable Adaptive Serious Games using Agent Organizations

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  1. Scalable Adaptive Serious Games using Agent Organizations Joost Westra, Frank Dignum,Virginia Dignum joostwestra@gmail.com

  2. Overview • Introduction • Adaptation to the trainee • Organized adaptation of agents • Scalability • Conclusions

  3. Serious Gaming

  4. Dynamic Difficulty Adjustment • Online adaptation: Continuously balance challenges in the game with (developing) skills of the trainee

  5. Current approaches • Fixed difficulties • Central control or no coordination • Mainly adjust simple subtasks

  6. Agent Approach • Agents part of the design process • Reasoning agents • Adapting agents • Specify boundaries of the adaptation (agent organization) Example: • Trainee is fire commander • 2 fireman agents • 1 victim agent • 1 agent controlling spreading of fires

  7. Aspects • User • Evolving skills (when learning) • Characters • Characters adapt independently • Characters active for long periods, so, adaptation should be believable • Keep storyline • Learning goals have to be maintained! • Adaptation must be coordinated! • Performance can not be measured separately for each skill and influence of each agent

  8. Story-line • Guarantee certain states are reached • Subtasks defined by scene scripts and landmarks • Connected by interaction structure • Describes game progress • Connecting scenes • Tasks in parallel • Extinguish Fire • Start • Get Access to Room • End • Evacuate Victim

  9. 2APL Agent • Agent Bidding Agent model Agent model Gather info Search building Extinguish fire Clear area End Start Get to site Secure area Evacuate victims Adaptation Engine • Coordinates task difficulty • Check with game model • Combinatorial auction • User model • Agent preferences Update • User Model Task Weights Skill Levels Preferences & Temination Scene States Applicable plans Plans Bid • Adaptation Engine Selection • Game Model

  10. Agent Perspective • Agents Propose actions to adaptation engine at “natural” synchronization points • Created to facilitate trainee’s objectives (optimize agent behavior relative to trainee’s performance!) • Not responsible for suitable combination • Conflict: • Stay as consistent as possible • Propose enough actions • Adaptation engine can request agents to terminate behavior if necessary for coordination

  11. 2APL Agent • Agent Bidding Agent model Agent model • NPC • NPC • NPC • NPC Framework Update • User Model • Game state User Performance Update Beliefbase Translate Task Weights Skill Levels • Game world Preferences & Temination Scene States Applicable plans Plans Bid • Adaptation Engine External Action Game Actions Selection • Game Model • Agent interface

  12. Scalabilty: Scenes • Agents can only execute plans of active scenes • Partial ordering gives a relatively low number of concurrent scenes • Sub-scenes: Even more fine grained pre-selection Kitchen Fire Start End Multiple victims Gather Info Search Building Get to site Extinguish Fire Secure Area Evacuate Victims

  13. Scalabilty: Agent implementation • Active Subscenes are put in beliefbase • Only plans with active sub-scenes are applicable • Only plans for current goals are applicable • Other active beliefs can also restrict the number of applicable plans

  14. Scalabilty: Believability • Agents estimate the Believabilty for each applicable action • Some actions clearly ruin the Believabilty of the agents • Believabilty 0 -> never suggest • Higher threshold than 0 is usually advisable -> even lower number of suggestions • Influence becomes bigger if the game progresses • The player has more expectations on the behavior of the NPC

  15. Scalabilty: Combination boundaries • Game model requirements can decrease the number of checked combinations • Influence greatly depend on the restriction • Easy: • At least one fireman should perform X • Only one fireman available • Only evaluate combinations with the fireman performing X • Difficult • X needs to be performed by at least two agents • No real indicator that other plans might not be better.

  16. Scalability: Example • Assumptions • 30 different scenes • 2 active at the same time • 4 subscenes • 2 active at the same time • 6 plans per subscene per agent • Results • Naive: • 720 active plans(30 scenes*4 sub-scenes*6 actions per sub-scene) • Agent Organization: • 12 active plans (6 actions per sub-scene *2 sub-scenes active per scene * 2 active scenes /2 for believability filtering) • 12.960.000 times as fast with only four agents

  17. Scalability: Example, number of Agents

  18. Conclusion • Continuous adaptation to the trainee • Agent based approach • Complex individual behavior and adaptation possible • Agent organization for coordination • Balance between individual flexibility and global story line maintaining learning goals • Minimal central control for more efficiency and more flexibility • More scalable than centralized approach

  19. Thank You! Questions? joostwestra@gmail.com

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