1 / 19

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.

daxia
Télécharger la présentation

Scalable Adaptive Serious Games using Agent Organizations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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

More Related