Joost Westra, Frank Dignum,Virginia Dignum [email protected] Scalable Adaptive Serious Games...
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Transcript of Joost Westra, Frank Dignum,Virginia Dignum [email protected] Scalable Adaptive Serious Games...
Joost Westra,Frank Dignum,Virginia Dignum
Scalable Adaptive Serious Games using Agent Organizations
Overview
Introduction Adaptation to the trainee Organized adaptation of agents Scalability Conclusions
Dynamic Difficulty Adjustment
Online adaptation:Continuously balance challenges in the game with (developing) skills of the trainee
Current approaches
Fixed difficulties Central control or no coordination Mainly adjust simple subtasks
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
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
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
• Start
• Get Access to Room
• Evacuate Victim
• Extinguish Fire
• End
Adaptation Engine
Coordinates task difficulty Check with game model Combinatorial auction
• User model• Agent preferences
Agent model• 2APL Agent
Agent model• Agent Bidding
• User Model
• Adaptation Engine
Update
Plans Bid
Task Weights Skill Levels
Selection
Preferences & Temination
Scene StatesApplicable plans
• Game Model
Start Get to site
Gather info
Secure area
Search building
Evacuate victims
Extinguish fire Clear area End
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
Framework
Agent model• 2APL Agent
Agent model• Agent Bidding
• Game world
• Game state
• Agent interface
• User Model
• Adaptation Engine
• NPC
• NPC
• NPC
• NPC
Update
User Performance
Translate
Plans Bid
Update Beliefbase
Task Weights Skill Levels
External ActionSelectionGame Actions
Preferences & Temination
Scene StatesApplicable plans
• Game Model
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
Gather Info Search Building
Secure Area Evacuate Victims
Extinguish FireGet to site
Multiple victims
EndStart
Kitchen Fire
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
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
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.
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
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