Adaptation

Adaptation

Adaptation encapsulates all the functionalities of GIMME. It is the highest level class, responsible for initializing and executing adaptation iterations. As instances of Adaptation can be initialized and executed whenever, adaptations can dynamically be reconfigured with different arguments. No direct coupling exists between the player and task models and Adaptation and so, even if the number of players or tasks change, the module can be executed.

Constructor and Attributes

Constructor

Adaptation()

Methods

init(playerModelBridge, taskModelBridge, name, configsGenAlg): void

Description

This module initializes the Adaptation, getting it ready for the execution of iterations.

Parameters

Name: expected type	Default value	Description
playerModelBridge: PlayerModelBridge	-	The connector for the players data model
taskModelBridge: TaskModelBridge	-	The connector for the tasks data model
name: string	-	A name for the Adaptation (for debug or if identification is needed)
configsGenAlg: ConfigsGenAlg	-	The configurations (coalitions structure) generation algorithm to be used in the adaptation iterations

iterate(): Dictionary { groups: int[][], profiles: InteractionsProfile[], avgCharacteristics: PlayerCharacteristics[], adaptedTaskId: int }

Description

This method triggers the beginning of a new iteration using the current Adaptation configuration.

selectTask(possibleTaskIds, bestConfigProfile, avgCharacteristics): int

Description

This method selects a new task tailored to a group through its profile and average state. It returns the id of that task in the TaskModelBridge.

Name: expected type	Default value	Description
possibleTaskIds: int[]	-	a list of possible tasks to choose
bestConfigProfile: InteractionsProfile	-	the profile to consider
avgCharacteristics: PlayerCharacteristics	-	the average characteristics of the group

Example

statisticsCourseAdapt = Adaptation()
statisticsCourseAdapt.init(playerBridge, taskBridge, regAlg = KNNRegression(3), configsGenAlg = GIMMEConfigsGen(numberOfConfigChoices=50, preferredNumberOfPlayersPerGroup = 5, PlayerCharacteristics(ability=0.5, engagement=0.5)), fitAlg = WeightedFitness(), name="Statistics Course Adaptation")

#do 50 iterations of statisticsCourseAdapt and print each chosen configuration
for i in range(50):
    print(statisticsCourseAdapt.iterate()["groups"]) #print chosen configuration