**Agent adapt ontologies to agree on decision taking**

Date: **20210224** (**Yasser Bourahla**)

**5** runs; **100000** games

Hypothesis: **['hypotheses of 20200623-DOLA are still verified for different training algorithms and starting with random ontologies',
'']**

Variation of: **20200623-DOLA**

Decision tree learning algorithms: **RAND,ID3,C45,CART**

independent variables: **['numberOfClasses', 'ratio', 'numberOfFeatures', 'trainer']**

dependent variables: **['ssrate', 'accuracy', 'distance']**

Date: **20210224** (**Yasser Bourahla**)

LazyLavender hash: **37dffefd52354174aac791c6513cae2a564428f7**

Link to lazylavender

Parameter file: params.sh

Executed command (script.sh):

**BEWARE: REPRODUCING THE ANALYSIS MAY TAKE A CONSIDERABLE AMOUNT OF TIME.**

The independent variables have been varied as follows:

Note here, the percentage of none zero distances is 98.52% (higher than previous 90.78%) because the experiments are carried with 20 agents only.

ANOVA results (without random ontology initialisation):

Confidence intervals are all within: [-0.0487, 0.0585]. There is no significant difference between the learning algorithms.

The confidence intervals are all within [-1225, - 0.0167]. They are left skewed. The accuracy is higher when a learning algorithm is used.