20220109-MTOA

Date: 2021-12-09 (Andreas Kalaitzakis)

An agent will improve its accuracy on one task by carrying out another task.

20 runs; 80000 games

Agents learn multi-task ontologies. Agents will coordinate on a set of decision tasks, making a decision about an object.

Variables independent variables: ['numberOfTasks']

dependent variables: ['t1_accuracy']

Date: 2022-01-09 (Andreas Kalaitzakis)

Computer: Dell Precision-5540 (CC: 12 * Intel(R) Core(TM) i7-9850H CPU @ 2.60GHz with 16GB RAM OS: Linux 5.4.0-92-generic)

Duration : 15 minutes

Lazy lavender hash: ceb1c5d1ca8109373d293b687fc55953fce5241d

Parameter file: params.sh

Executed command (script.sh):

Full results can be found at: |

Table 1 consists of the final achieved ontology accuracy values, i.e., the accuracy after the last iteration, with respect to task 1. Each column corresponds to a different number of tasks, while each row corresponds to a different run, for the same number of tasks.

Out[7]:

Figure 1 depicts the evolution of the accuracy for task_1 (yaxis), depending on the number of tasks. The x-axis is scaled to the number of tackled tasks, and thus does not correspond to the total interactions for each run. Each point x,y corresponds to the accuracy for task 1 when x interactions regarding task 1 have taken place. The figure demonstrates how knowledge with respect to one task is affected by tackling another task. Results support the hypothesis, showing that the presence of additional tasks affects the accuracy for task_1. In particular, tackling one additional task is shown to improve, on this particular setting, the accuracy for task 1 by 33% while tackling 3 additional tasks improves the accuracy fortask 1 by 39 %.

Based on Table 1, we perform one-way ANOVA, testing if the independent variable "numberOfTasks" has a statistically significant effect on the final average accuracy values.

Performing one-way ANOVA on the values of Table 1 returns a p value of ~1.06e-13. This value is lower than 0.05, thus we consider that the number of tackled tasks statistically significantly affects the obtained final task 1 accuracy values.

In this work, we hypothesize that by agents will improve their knowledge on one task, by carrying out a different one (additional). Results support this hypothesis, suggesting the formation of general purpose knowledge.