20230822-IKEM: Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.

This archive contains the results of a multi-agent simulation experiment [1] carried out with Lazy lavender [2] environment.

Experiment Label: 20230822-IKEM

Experiment design: Agents interact by playing games with objects and adapt their ontologies to agree on decision taking. Intrinsic exploration motivation (individual / social and direct / indirect curiosity, creativity and non-exploration), that influences which object and, if the agent is social, partner(s) are chosen for the interaction, is introduced.

Experiment setting: Agents learn decision trees (transformed into ontologies); choose the objects (and partners) to interact with; adapt by splitting their leaf nodes

Hypotheses: 1) Agents will be able to fulfil their motivation in terms of increased exploration (exploratory motivations) and decreased exploration (non-exploratory motivation). 2) Agents with exploratory motivation will be more complete, but less accurate than the baseline and non-exploration. 3) Curious agents will be more accurate and complete, but converge slower in comparison with creativity. 4) In more complex settings (higher number of agents and properties), exploration leads to more completeness, accuracy, distance and faster convergence than the baseline. 5) Agents with non-exploratory motivation will be less accurate and complete than the baseline. 6) The indirect learning models have a higher accuracy and completeness than the direct models. 7) Agents with socially oriented intrinsic motivation will have less diverse knowledge, but agree more and converge faster than agents with individual intrinsic motivation. 8) Heterogeneously motivated agents will have a higher accuracy and completeness, but lower diversity and converge slower than homogeneously motivated agents.

Detailed information can be found in index.html or notebook.ipynb.

[1] https://sake.re/20230822-IKEM
[2] https://gitlab.inria.fr/moex/lazylav/