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Communication Dans Un Congrès Année : 2022

Knowledge transmission and improvement across generations do not need strong selection

Résumé

Agents have been used for simulating cultural evolution and cultural evolution can be used as a model for artificial agents. Previous results have shown that horizontal, or intra-generation, knowledge transmission allows agents to improve the quality of their knowledge to a certain level. Moreover, variation generated through vertical, or inter-generation, transmission allows agents to exceed that level. Such results were obtained under specific conditions such as the drastic selection of agents allowed to transmit their knowledge, seeding the process with correct knowledge or introducing artificial noise during transmission. Here, we question the necessity of such measures and study their impact on the quality of transmitted knowledge. For that purpose, we combine the settings of two previous experiments and relax these conditions (no strong selection of teachers, no fully correct seed, no introduction of artificial noise). The rationale is that if interactions lead agents to improve their overall knowledge quality, this should be sufficient to ensure correct knowledge transmission, and that transmission mechanisms are sufficiently imperfect to produce variation. In this setting, we confirm that vertical transmission improves on horizontal transmission even without drastic selection and oriented learning. We also show that horizontal transmission is able to compensate for the
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Dates et versions

hal-03939919 , version 1 (16-01-2023)

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Paternité

Identifiants

  • HAL Id : hal-03939919 , version 1

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Yasser Bourahla, Manuel Atencia, Jérôme Euzenat. Knowledge transmission and improvement across generations do not need strong selection. AAMAS 2022 - 21st ACM international conference on Autonomous Agents and Multi-Agent Systems, May 2022, online, United States. pp.163-171. ⟨hal-03939919⟩
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