Cultural knowledge evolution considers how agents evolve their knowledge through communicating with each other. However, changes in their environment and especially disruptive events may render this knowledge inaccurate. We want to assess the impact of knowledge diversity in recovering from disruption.
Cultural evolution applies the theory of evolution to culture. It has been applied to various aspects of our life in societies: from customs to languages, from boat shapes to company structures [Mesoudi, 2011]. Artificial cultural knowledge evolution deals with the evolution of knowledge representation in a group of computer agents. Our ambition is to understand and develop general mechanisms by which a society evolves its knowledge. For that purpose, cooperating agents adapt their knowledge to the situations they are exposed to and the feedback they receive from others. After playing this repeatedly, it is possible to observe the properties of the resulting knowledge. This framework has been considered in the context of evolving natural languages [Steels, 2012]. We have applied it to ontology alignment repair, i.e. the improvement of incorrect alignments [Euzenat, 2017] and ontology evolution [Bourahla et al., 2021]. We have shown that it converges towards successful communication, improves the intrinsic knowledge quality but preserves the diversity of agent knowledge.
Knowledge diversity is important because it has been argued generally that diverse teams have an advantage in problem solving [Hong and Page, 2004]. Moreover, in an evolutionary context, diversity is considered a source of resilience for facing change and especially disruptive events. We have proposed a measure of diversity that takes into account both the proportion of agents sharing some knowledge and the similarity between their knowledge representation [Bourahla et al., 2022].
So far, experiments have concerned the development of knowledge with a static environment. We aim to study how agents can cope with environment change and, in particular, disruptive events and understand whether more diverse population are resilient to changes. For that purpose, it will be necessary to introduce environment changes, in the context of this type of experiments, likely in a probabilistic way combining frequency and intensity of the changes. This would introduce disruptive events. Additionally, it is possible to introduce lethal elements in the environments. Such elements, by causing the death of agents, will lead to a more direct knowledge selection by the environment, rather than by the agents themselves.
The main work to be performed is thus:
This work is part of an ambitious program towards what we call artificial cultural knowledge evolution. It is part of the ACBE project and as such may lead to a PhD thesis.
References:
[Bourahla et al., 2021] Yasser Bourahla, Manuel Atencia, Jérôme Euzenat, Knowledge improvement and diversity under interaction-driven adaptation of learned ontologies, Proc. 20th AAMAS, London (UK), pp242-250, 2021 https://moex.inria.fr/files/papers/bourahla2021a.pdf
[Bourahla et al., 2022] Yasser Bourahla, Jérôme David, Jérôme Euzenat, Meryem Naciri, Measuring and controlling knowledge diversity, Proc. 1st JOWO workshop on formal models of knowledge diversity (FMKD), Jönköping (SE), 2022 https://moex.inria.fr/files/papers/bourahla2022c.pdf
[Euzenat, 2017] Jérôme Euzenat, Communication-driven ontology alignment repair and expansion, in: Proc. 26th International joint conference on artificial intelligence (IJCAI), Melbourne (AU), pp185-191, 2017 https://moex.inria.fr/files/papers/euzenat2017a.pdf
[Hong and Page, 2004] Lu Hong, Scott Page, Groups of diverse problem solvers can outperform groups of high-ability problem solvers, Proceedings of the national academy of sciences 101(46):16385–16389, 2004
[Mesoudi, 2011] Alex Mesoudi, Cultural evolution: how Darwinian theory can explain human culture and synthesize the social sciences, Chicago university press, Chicago (IL US), 2011 See also: Alex Mesoudi, Andrew Whiten, Kevin Laland, Towards a unfied science of cultural evolution, Behavioral and brain sciences 29(4):329–383, 2006 https://www.alexmesoudi.com/publication/mesoudi-towards-2006/Mesoudi_Whiten_Laland_BBS_2006.pdf
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012
Links:
Reference number: Proposal n°3240
Master profile: M2, research oriented.
Advisor: Jérôme Euzenat (Jerome:Euzenat#inria:fr) and Jérôme David (Jerome:David#univ-grenoble-alpes.fr)
Team: The work will be carried out in the mOeX team common to INRIA & Université Grenoble Alpes. mOeX is dedicated to study knowledge evolution through adaptation. It gather permanent researchers from the Exmo team which has taken an active part these past 15 years in the development of the semantic web.
Laboratory: LIG.
Place of work: The position is located at INRIA Grenoble Rhône-Alpes, Montbonnot (near Grenoble, France) a main computer science research lab, in a stimulating research environment.
Perspectives: There is possibility to pursue in PhD, related to this topic.
Procedure: Contact us and provide vitæ and possibly motivation letter and references.