Master 2ND YEAR Research
YEAR 2021–2022

Disruptive events in cultural knowledge evolution

Master topic / Sujet de master recherche

Agents can improve the accuracy of their knowledge through adaptation. However, disruptive events may render this knowledge inaccurate. We want to assess the impact of knowledge diversity in addressing this problem.

Cultural evolution is the application of evolution theory to culture [Messoudi 2006]. It may be addressed through multi-agent simulations [Steels 2012]. Experimental cultural evolution provides a population of agents with interaction games that are played randomly. In reaction to the outcome of such games, agents adapt their knowledge. It is possible to test hypotheses by precisely crafting the rules used by agents in games and observing the consequences.

Our ambition is to understand and develop general mechanisms by which a society evolves its knowledge. For that purpose, this approach has been adapted to the evolution of the way agents represent knowledge [Anslow & Rovatsos, 2015; Chocron & Schorlemmer, 2016; Euzenat, 2017; Bourahla et al., 2021]. In particular, we considered agents correcting their ontologies representing their environment in order to cooperate with other agents. We showed that agent knowledge is able to converge towards successful communication and improves the objective knowledge correctness. We also showed that this does not constrain agents to adopt the same ontology [Bourahla et al., 2021]. Hence knowledge diversity is preserved.

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 disruptive events.

The goal of this topic is to confirm this latter aspect and, in the context of this type of experiments, to introduce disruptive events. Two types of disruptive events are considered in this context: population-change is characterised by the introduction of a new and different agent population; environment-change is the introduction, or suppression, of some objects from the environment. 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: Defining precisely the type of disruptive events and designing experiments allowing for their introduction; Running the experiments and analysing their results. In particular it will compare the quality and recovery speed of the knowledge shared by different agent populations and identify the factors that impact them.

This work is part of an ambitious program towards what we call cultural knowledge evolution. It is part of the MIAI Knowledge communication and evolution chair and as such may lead to a PhD thesis.


[Anslow & Rovatsos, 2015] Michael Anslow, Michael Rovatsos, Aligning experientially grounded ontologies using language games, Proc. 4th international workshop on graph structure for knowledge representation, Buenos Aires (AR), pp15-31, 2015 [DOI:10.1007/978-3-319-28702-7_2]
[Bourahla et al., 2021] Yasser Bourahla, Manuel Atencia, Jérôme Euzenat, Knowledge improvement and diversity under interaction-driven adaptation of learned ontologies, Proc. 20th ACM international conference on Autonomous Agents and Multi-Agent Systems (AAMAS), London (UK), pp242-250, 2021
[Chocron & Schorlemmer, 2016] Paula Chocron, Marco Schorlemmer, Attuning ontology alignments to semantically heterogeneous multi-agent interactions, Proc. 22nd European Conference on Artificial Intelligence, Der Haague (NL), pp871-879, 2016 [DOI:10.3233/978-1-61499-672-9-871]
[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
[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 2006] Alex Mesoudi, Andrew Whiten, Kevin Laland, Towards a unfied science of cultural evolution, Behavioral and brain sciences 29(4):329–383, 2006
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012


Master profile: M2R MOSIG, M2R MSIAM, M2R Informatics.

Advisor: Jérôme Euzenat (Jerome:Euzenat#inria: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 and more specifically ontology matching.

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.

Procedure: Contact us and provide vitæ and possibly motivation letter and references.