MOSIG Master 2ND
Agents can adapt their knowledge using different adaptation operators and modalities. These may be considered as knowledge, so they also may be adapted and selected.
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, we adapted this approach to the evolution of the way agents represent knowledge [Euzenat, 2014, 2017; Anslow & Rovatsos, 2015; Chocron & Schorlemmer, 2016]. We showed that cultural repair is able to converge towards successful communication and improves the objective correctness of alignments.
In the recent years, we developed different types of agents with various adaptation operators and modalities [Euzenat 2017]. These are used by agents to select knowledge. However, as such they can also be considered knowledge, hence selected.
The goal of this research topic is to consider agents able to use any of these operators and modalities Then experiments may be designed so that agents not only select target knowledge, but as well select the operators for selecting knowledge among a library of available methods.
They may do this judging by various criterion: speed, success, coverage. We want to study the impact of this process on similar long term criterion applied to target knowledge.
Another alternative, is to have experiments using such agents and having the environment selecting those with the fittest choices.
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]
[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 & Shvaiko, 2013] Jérôme Euzenat, Pavel Shvaiko, Ontology matching, 2nd edition, Springer-Verlag, Heildelberg (DE), 2013
[Euzenat, 2014] Jérôme Euzenat, First experiments in cultural alignment repair (extended version), in: Proc. 3rd ESWC workshop on Debugging ontologies and ontology mappings (WoDOOM), Hersounisos (GR), LNCS 8798:115-130, 2014 ftp://ftp.inrialpes.fr/pub/exmo/publications/euzenat2014c.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 ftp://ftp.inrialpes.fr/pub/moex/papers/euzenat2017a.pdf
[Mesoudi 2006] Alex Mesoudi, Andrew Whiten, Kevin Laland, Towards a unfied science of cultural evolution, Behavioral and brain sciences 29(4):329–383, 2006 http://alexmesoudi.com/s/Mesoudi_Whiten_Laland_BBS_2006.pdf
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012
Reference number: Proposal n°2746
Master profile: M2R MOSIG, M2R MSIAM, M2R Informatics.
Advisor: Jérôme Euzenat (Jerome:Euzenat#inria:fr) and Manuel Atencia (Manuel:Atencia#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.
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