In cultural knowledge evolution experiments, agents interact and evolve their knowledge. The communication of agents is only due to cooperation. However, symbolic knowledge may be directly transmitted from agent to agent and this is supposed to spread knowledge more efficiently. We aim at developing transmission mechanisms integrated within cultural knowledge evolution.
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.
Cultural evolution, contrary to biological evolution, allows for the unconstrained transmission of knowledge. Of course, it is transmitted from parents to children (vertical transmission), but it is also transmitted to peers and other peoples (horizontal transmission). There could be many ways in which this transmission is implemented in a society of agents.
This thesis proposal aims at developing these in a systematic way. Horizontal transmission may occur through agent-to-agent communication or in a broadcast mode (or broadcast restricted to a particular population). If broadcast if used, it is necessary to (a) define the way to reach consensus knowledge, that can properly be called culture, and (b) consider how agents alter their knowledge with respect to the consensus. The first case may resort to different kinds of voting or aggregating weighted alignments. Concerning vertical transmission, usual genetic programming techniques, such as mutation an cross-over, may be adapted to knowledge.
Expected contributions are firstly the design of agents applying different knowledge transmission mechanisms as well as the study of the relative benefits of such mechanisms.
This work is part of an ambitious program towards what we call cultural knowledge evolution. Its results may be of experimental or theoretical nature and it may provide practical contributions, e.g. new adaptation operators, or methodological contributions, e.g. better experimental procedures.
References:
[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, 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 https://exmo.inria.fr/files/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 https://moex.inria.fr/files/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
Links:
Qualification: Master or equivalent in computer science.
Researched skills:
Doctoral school: MSTII, Université Grenoble Alpes.
Advisor: Jérôme Euzenat (Jerome:Euzenat#inria:fr) and Manuel Atencia (Manuel:Atencia#inria:fr).
Group: The work will be carried out in the mOeX team common to INRIA & LIG. mOeX is dedicated to study knowledge evolution through adaptation. It gathers researchers which have taken an active part these past 15 years in the development of the semantic web and more specifically ontology matching and data interlinking.
Place of work: The position is located at INRIA Grenoble Rhône-Alpes, Montbonnot a main computer science research lab, in a stimulating research environment.
Hiring date: Fourth quarter 2019 (October 1st in principle).
Duration: 36 months
Contact: For further information, contact us.
Procedure: Contact us.
File: Provide Vitæ, motivation letter and references. It is very good if you can provide a Master report and we will ask for your marks in Master, so if you have them, you can join them.