Sought profile: Knowledge evolution may be studied using multi-agent simulations and publishing results in the top multi-agent conferences. To report reproducible experiments, we took steps to automate the experiment design, processing, analysis and publication. This involves describing explicitly the hypotheses, initial conditions, processes, measures, graphic output and statistical tests. We are seeking cooperation with a scientist having a complementary standpoint on these issues. This complementation may come from various different angles, e.g. providing a comprehensive and stronger methodology for multi-agent simulation, further enforcing open (FAIR) research and reproducibility, or semantically describing and exploiting experiment descriptions.
About mOeX: Cultural evolution is the application of evolution theory to culture [Messoudi 2006]. It is now widely acknowledged in social sciences and the humanities. mOeX adopts a computational approach to the study of the cultural evolution of knowledge, determining, in silico, properties of knowledge that artificial agents may obtain. Our ambition is to understand and develop general mechanisms by which a society evolves its knowledge. For that purpose, we combine knowledge representation and experimental cultural evolution methods [Steels, 2012]. The former provides formal models of knowledge; the latter provides a well-defined framework for studying situated evolution. We consider societies of agents representing their knowledge and adapting it when interacting with each other. We study the global properties of local adaptation operators both
This action is part of an ambitious program towards what we call cultural knowledge evolution within the MIAI Knowledge communication and evolution chair.
Mission: The rôle of the recruited person will be to help us improve our experimental methodology in particular in order to ensure better reproducibility.
We currently have a complete workflow (see https://sake.re) allowing us to perform multi-agent simulation, collect output data, analyse it and publish it. This framework is based on a collection of tools (git, java, docker, jupyter. In order to strenghen our methodology we are looking for proposals to reengineer our process (making it smoother, easier, more robust) and/or rationalising our home-made agent-similation strategy. This may be tied to research on agents-simulation frameworks, knowledge evolution game framework, reproducible research or scientific knowledge graphs.
Collaboration: The recruited person will be in connection with most of the team: researchers, doctoral students, master students who carry out experimental work on cultural knowledge evolution.
This post-doc proposal closely ties scientific skills (multi-agent simulation, cultural evolution, reproducible research) and technical application of these (designing methodlogies, implementing usable framework). The most successful outcome of the project would be published results put in practice in our day-to-day activities.
Hence, the activities may be quite diverse including solution design, software development, user study and paper writing.
Keywords: multi-agent, agent-based simulation, methodology, open data, science, reproducibility, semantic web
References:
[Bourahla, 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 https://moex.inria.fr/files/papers/bourahla2021a.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
[van den Berg, 2021] Line van den Berg, Manuel Atencia, Jérôme Euzenat, A logical model for the ontology alignment repair game, Autonomous agents and multi-agent systems 35(2):32, 2021 https://moex.inria.fr/files/papers/vandenberg2021a.pdf
Links
Qualification: PhD or equivalent.
Researched skills:
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.
Environnement: The work will be carried out in the mOeX team common to INRIA & Laboratoire d'Informatique de Grenoble. 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.
Language: Our working language is English (and Grenoble is a city in which it can easily be used)
Hiring date: 2022.
Duration: 12-18 months (hiring opportunities afterwards)
Salary: From 2125€/month (benefits included, net before income tax), i.e., 2653€/month gross.
Contact: For further information, contact Jerome:Euzenat#inria:fr.
Procedure:
Contact us (do not wait).
There are two opportunities to address that topic:
File: Provide Vitæ, motivation letter, 2 relevant or significant publications and reference letters.