Cultural knowledge evolution considers how agents can evolve their knowledge through communicating and adapting their knowledge. Dynamic epistemic logics can represent knowledge, beliefs and actions, such as communicating. Adaptation may be considered as belief revision.
Cultural knowledge evolution deals with the evolution of knowledge representation in a group of agents. For that purpose, cooperating agents interact with their environment and other agents. When these agents find their behaviour inadequate, which can be detected by failing to understand others, they use operators to adapt their beliefs. 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 through improving the intrinsic knowledge quality.
Multi-agent dynamic epistemic-doxastic logics (DEL for short) are dedicated to describe agent knowledge and beliefs and modelling agent actions, such as communicating, through dynamic modal operators [van Ditmarsch et al., 2007]. Moreover, they have been the support for various work in belief revision [Baltag and Smets, 2006; van Benthem, 2007] that enable them to revise beliefs in the face of new information.
Hence, dynamic epistemic logics and belief revision seems particularly appropriate to model cultural knowledge evolution in general. Some work has modelled specific cultural knowledge evolution experiments with dynamic epistemic logics [van den Berg, 2021]. It has shown some limitation of this bottom-up approach as well as the promises offered by such models.
This thesis aims at adopting a top-down approach to the problem: identifying the key elements of cultural (knowledge) evolution and what they needs in order to be modelled.
Solving this problem may involve:
This work is part of an ambitious program towards what we call cultural knowledge evolution partly funded by the MIAI Knowledge communication and evolution chair.
[Baltag and Smets, 2006] Alexandru Baltag, Sonja Smets, Dynamic belief revision over multi-agent plausibility models, In: Proc. of 6th LOFT, pp11–24. University of Liverpool, 2006
[Bourahla, 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
[Euzenat, 2017] Jérôme Euzenat, Communication-driven ontology alignment repair and expansion, Proc. 26th IJCAI, Melbourne (AU), pp185-191, 2017 https://moex.inria.fr/files/papers/euzenat2017a.pdf
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012
[van den Berg, 2021] Line van den Berg, Cultural knowledge evolution in dynamic epistemic logic, Phd thesis, Université Grenoble Alpes, 2021
[van Benthem, 2007] Johan van Benthem, Dynamic logic for belief revision, Journal of applied non-classical logics 17(2):129-155, 2007 https://staff.fnwi.uva.nl/j.vanbenthem/DL-BR-new.pdf
[van Ditmarsch et al., 2007] Hans van Ditmarsch, Wiebe van der Hoek, Barteld Kooi, Dynamic epistemic logic, Springer, Synthese library 337, 2007
Qualification: Master or equivalent in computer science.
Doctoral school: MSTII, Université Grenoble Alpes.
Advisor: Jérôme Euzenat (Jerome:Euzenat#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: October 2022.
Duration: 36 months
Salary: 1982€/month (gross, before social contributions and taxes).
Deadline: as soon as possible.
Contact: For further information, contact us.
Procedure: Contact us and/or apply to the corresponding proposal (https://jobs.inria.fr/public/classic/fr/offres/2022-04583). See also here.
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.