Cultural knowledge evolution considers how agents can evolve their knowledge through communicating and adapting their knowledge. It may be considered that agents implement a specific distributed belief revision operation, providing an evolutionary selection function.
Cultural knowledge evolution deals with the evolution of knowledge representation in a group of agents inspired by cultural evolution [Mesoudi, 2011]. 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.
Cultural knowledge evolution may be considered as performing some kind of belief revision [Fermé and Hansson, 2018]: when new observations contradict their current knowledge and beliefs, agents adapt them. Partial meet revision is the most natural syntactic revision operator [Alchourron et. al., 1985]. It computes the intersection of selected maximal sub-theories consistent with the formula by which it is revised. Partial meet revision seems a good compromise, but the difficulty lies in the design of a 'good' selection operation.
It may be considered that each agent simply selects one sub-theory (this is called maxichoice revision). This can be considered as a realistic approach since this is what individuals usually tend to do. Then through cultural evolution mechanisms, the relevant subtheories will be selected and only the viable ones will remain. As shown in [Bourahla et. al., 2021], such theories may not be the same, but will be compatible with the agent environment and societies which have selected them. This could be considered as a distributed reconstruction of partial meet revision in which the selection operator takes an evolutionary interpretation: the subtheories will be selected by the society of agents with respect to the environment in which they are. This selection operator would however be different from what is usually understood [Liu and Williams, 2001; Dragoni and Giorgini, 2003]: it would be asynchronous because not all agents revise their knowledge at the same time and interleaved as they would revise it simultaneously from different observations.
The goal of the topic is to investigate the relation between, especially partial meet, belief revision and cultural knowledge evolution seen as a belief revision operator. The kind of questions that follow are:
The work could be expected to unfold as follows:
This topic focusses on how cultural knowledge evolution could implement belief revision. As such, this topic can be continued in PhD on its own if the topic shows promises. However, there is more to cultural knowledge evolution with respect to belief revision: cultural knowledge evolution may also be seen as revising common distributed knowledge, e.g. networks of ontologies [Euzenat, 2015], or as using it, e.g. in a dynamic epistemic logic context [van den Berg, 2021] This master topic would be a good basis for considering these more general issues.
References:
[Alchourron et. al., 1985] Carlos Alchourrón, Peter Gärdenfors, David Makinson, On the logic of theory change: partial meet contraction and revision functions, Journal of symbolic logic 50(2):510–530, 1985
[Bourahla et. al., 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
[Dragoni and Giorgini, 2003] Aldo Franco Dragoni, Paolo Giorgini, Distributed belief revision, Autonomous agents and multi-agent systems 6(2):115-143, 2003
[Euzenat, 2015] Jérôme Euzenat, Revision in networks of ontologies, Artificial intelligence 228:195-216, 2015 https://exmo.inria.fr/files/publications/euzenat2015a.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
[Fermé and Hansson, 2018] Edoardo Fermé, Sven Ole Hansson, Belief change: introduction and overview, Springer, Cham (CH), 2018
[Liu and Williams, 2001] Wei Liu, Marie-Anne Williams, A framework for multi-agent belief revision, Studia logica 67(2):291-312, 2001
[Mesoudi, 2011] Alex Mesoudi, Cultural evolution: how Darwinian theory can explain human culture and synthesize the social sciences, Chicago university press, Chicago (IL US), 2011 See also: 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, Cultural knowledge evolution in dynamic epistemic logic, Phd thesis, Université Grenoble Alpes, 2021 https://moex.inria.fr/files/theses/thesis-vandenberg.pdf
Links:
Master profile: M2, research oriented.
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.
Perspectives: There is possibility to pursue in PhD, especially related to belief revision and cultural knowledge evolution.
Procedure: Contact us and provide vitæ and possibly motivation letter and references.