How do agent populations match their ontologies

PhD position / Sujet de thèse

When two populations of agents encounter, they do not necessarily organise their knowledge about their environment in the same way. They may however attempt at communicating and progressively align their knowledge. We aim at studying the effectiveness and robustness of such a process.

These problems may be approached either theoretically or experimentally, through the framework of cultural evolution. 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 adapt the successful cultural language evolution approach [Steels, 2012] to the evolution of the way agents represent knowledge [Euzenat, 2014; Anslow & Rovatsos, 2015; Chocron & Schorlemmer, 2016]. We have applied this approach to ontology alignment repair, i.e., the improvement of incorrect alignments [Euzenat, 2014; 2017]. For that purpose, we performed a series of experiments in which agents react to mistakes in ontology alignments —expressing relations across ontology concepts [Euzenat & Shvaiko, 2013]. Agents only know about their ontologies and alignments with others and they act in a fully decentralised way. We showed that cultural repair is able to converge towards successful communication through improving the objective correctness of alignments.

This proposal focusses on the behaviour of populations of agents. Its goal is to study how agents in a population, sharing the same ontology, can take advantage of what is learnt by the others agents interacting with agents of another population. Games will have to be designed for agents to locally adapt alignments between their ontology and tho one used by other populations. It is expected that different agents, in the same population, do not necessarily end up with the same alignments.

We want first to understand when this occurs as well as what can be done for the agents to share with their peers the correspondences that they found. Roughly, three modalities can be compared:

In the two latter modalities, different operations may be used to aggregate the results brought by other agents.

Finally, it is possible to consider more than two populations and/or ontologies, eventually by splitting and merging populations and to study its impact on the alignment process.

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, e.g., new adaptation operators, or methodological, e.g., better experimental procedures, contributions.

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 & 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), 2017 (to appear) ftp://ftp.inrialpes.fr/pub/moex/papers/euzenat2017a.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: 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 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.

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 2017 (October 1st in principle).

Duration: 36 months

Salary: From 1600 EUR/month (benefits included, net before income tax), i.e., 2000 EUR/month gross.

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.