By performing tasks, agents acquire knowledge that can be used in other contexts for other tasks. We aims at understanding how several agents performing, collaboratively or competitively, different tasks for achieving different goals, may acquire better knowledge.
Cultural knowledge evolution deals with the evolution of knowledge representation in a group of agents. For that purpose, cooperating agents adapt their knowledge to the situations they are exposed to and the feedback they receive from others. 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, 2014; 2017]. We have shown that cultural repair is able to converge towards successful communication through improving the objective knowledge quality.
In most of the work so far, agents are designed for dealing with one single task. Hence, their knowledge is shaped for this particular task. However, pursuing several goals at once, and performing different tasks to that end, would benefit from developing non-specialised (multi-purpose) knowledge. It is expected that agents developing such knowledge would have more facility to address different tasks.
On the other side of the spectrum, we may consider societies of complementary and very specialised agents. This includes the competition of several agents able to perform the same specific task. Such societies are more specifically considered by economic approaches, in particular game theory.
As an example, one may consider agents pursuing different goals for subsisting: being fed and in good health. This involves various tasks such as growing food, producing medicine and providing care. In turns, this involves other tasks such as extracting matters, moving matter, manufacturing medicine, moving manufactured products such as food and medicine, training nurses, etc. To achieve their goals, all agents may be a gardener, a cook, a nurse, a nurse trainer and a transporter. Skills developed for transporting people, may be reused for transporting food and vice-versa, knowledge developed for training gardiners, may be reused for training pharmacists. However, in other societies agents may specialise into gardening, caring or teaching and the bests at each task may be selected in a competitive market.
This thesis proposal thus considers two different dimensions together: on the one hand, whether agents are expected to perform one or several tasks; on the other hand, whether they work collaboratively, independently or competitively.
We consider cultural knowledge evolution in such a context with the aim to understand the impact of such an organisation on the knowledge developed by each agent and by the society as a whole. Knowledge may be considered under the light of its contribution to reaching the agents' goals or for its own value, such as its correctness and completeness. Knowledge may also be measured at the level of the society. For instance, one may want to measure the degree at which the wealth is shared. In addition, we want to assess are the differential benefits of each approach: this may be short-term efficiency or long-term resilience.
These problems may be treated both theoretically or experimentally
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 and may be carried out in collaboration with international partners.
References:
[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
[Steels, 2012] Luc Steels (ed.), Experiments in cultural language evolution, John Benjamins, Amsterdam (NL), 2012
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Qualification: Master or equivalent in computer science.
Researched skills:
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: as soon as possible.
Duration: 36 months
Salary: 1760€/month (gross, before social contributions and taxes).
Deadline: as soon as possible.
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.