Phd Position F/m

Le descriptif de l’offre ci-dessous est en Anglais_

**Type de contrat **:CDD

**Niveau de diplôme exigé **:Bac + 5 ou équivalent

**Fonction **:Doctorant

**A propos du centre ou de la direction fonctionnelle**:
The Centre Inria de l’Université de Grenoble groups together almost 600 people in 22 research teams and 7 research support departments.

Staff is present on three campuses in Grenoble, in close collaboration with other research and higher education institutions (Université Grenoble Alpes, CNRS, CEA, INRAE,), but also with key economic players in the area.

The Centre Inria de l’Université Grenoble Alpe is active in the fields of high-performance computing, verification and embedded systems, modeling of the environment at multiple levels, and data science and artificial intelligence. The center is a top-level scientific institute with an extensive network of international collaborations in Europe and the rest of the world.

**Contexte et atouts du poste**:
**Doctoral school**: MSTII, Université Grenoble Alpes.

**Advisor**: Jérôme Euzenat (Jerome:Euzenat#inria:fr) and Jérôme David (Jerome:David#univ-grenoble-alpes.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.

**Mission confiée**:
**Cultural knowledge evolution and multiagent reinforcement learning share some of their prominent features. Putting explicit knowledge at the heart of the reinforcement process may contribute to better explanation and transfer.**

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, 2017] and ontology evolution [Bourahla _et al._, 2021]. We have shown that it converges towards successful communication through improving the intrinsic knowledge quality.

Reinforcement learning is a learning mechanism adapting the decision making process for maximising the reward provided by the environment to the actions performed by agents [Sutton and Barto, 1998]. Many multi-agent versions of reinforcement learning have also been proposed depending on the agent attitude (cooperative, competitive) and the task structure (homogeneous, heterogeneous) [Bučoniu _et al._, 2010].

From an external perspective, the two approaches operate in a similar manner: agents perceive their environment, perform an action, receive reward or punishment, adapt their behaviour in consequence. However, a look into the inner mechanisms reveals important differences: the emphasis on knowledge quality instead of reward maximisation, the lack of probabilistic or even gradual interpretation, and even the absence of explicit choice in action or adaptation. Hence these two knowledge acquisition techniques are close enough to suggest replacing one by the other and different enough to cross-fertilise.

This thesis position aims at further exploring the commonalities and differences between experimental cultural knowledge evolution and reinforcement learning. In particular, its purpose is to study which features of one technique may be fruitful in the context of the other and which may not.

For that purpose, one research direction is the introduction of knowledge-based reinforcement learning. In knowledge-based reinforcement learning, the decision-making process (the choice of the action to be performed) is obtained through accumulated explicit knowledge. Thus the adaptation performed after reward or punishment will have to directly affect this knowledge. This has the advantage that it allows to explain the decisions made by agents. It will also allow for explicit knowledge exchange among them [Leno da Silva _et al._, 2018].

This promotes a less utilitarian view of knowledge in which the evaluation of the performance of the system has to be disconnected from reward maximisation but to depend on the quality of the acquired knowledge. Of course, these two aspects need to remain related (the acquired knowledge must be relevant to the environment). This separation between knowledge and reward is useful when agents have to change environment or use their knowledge to perform various tasks.

Another use of reinforcement mechanisms relevant to cultural knowledge evolution is related to the motivation for agents to explore unknown knowledge territories [Colas _et al._, 2019]. By associating an intrinsic reward to the newly acquired knowledge, agents are able to improve the coverage of their knowle