[PlanetKR] PhD position in Epistemic Reasoning and Multi-Agent Learning, IRIT, Toulouse University

[lorini]

PhD position in Epistemic Reasoning and Multi-Agent Learning

Institut de Recherche en Informatique de Toulouse (IRIT)

Toulouse University

France

 

The proposed PhD thesis will be developed in context of the ANR project entitled “Learning through epistemic reinforcement” (EpiRL) which was accepted in July 2022 and will be carried out between 2023 and 2027. 

The PhD thesis will start in September 2023 and will be funded on a three-year contract with gross salary of approximately 2000€ per month. 

Description of the research project

The need for an integration of machine learning (ML) and knowledge representation (KR) has been largely emphasized in the artificial intelligence (AI) community. 

According to (Valiant, 2003), a key challenge for computer science is to come up with an integration of the two most fundamental phenomena of intelligence, namely, the ability to learn from experience and 

the ability to reason from what has been learned. The PhD thesis will be focused on the integration of epistemic reasoning and multi-agent learning. Different solutions of integration will be explored including:

·      how to combine an agent’s capacity to attribute beliefs to other agents and to reason strategically with the capacity to form predictions about future events and future agents’ actions based on its past experiences;

·      how to relate the notion of reward to mental attitudes including beliefs and desires;

·      how to include in the description of a state used in an agent’s reward function the representation of other agents’ beliefs.

To this aim, we plan to combine concepts and methods from epistemic logic and planning (Fagin et al., 1995; Lorini, 2020; Davila et al., 2021), theories of learning in games and 

multi-agent learning (Fudenberg & Levine, 1998; Tuyls & Weiss, 2012), and the epistemic theory of convention (Lewis, 1969).  We expect the kind of integration proposed in the context of 

PhD thesis to be relevant for AI applications in social robotics and human-machine interaction, given the importance of combining reasoning and learning as well as prediction and explanation for such applications.

References
- J. Fernandez Davila, D. Longin, E. Lorini, F. Maris (2021). A Simple Framework for Cognitive Planning. In Proceedings of AAAI-21, pp. 6331-6339. 
- R. Fagin, J. Y. Halpern, Y. Moses, and M. Vardi. Reasoning about Knowledge. MIT Press, Cambridge, 1995.
- D. Fudenberg, D. K. Levine. The Theory of Learning in Games. MIT Press, Cambridge, 1998. 
- D. K. Lewis. Convention: a philosophical study. Harvard University Press, Cambridge, 1969. 

- Lorini, E. (2020). Rethinking epistemic logic with belief bases. Artificial Intelligence, 282.
- K. Tuyls and G. Weiss. Multiagent Learning: Basics, Challenges, and Prospects. AI Magazine, 33(3):41, 2012.
- L. G. Valiant. Three Problems in Computer Science. Journal of the ACM, 50(1):96-99,2003. 
 
Candidate profile

The PhD is at the intersection of logic, game theory and machine learning. The ideal candidate should have a strong mathematical background and a master’s degree in Logic, 

Computer Science or Mathematics. Ideally, it should be familiar with propositional logic, modal logic, epistemic and temporal logics, the theory of static and sequential games as well as with basic notions of machine learning.  

 
PhD supervisor

The PhD supervisor is Emiliano Lorini, CNRS research director at the Institut de Recherche en Informatique de Toulouse (IRIT). See https://www.irit.fr/~Emiliano.Lorini/ <https://www.irit.fr/~Emiliano.Lorini/> for more information.


How to apply

Please email your detailed CV, a motivation letter, and transcripts of bachelor's degree and master’s degree to Emiliano.Lorini at irit.fr <mailto:Emiliano.Lorini at irit.fr>. 


APPLICATION DEADLINE FOR FULL CONSIDERATION: May 1st, 2023.












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