Knowledge Representation and Reasoning
In artificial intelligence, physical or virtual systems evolving in an autonomous way must be provided with capacities of representation of their environment and reasoning. The research axis “Knowledge representation and reasoning” deals with the identification, modeling, representation and implementation of the different types of information (knowledge, beliefs, preferences, actions, and so on) and setting up reasoning necessary for such systems.
Logical languages (propositional, modal, weighted, etc.) and graphical models (Bayesian networks, abstract argumentation systems, conceptual networks, etc.) are used to represent the different information (qualitative or numerical) available. The choice of the knowledge representation language is crucial to obtain good properties in terms of expressiveness, spatial efficiency, algorithmic complexity, and so on. Once the language has been chosen and the desired reasoning has been modeled, it is necessary to design practical methods to realize this reasoning, and to focus on the computational aspects of these methods, which are important in order to judge their practical use. The research carried out within the “Knowledge representation and reasoning” axis is based on the following themes and issues:
- Belief Dynamics
Development of approaches allowing the updating of an agent’s beliefs according to new information such as theory of belief change, rationality, belief updating and revision operators, logics for belief change, etc.).
Study of argumentation systems (argumentation theory, semantics for abstract argumentation, extension-based semantics, ranking-based semantics, revision and aggregation of argumentation systems, deliberation systems.
- Epistemic reasoning, reasoning about actions
Design of approaches for reasoning about action and change in the context of a dynamic multi-agent system (modal logics, epistemic logic, dynamic logic, etc.).
- Information fusion
Study of approaches for merging potentially conflicting multi-source information such as belief fusion operators, distance-based operators, preference aggregation, source reliability assessment, judgment aggregation, collective decisions.
- Conflicting information management
Development of practical methods for restoring coherence and querying from inconsistent bases that are totally or partially ordered. Development of incoherence measures, which allow to measure the extent to which several pieces of information are in conflict.
- Ontologies and description logics
Study of formal ontology formalisms based on description logics in order to take into account priorities or uncertainty in knowledge representation or revisable reasoning (reasoning in the presence of inconsistency, possibilistic logics, non-monotonic description logics, “light” description logics, ontology completion, and so on.).
- Graphical models and uncertainty
Design of formalisms based on graphical models for the representation and reasoning from uncertain or imprecise information such as Bayesian networks, possibility theory, possibilistic networks.
- Spatio-temporal reasoning
Study of formalisms allowing to reason about temporal and spatial knowledge represented qualitatively (qualitative formalisms, symbolic representation, relational languages, and so on.).
- Conceptual spaces
Development of approaches for learning representations of conceptual spaces and reasoning from conceptual spaces. Application to automatic language processing.
Development of approaches and models for knowledge compilation (knowledge compilation maps, representation languages, expressiveness, compactness, complexity). Application to product configuration.
Members in the axis
|Al Anaissy Caren||PhD student||fac|
|Bellart Steve||PhD student||fac|
|Boukontar Abderrahmane||PhD student||fac|
|Bounia Louenas||PhD student||fac|
|Bouraoui Zied||Associate professor, accredited to supervise research||fac|
|Chafik Anasse||Associate member||fac|
|Cheikh-Alili Fahima||Associate professor||iut|
|Chetcuti-Sperandio Nathalie||Associate professor||fac|
|Coste-Marquis Sylvie||Associate professor||iut|
|de Lima Tiago||Associate professor, accredited to supervise research||fac|
|Elsaesser Quentin||PhD student||fac|
|Falque Thibault||PhD student||fac|
|Fellah Chouaib||PhD student||fac|
|Guffroy Yves||PhD student||fac|
|Ing David||PhD student||fac|
|Kemgue Alain Trésor||Engineer||fac|
|Klipfel Astrid||PhD student||fac|
|Konieczny Sébastien||DR CNRS researcher||fac|
|Kteich Hanane||PhD student||fac|
|Le Berre Daniel||Professor||fac|
|Mengel Stefan||CNRS researcher, accredited to supervise research||fac|
|Piette Cédric||Associate professor||iut|
|Pino Perez Ramon||Contractor researcher||fac|
|Tabia Karim||Associate professor, accredited to supervise research||fac|
|Vesic Srdjan||CNRS researcher, accredited to supervise research||fac|
|Wallon Romain||Associate professor||iut|
- Analysis of complex data using qualitative reasoning - Abderrahmane Boukontar
- Application de la théorie de l’argumentation au raisonnement juridique - Caren Al Anaissy
- Confidence measures by confrontation of sources - Quentin Elsaesser
- Design of new semiconducting electrodes for photoelectrochemical energy conversion by machine learning and DFT calculations - Astrid Klipfel
- Découverte de connaissances à partir de données migratoires - David Ing
- Formal models for explainable and robust AI - Louenas Bounia
- Intelligent processing of health data for the prediction and prevention of pathologies - Hanane Kteich
- Knowledge Compilation for explainable and robust AI - Steve Bellart
- Optimization of passenger flows and resource management through machine learning and constraint programming techniques - Thibault Falque
- Weighted Belief Base Merging - Chouaib Fellah