Abductive Reasoning with Minimal Sensing in a Home Environment

31/07/2022 – 01/08/2022 all-day

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : LIMOS / Mines Saint-Étienne
Durée : 3 ans
Contact : victor.charpenay@emse.fr
Date limite de publication : 2022-07-31

Contexte :
The thesis is equally funding by ANR (Agence Nationale de la Recherche) and elm.leblanc, one of the leading home automation system vendors. One of the main technical challenges in modern home automation is to using Artificial Intelligence (AI) to minimize the energy consumption of technical systems without loss of comfort. For instance, the production of hot water can be optimized by dynamically adapting the temperature of water and the time of use of the boiler based on activities monitored in the home. The general objective of the thesis is to monitor human activities without ubiquitous sensing capabilities.

Sujet :
The domain of research of the thesis is knowledge representation and reasoning, a subfield of AI. Its objective is to evaluate abductive reasoning methods over sensor measurements performed in a home environment. The baseline assumption of the thesis is that only minimal sensing is available in the home, as is the case in most homes today: smart meters provide aggregated values (every hour/day) but no information is available per room. Abductive reasoning is expected to help optimize home automation systems without relying on some ubiquitous sensing apparatus (which raises environmental, technical and privacy-preservation questions). Several abduction mechanisms will be evaluated, including Abductive Logic Programming (for an exhaustive exploration of hypothesis space) and neural-symbolic integration methods (for a probabilistic exploration of hypothesis space).

Profil du candidat :
Candidates are expected to have prior knowledge in AI, especially in computational logics, logic programming and/or Semantic Web technologies. Basic understanding of statistical inference methods and linear programming is also considered relevant.

Candidates whose background is machine learning may apply as well. A cover letter exposing the candidate’s motivation to combine (neural) learning methods with symbolic AI is however expected.

Formation et compétences requises :
Holder of a Master’s degree in computer science or data science. Technical skills required for the thesis include: multi-paradigm programming (Java, Lisp, R, Prolog, …), data modeling (UML, OWL, E/R, BPMN, …), Linux system administration (Bash, SSH, Docker, …).

Adresse d’emploi :
Saint-Étienne (with stays in Paris and/or Lille-Douai)

Document attaché : 202206071402_phd-offer.pdf