Abductive Reasoning with Minimal Sensing in a Home Environment

30/04/2022 – 01/05/2022 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 : 2022-04-30

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. Abductive reasoning in this context consists in finding logically sound hypotheses (e.g. ‘the dishwasher is on’) that explain observed sensor measurements (‘electric consumption has risen in the last two hours’) according to a model of human activity in a home.

Profil du candidat :
Prior knowledge in AI is expected, especially in computational logics, logic programming and/or Semantic Web technologies. Basic understanding of statistical inference methods and linear programming is also considered important.

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 :

Document attaché : 202203300752_phd-offer.pdf