Reconnaissance des buts d'un agent à partir d'une observation partielle de ses actions et des connaissances stratégiques de son espace de décision

La capacité de reconnaître les intentions des autres est une composante essentielle non seulement de l'intelligence humaine mais aussi de l'intelligence artificielle dans plusieurs domaines d'application. Pour les algorithmes d'intelligence artificielle, reconnaître l'intention d'un agent à partir d'une observation partielle de ses actions demeure un défi de taille. Par exemple dans les jeux de stratégie en temps réel, on aimerait reconnaître les intentions de son adversaire afin de mieux contrer ses actions futures. En domotique, on voudrait une maison capable de comprendre et d'anticiper les intentions de ses habitants pour maximiser leur confort et les assister dans leurs activités quotidiennes. Dans le domaine de la sécurité informatique, un outil de détection des intrus doit pouvoir observer les actions des usagers du réseau et déceler ceux qui ont des intentions malveillantes. Ce mémoire de maîtrise propose d'aborder ce problème sous observabilité partielle par adaptation des méthodes utilisées dans l'analyse grammaticale probabiliste. L'approche probabiliste considérée utilise une grammaire hors contexte de multi-ensemble partiellement ordonnée et considère la poursuite de plusieurs buts simultanément, ordonnés ou non. Cela revient donc à faire de l'analyse grammaticale probabiliste avec plusieurs symboles de départ

Hierarchical reinforcement learning for trading agents

Autonomous software agents, the use of which has increased due to the recent growth in computer power, have considerably improved electronic commerce processes by facilitating automated trading actions between the market participants (sellers, brokers and buyers). The rapidly changing market environments pose challenges to the performance of such agents, which are generally developed for specific market settings. To this end, this thesis is concerned with designing agents that can gradually adapt to variable, dynamic and uncertain markets and that are able to reuse the acquired trading skills in new markets. This thesis proposes the use of reinforcement learning techniques to develop adaptive trading agents and puts forward a novel software architecture based on the semi-Markov decision process and on an innovative knowledge transfer framework. To evaluate my approach, the developed trading agents are tested in internationally well-known market simulations and their behaviours when buying or/and selling in the retail and wholesale markets are analysed. The proposed approach has been shown to improve the adaptation of the trading agent in a specific market as well as to enable the portability of the its knowledge in new markets

Infection a COVID-19 et complications neurologiques

No Abstract

Crossing the frontier to survive: mainstream churches and the 'Pentecost' in Buea-Cameroon, 1960-2020

This study deals with the borrowing of Pentecostal practices by mainstream churches in the town of Buea in Cameroon. Contrary to the impression given by the conceptualizers of 'Pentecost' who consider it as a de-territorialized place or a space characterized by the only presence of Pentecostals and their culture, this reflection which relies on diffusion theory, postulates that ‘Pentecost’ is not solely a matter of Pentecostals. It is a heterogeneous sphere shared by mainstream and Pentecostal churches. This sharing has led to the borrowing of Pentecostal religious forms by the former notably through proximity and media used to diffuse Pentecostal culture. By focusing on Buea which religious setting is a mosaic of Christian Churches with more than one hundred Pentecostal groups and about only six mainstream churches, the paper states that the latter are progressively becoming part of 'Pentecost' by adopting Pentecostal style. Pentecostal forms are usually expressed in mainstream churches activities principally through the way of preaching and praying. In some cases Pentecostal songs are even performed by church members. In the Catholic Church precisely, these forms are highly noticed in charismatic renewal groups where healings, glossolalia and crusades which are key features of 'Pentecost' are also practiced. The work relies on qualitative data collected in Buea through interviews, participant observation and media

Atraumatic oral spray immunization with replication-deficient viral vector vaccines

The development of needle-free vaccines is one of the recently defined grand challenges in global health (H. Varmus, R. Klausner, R. Klausner, R. Zerhouni, T. Acharya, A. S. Daar, and P. A. Singer, Science 302:398-399, 2003). To explore whether a natural pathway to the inductive site of the mucosa-associated lymphatic tissue could be exploited for atraumatic immunization purposes, replication-deficient viral vector vaccines were sprayed directly onto the tonsils of rhesus macaques. Tonsillar immunization with viral vector vaccines encoding simian immunodeficiency virus (SIV) antigens induced cellular and humoral immune responses. Viral RNA levels after a stringent SIV challenge were reduced, providing a level of protection similar to that observed after systemic immunization with the same vaccines. Thus, atraumatic oral spray immunization with replication-deficient vectors can overcome the epithelial barrier, deliver the vaccine antigen to the mucosa-associated lymphatic tissue, and avoid induction of tolerance, providing a novel approach to circumvent acceptability problems of syringe and needle vaccines for children and in developing countries

Immunogenicity and efficacy of immunodeficiency virus-like particles pseudotyped with the G protein of vesicular stomatitis virus

Vaccination with exogenous antigens such as recombinant viral proteins, immunodeficiency virus-derived whole inactivated virus particles, or virus-like particles (VLP) has generally failed to provide sufficient protection in animal models for AIDS. Pseudotyping VLPs with the vesicular stomatitis virus G protein (VSV-G), which is known to mediate entry into dendritic cells, might allow more efficient stimulation of immune responses. Therefore, we pseudotyped noninfectious immunodeficiency virus-like particles with VSV-G and carried out a preliminary screen of their immunogenicity and vaccination efficacy. Incorporation of VSV-G into HIV-1 VLPs led to hundred-fold higher antibody titers to HIV-1 Gag and enhancement of T cell responses in mice. Repeated vaccination of rhesus monkeys for 65 weeks with VSV-G pseudotyped simian immunodeficiency virus (SIV)-like particles (VLP[G]) provided initial evidence for efficient suppression of viral load after mucosal challenge with the SIVmac239 virus. Challenge of monkeys after a 28 week vaccination regimen with VLP[G] led to a reduction in peak viremia, but persistent suppression of viral load was not achieved. Due to limitations in the number of animals available for this study, improved efficacy of VSV-G pseudotyped VLPs in nonhuman primates could not be demonstrated. However, mouse experiments revealed that pseudotyping of VLPs with fusion-competent VSV-G clearly improves their immunogenicity. Additional strategies, particularly adjuvants, should be considered to provide greater protection against a challenge with pathogenic immunodeficiency virus

An efficient knowledge transfer solution to a novel SMDP formalization of a broker's decision problem

This paper introduces a new technique for optimizing the trading strategy of brokers that autonomously trade in re- tail and wholesale markets. Simultaneous optimization of re- tail and wholesale strategies has been considered by existing studies as intractable. Therefore, each of these strategies is optimized separately and their interdependence is generally ignored, with resulting broker agents not aiming for a glob- ally optimal retail and wholesale strategy. In this paper, we propose a novel formalization, based on a semi-Markov deci- sion process (SMDP), which globally and simultaneously op- timizes retail and wholesale strategies. The SMDP is solved using hierarchical reinforcement learning (HRL) in multi- agent environments. To address the curse of dimensionality, which arises when applying SMDP and HRL to complex de- cision problems, we propose an ecient knowledge transfer approach. This enables the reuse of learned trading skills in order to speed up the learning in new markets, at the same time as making the broker transportable across market envi- ronments. The proposed SMDP-broker has been thoroughly evaluated in two well-established multi-agent simulation en- vironments within the Trading Agent Competition (TAC) community. Analysis of controlled experiments shows that this broker can outperform the top TAC-brokers. More- over, our broker is able to perform well in a wide range of environments by re-using knowledge acquired in previously experienced settings