Ethics of autonomous information systems towards an artificial thinking

Many projects relies on cognitives sciences, neurosciences, computer sciences and robotics. They concerned today the building of autonomous artificial beings able to think. This paper shows a model to compare the human thinking with an hypothetic numerical way of thinking based on four hierarchies : the information system classification, the cognitive pyramid, the linguistic pyramid and the digital information hierarchy. After a state of art on the nature of human thinking, feasibility of autonomous multi-agent systems provided with artificial consciousness which are able to think is discussed. The ethical aspects and consequences for humanity of such systems is evaluated. These systems lead the scientific community to react.Comment: in Frenc

Constructing Ontology-Based Cancer Treatment Decision Support System with Case-Based Reasoning

Decision support is a probabilistic and quantitative method designed for modeling problems in situations with ambiguity. Computer technology can be employed to provide clinical decision support and treatment recommendations. The problem of natural language applications is that they lack formality and the interpretation is not consistent. Conversely, ontologies can capture the intended meaning and specify modeling primitives. Disease Ontology (DO) that pertains to cancer's clinical stages and their corresponding information components is utilized to improve the reasoning ability of a decision support system (DSS). The proposed DSS uses Case-Based Reasoning (CBR) to consider disease manifestations and provides physicians with treatment solutions from similar previous cases for reference. The proposed DSS supports natural language processing (NLP) queries. The DSS obtained 84.63% accuracy in disease classification with the help of the ontology

Un système multi-agents neuronal : vers des systèmes d\u27information épigénétiques

The software evolution is constrained by the symbolic nature of computers. Nervertheless, the brain is not a symbolic machine, it is able to adapt itself to the environment modifications. The brain is continually building up, it modifies its own structure to adapt itself to the perceived world and to the life events. We examine the possibilities of a research way which is usually considered as a dead end: how to provide computers with the ability to modify themselves. This article proposes a neuronal multi-agent system which relies on recent works in neurosciences and genetic. It borrows from the nervous system some adaptation facilities which are not currently included in usual neural networks. Neural reactive agents are provided with operators which are used not only to tune-up some parameters but to change radically the communication mode according to the frequency and the intensity of their stimulation inputs

UNE APPROCHE ORIENTEE OBJET DE LA PROTECTION DANS LES SYSTEMES D'INFORMATION

International audienceWe provide a protection system making use of encapsulation, messages communication, interface functions coming from an object oriented model described in previous works. Each user represents himself to the system by the mean of his "USER" object type. The recognition procedure is suitable to every one's needs. Any user's objects and types are labeled with a personal signature, exclusively provided and known by the system. Administrator's rights are restricted to backup procedures. The system verify each messages access, it is robust because partitioned, flexible, suitable and psychologically acceptable.Le système de protection présenté exploite l'encapsulation, la communication par messages, les fonctions d'interface d'un modèle orienté objet décrit dans de précédents travaux. Chaque personne est représentée au système par son objet "UTILISATEUR". La procédure de reconnaissance est adaptable au gré de chacun. Les objets et types d'un utilisateur portent sa signature personnelle, fournie et connue seulement par le système. Les droits de l'administrateur sont réduits aux sauvegardes. Le contrôle des accès concerne chaque message, le système est solide car cloisonné, souple, adaptable et psychologiquement acceptable. Mots-clés : Système de protection, modèle orienté objet, objet utilisateur, signature du propriétaire, encapsulation, message

A multi-agent model approach to fill up the gap between emotion, psychologic and brain computing

International audienceDeep learning and artificial neural networks are used to simulate synapses and recognition features of brain perception. However, they do not take into account, the dynamic, the plasticity and the diversity of the interneuron local communication: electrical, chemical and paracrine synapses. Moreover, these models do not implement the glia that conveys long distance hormonal messages that interact with neurons and make them available everywhere in the whole body at the same time. These internal hormonal messages strongly influence the state and the behavior of all the brain layers and nuclei (fear, emotions, sleeping and awakening). We have shown in our previous article that these hormonal messages are essential to consciousness and thinking. We know that the nervous system is always evolving and that this evolution is necessary to achieve longterm memory, cognitive, sensitive, motor and language tasks. We have also described the features of the object oriented subsymbolic perception pyramid and those of the object oriented linguistic pyramid. In this work we provide a junctions object ontology to implement the different type of synapses and hormonal messages and a multi-agent system that integrates and control them. The supervisor agent represents anatomical and connection constraints of the brain layer and nuclei and control the diffusion of hormones and neuromediators. The temporal fuzzy vector space (TFVS) is used to tune the composition of neuron objects in layers and nuclei objects and to implement the emergence of their states and features according to a holistic systemic approach. We present the necessary tools and TFVS object classes to implement the MAS subsymbolic perceptive and psychological layers. To illustrate the approach we propose a simulation of object perception and recognition, emotion tagging, indexation to store the cognitive experience corresponding to a set of threatening objects in the environment

Fvsoomm a Fuzzy Vectorial Space Model and Method of Personality, Cognitive Dissonance and Emotion in Decision Making

The purpose of this extension of the ESM’2019 conference paper is to propose some means to implement an artificial thinking model that simulates human psychological behavior. The first necessary model is the time fuzzy vector space model (TFVS). Traditional fuzzy logic uses fuzzification/defuzzification, fuzzy rules and implication to assess and combine several significant attributes to make deductions. The originality of TFVS is not to be another fuzzy logic model but rather a fuzzy object-oriented model which implements a dynamic object structural, behavior analogy and which encapsulates time fuzzy vectors in the object components and their attributes. The second model is a fuzzy vector space object oriented model and method (FVSOOMM) that describes how-to realize step by step the appropriate TFVS from the ontology class diagram designed with the Unified Modeling Language (UML). The third contribution concerns the cognitive model (Emotion, Personality, Interactions, Knowledge (Connaissance) and Experience) EPICE the layers of which are necessary to design the features of the artificial thinking model (ATM). The findings are that the TFVS model provides the appropriate time modelling tools to design and implement the layers of the EPICE model and thus the cognitive pyramids of the ATM. In practice, the emotion of cognitive dissonance during buying decisions is proposed and a game addiction application depicts the gamer decision process implementation with TFVS and finite state automata. Future works propose a platform to automate the implementation of TFVS according to the steps of the FVSOOMM method. An application is a case-based reasoning temporal approach based on TFVS and on dynamic distances computing between time resultant vectors in order to assess and compare similar objects’ evolution. The originality of this work is to provide models, tools and a method to design and implement some features of an artificial thinking model