ESM'2026 - GAME-ON 2026 Keynotes and Invited Speakers

Abstract

Deep-learning algorithms are based on calculation of 2D tensors (vector data), 3D tensors (time series), 4D (images) and 5D (videos) encoded in neural networks trained on data sets to perform a classification in order to recognize situations or images. Deep learning is only relevant if the data set is sufficiently large and representative of the situations to be recognized. Generative AI (GAI) relies on a top-down statistical classification learned with a limited selection of attributes that does not take into account the structure, dynamics of objects and relationships between them (ontology). GAI is therefore a black box that does not implement any reasoning or knowledge capable of justifying the results obtained and informing the user about the limitations associated with the learning data sets.

We propose a Fuzzy Tensorial Object Model FUTOM which is a natural tensor extension of our previous multidimensional FVSOM model (Fuzzy Vector Space Object Model) that relies on the robustness of the composition (+) and synthesis (x) operators of our object model and offers a bottom-up inheritance from the attribute layers, simple objects layers to complex objects layers along the object composition hierarchy. The semantics of the relationships between components is expressed using the junction objects implanted in this composition hierarchy. The fuzzy characteristic function of FUTOM provides a homogeneous encoding and learning of attributes, simple objects (with attributes only) and complex objects (composed of objects). Indeed FUTOM is truly a new Object Composition and Synthesis Generative Artificial Intelligence (OCSyGenAI) that realizes a bottom-up learning of knowledge objects emerging from the structure, relationships and values of qualifying attributes representative of the target application domain which are sorted, indexed and stored in the object case base. Finally, reasoning modes like induction, abduction, subsumption, analogy exploit these knowledge objects in order to implement and to explain the successive stages of the decision-making process in a comprehensible way.

Short Biography

Joël Colloc is Emeritus Professor of Le Havre Normandy University. He earned his M.D. at the medical faculty of Lyon and a specialty degree of forensic medicine with a degree of clinical toxicology. He received a MSc. degree of IT from the Business School of Lyon (IAE) and a MSc. degree of computer sciences from the engineering school INSA of Lyon. He served as forensic physician at the Edouard Herriot Hospital in the neurological emergency department to cure drug addicted people, medical ethics and developed drug and addiction database. He went on to earn his Ph.D. in computer sciences at the INSA of Lyon.

As Hospital assistant at the laboratory of medical computer science and he taught IT at the medical faculty. He was elected as associate professor in computer sciences at IAE of Lyon and he earned his accreditation to supervise researches in sciences at the Lyon 1 University. He is a Le Havre Normandy University professor in computer sciences since 2003.

His main research topics concern e-health and particularly: fuzzy vectorial spaces (FVS), multi-agent clinical decision support systems (MADSS) and knowledge bases, Case Based Reasoning, ontologies, nervous system modeling and cognitive sciences and AI applications in medicine and human sciences.

His human sciences researches try to conciliate the ethics of using Big Data in epidemiological studies, autonomous systems and robots and keeping ethics use of AI in order to improve clinical decision in medicine while preserving the patient-caregiver relationship, the privacy and the freewill choice of the patients.