Self-improving system integration: Mastering continuouschange
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International audience
Multi-Agent Reinforcement Learning as a Computational Tool for Language Evolution Research: Historical Context and Futur...
Computational models of emergent communication in agent populations are currently gaining interest in the machine learning community due to recent advances in Multi-Agent Reinforcement Learning (MARL). Current contributions are however still relatively disconnected from the earlier theoretical and computational literature aiming at understanding ho...
Self-healing Networks via Self-organising Mobile Agents
International audience
Exogenous coordination in multi-scale systems: How information flows and timing affect system properties
International audience
Agent-based ordinal classification for group decision making
In this article we are interested in group decision aiding for an ordinal classification problem. Our approach is based on a multiagent system where each decision maker is represented by a user agent and the process is guided by a mediator agent. Each user agent has a personalized preferencebased behavior defined by a utility function. The aim of t...
Novel Off-board Solution for Predicting Drivers’ Intentions using Probabilistic Classifiers
International audience
State Variance Estimation in Large-Scale Network Systems
The state variance of a network system is a nonlinear functional computed as the squared deviation of the network's state vector. Such a quantity is useful to monitor how much the states of network nodes are spread around their average mean. Estimating state variance is crucial when the full state estimation of a network system is not possible due ...
Explainability in Deep Reinforcement Learning
A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data. However, assessing how XAI techniques can help understand models beyond classification tasks, e.g. for reinforcement learning (RL), has ...
Average Consensus: A Little Learning Goes A Long Way
When networked systems of autonomous agents carry out complex tasks, the control and coordination sought after generally depend on a few fundamental control primitives. Chief among these primitives is consensus, where agents are to converge to a common estimate within the range of initial values, which becomes average consensus when the joint limit...
SApHESIA: An agent-based model and a criticality-based heuristic for cooperatively coupling SoSs
International audience
