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Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...

Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinem ...

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. We introduce ...

This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We stud ...

In this paper, we propose a novel approach that employs kinetic equations to describe the collective dynamics emerging from graph-mediated pairwise interactions in multi-agent systems. We formally show that for large graphs and specific classes of interact ...

Modular robotics link the reliability of a centralised system with the adaptivity of a decentralised system. It is difficult for a robot with a fixed shape to be able to perform many different types of tasks. As the task space grows, the number of functi ...

The aircraft assembly system is highly complex involving different stakeholders from multiple domains. The design of such a system requires comprehensive consideration of various industrial scenarios aiming to optimize key performance indicators. Tradition ...

A multi-agent system consists of a collection of decision-making or learning agents subjected to streaming observations from some real-world phenomenon. The goal of the system is to solve some global learning or optimization problem in a distributed or dec ...

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments ...

In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, b ...

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...

We generalize the bulk-synchronous parallel (BSP) processing model to make it better support agent-based simulations. Such simulations frequently exhibit hierarchical structure in their communication patterns which can be exploited to improve performance. ...

This paper considers the problem of resilient distributed optimization and stochastic learning in a server-based architecture. The system comprises a server and multiple agents, where each agent has its own local cost function. The agents collaborate with ...

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

This work addresses the problem of sharing partial information within social learning strategies. In social learning, agents solve a distributed multiple hypothesis testing problem by performing two operations at each instant: first, agents incorporate inf ...

Order, regularities, and patterns are ubiquitous around us. A flock of birds maneuvering in the sky, the self-organization of social insects, a global pandemic or a traffic jam are examples of complex systems where the macroscopic patterns arise from the m ...

The type VI secretion system (T6SS) is a broadly distributed interbacterial weapon that can be used to eliminate competing bacterial populations. Although unarmed target populations are typically used to study T6SS function in vitro, bacteria most likely e ...

Interactions are ubiquitous in our world, spanning from social interactions between human individuals to physical interactions between robots and objects to mechanistic interactions among different components of an intelligent system. Despite their prevale ...

The adaptive social learning paradigm helps model how networked agents are able to form opinions on a state of nature and track its drifts in a changing environment. In this framework, the agents repeatedly update their beliefs based on private observation ...

Quality embedded intelligent remanufacturing

Chattez avec Graph Search

Stochastic pairwise preference convergence in Bayesian agents

Towards practical reinforcement learning for tokamak magnetic control

Augmenting large language models with chemistry tools

Social Opinion Formation and Decision Making Under Communication Trends

Network-based kinetic models: Emergence of a statistical description of the graph topology

Memento Mori: Reliable robustness in self-reconfigurable modular robots

An aircraft assembly process formalism and verification method based on semantic modeling and MBSE

Multi-agent Learning with Privacy Guarantees

Networked Signal and Information Processing: Learning by multiagent systems

Multi-agent actor-critic with time dynamical opponent model

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Impact of Redundancy on Resilience in Distributed Optimization and Learning

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Partial Information Sharing Over Social Learning Networks

Human-Robot Swarm Interaction: An Explorative Path to Foster Complex Systems Understanding

A subcellular biochemical model for T6SS dynamics reveals winning competitive strategies

Learning Robust and Adaptive Representations: from Interactions, for Interactions

Discovering Influencers in Opinion Formation Over Social Graphs