4th CONFMLA

Learning and Decision Making in Multi Agent Software Systems

Organizer Submission Deadline Notification of Acceptance Submission Email Download
University of Bath October 19th, 2026 7-20 workdays [email protected] Manuscript Template

Scope

We invite contributions on a broad range of topics related to learning and decision making in multi agent software systems, including but not limited to:

  • Multi agent reinforcement learning and adaptive agents
  • Decision making under uncertainty and incomplete information
  • Coordination, cooperation, and competition among agents
  • Game theoretic models and mechanisms for multi agent systems
  • Learning in strategic environments
  • Distributed and decentralised optimisation
  • Agent based modelling and simulation
  • Large language model based agents and multi agent AI systems
  • Trust, robustness, fairness, and explainability in multi agent decision making
  • Applications of multi agent systems in real world domains

The symposium welcomes theoretical, algorithmic, and empirical contributions that advance the understanding and development of intelligent multi agent systems.

Topics

This symposium welcomes submissions with the following topics

Machine Learning

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning
  • Deep Learning
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Generative Adversarial Networks
  • Transfer Learning
  • Ensemble Learning
  • Explainable AI
  • Natural Language Processing
  • Speech Recognition
  • Image Recognition
  • Recommendation Systems
  • Anomaly Detection
  • Cluster Analysis
  • Dimensionality Reduction
  • Feature Engineering
  • Model Evaluation

Meanwhile, submissions aligned with the overall conference scope are also welcomed.

Automation

  • AI-Assisted Design
  • Automated Machine Learning
  • Clustering and Classification
  • Collaborative Filtering and Recommendation Systems
  • Computer Vision
  • Cyber-Physical Systems
  • Data Preprocessing Methods
  • Feature Selection Approaches
  • Graph and Network Data
  • Home Automation
  • Regression with Machine Learning
  • Robotic Process Automation
  • Sensor Technology
  • Warehouse Automation
  • Hyperparameter Optimization
  • Industrial Automation
  • IoT (Internet of Things)
  • Machine-to-Machine Communication
  • Meta-learning
  • Model Interpretability Techniques
  • Model Selection Strategies
  • Neural Architecture Search
  • Pipeline Generation Methods
  • Predictive Maintenance
  • Process Automation
  • Supply Chain Automation
  • Time Series Analysis

Robotics and Intelligent Systems

  • Aerial and Underwater Robotics
  • Assistive Devices and Exoskeletons
  • Autonomous Vehicles
  • Deep Learning for Robotic Vision
  • Drones
  • Human-Robot Collaboration and Learning
  • Human-Robot Interaction
  • Imitation Learning and Learning from Demonstration
  • Intelligent Transportation Systems
  • Learning-Based Control and Planning Algorithms
  • Multi-Robot Systems and Learning
  • Reinforcement Learning for Robotics
  • Robotic Manipulation and Grasping
  • Robot Navigation and Path Planning
  • Robot Perception
  • Transfer Learning and Domain Adaptation