M. Lozano

发表

J. Orduña, F. Grimaldo, M. Lozano, 2011 .

Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

Eduardo J. Godoy, R. Macleod, M. Lozano, 2018, Front. Physiol..

A Partitioning Method based on Convex Hulls for Crowd Simulations

J. Orduña, F. Grimaldo, M. Lozano, 2008 .

Inverse estimation of terminal connections in the cardiac conduction system

Miguel Lozano, Ignacio García-Fernández, Fernando Barber, 2017 .

Design of an ICT Tool for Decision Making in Social and Health Policies

J. Duato, E. Fatas, J. Orduña, 2015 .

Modeling, Evaluation, and Scale on Artificial Pedestrians

Fernando Fernández, Ignacio García-Fernández, Miguel Lozano, 2017, ACM Comput. Surv..

Predicting the Origin of Outflow Tract Ventricular Arrhythmias Using Machine Learning Techniques Trained With Patient-Specific Electrophysiological Simulations

O. Camara, A. Alcaine, L. Mont, 2019, 2019 Computing in Cardiology (CinC).

Analysis of Jason’s performance for crowd simulations

J. Orduña, F. Grimaldo, M. Lozano, 2010 .

Strategies for simulating pedestrian navigation with multiple reinforcement learning agents

Fernando Fernández, Miguel Lozano, Francisco Martinez-Gil, 2014, Autonomous Agents and Multi-Agent Systems.

Calibrating a Motion Model Based on Reinforcement Learning for Pedestrian Simulation

F. Fernández, M. Lozano, Francisco Martínez-Gil, 2012, MIG.

Multi-agent Reinforcement Learning for Simulating Pedestrian Navigation

Fernando Fernández, Miguel Lozano, Francisco Martinez-Gil, 2011, ALA.

Emergent Collective Behaviors in a Multi-agent Reinforcement Learning Pedestrian Simulation: A Case Study

Fernando Fernández, Miguel Lozano, Francisco Martinez-Gil, 2014, MABS.

Simulating socially intelligent agents in semantic virtual environments

Miguel Lozano, Guillermo Vigueras, Francisco Grimaldo, 2008, The Knowledge Engineering Review.

Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

Ana Ferrer-Albero, Javier Saiz, Felipe Atienza, 2017, PloS one.

Emergent collective behaviors in a multi-agent reinforcement learning based pedestrian simulation

Miguel Lozano, Francisco Martinez-Gil, Fernando Fernández, 2012 .

A scalable multiagent system architecture for interactive applications

Yvon Jégou, Guillermo Vigueras, Juan M. Orduña, 2013, Sci. Comput. Program..

Workload balancing in distributed crowd simulations: the partitioning method

Miguel Lozano, Guillermo Vigueras, Juan M. Orduña, 2011, The Journal of Supercomputing.

Adding Synthetic Detail to Natural Terrain Using a Wavelet Approach

Miguel Lozano, Marcos Fernández, Mariano Pérez, 2002, International Conference on Computational Science.

MARL-Ped: A multi-agent reinforcement learning based framework to simulate pedestrian groups

Fernando Fernández, Miguel Lozano, Francisco Martinez-Gil, 2014, Simul. Model. Pract. Theory.

Towards a model for urban mobility social simulation

F. Grimaldo, M. Lozano, Fernando Barber, 2012, Progress in Artificial Intelligence.

J-MADeM, a market-based model for complex decision problems

Miguel Lozano, Francisco Grimaldo, Fernando Barber, 2011, Log. J. IGPL.

J-MADeM, an open-source library for social decision-making

Miguel Lozano, Francisco Grimaldo, Fernando Barber, 2009, CCIA.

Coordination and Sociability for Intelligent Virtual Agents

Miguel Lozano, Francisco Grimaldo, Fernando Barber, 2007, COIN.

A J-MADeM Agent-Based Social Simulation to Model Urban Mobility

Miguel Lozano, Alejandro Guerra-Hernández, Francisco Grimaldo, 2011, PAAMS.

Combining Biophysical Modeling and Machine Learning to Predict Location of Atrial Ectopic Triggers

Ignacio García-Fernández, Rafael Sebastián, Miguel Lozano, 2018, 2018 Computing in Cardiology Conference (CinC).

Characterization of the Jason multiagent platform on multicore processors

Miguel Lozano, Juan M. Orduña, Francisco Grimaldo, 2014, Sci. Program..

Emergent behaviors and scalability for multi-agent reinforcement learning-based pedestrian models

Fernando Fernández, Miguel Lozano, Francisco Martinez-Gil, 2017, Simul. Model. Pract. Theory.

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

Ignacio García-Fernández, Francisco Martinez-Gil, Miguel Lozano, 2020 .

Towards a Simulator of Integrated Long-Term Care Systems for Elderly People

Miguel Lozano, Juan M. Orduña, Francisco Grimaldo, 2014, Int. J. Artif. Intell. Tools.

A Read-Copy Update based parallel server for distributed crowd simulations

J. Orduña, M. Lozano, G. Vigueras, 2013, The Journal of Supercomputing.

A new system architecture for crowd simulation

Miguel Lozano, Guillermo Vigueras, Juan M. Orduña, 2009, J. Netw. Comput. Appl..

On the Design of an Efficient Architecture for Supporting Large Crowds of Autonomous Agents

Miguel Lozano, Juan M. Orduña, Pedro Morillo, 2007, 21st International Conference on Advanced Information Networking and Applications (AINA '07).

A distributed visualization system for crowd simulations

Miguel Lozano, Guillermo Vigueras, Juan M. Orduña, 2011, Integr. Comput. Aided Eng..

Planning formalisms and authoring in interactive storytelling

Fred Charles, Miguel Lozano, Marc Cavazza, 2003 .

A Distributed Framework for Scalable Large-Scale Crowd Simulation

Carolina Cruz-Neira, Pedro Morillo, Miguel Lozano, 2007, HCI.

Modeling, Evaluation, and Scale on Artificial Pedestrians

F. Fernández, M. Lozano, I. García-Fernández, 2017, ACM Comput. Surv..