Research

My personal research interests are the evolutive simulation, the use of geographical data in a simulation models, the simulation formalisms and the development of new simulation environments using the more recent technology.

I belong to different groups that helps me and makes my research possible; my department (EIO), the Computing Laboratory of the Barcelona School of Informatics(LCFIB), LOGISIM, LIAM and GORS.

I focus my research in the goal of develops a simulation model of an “evolutive system”, since this objective is quite complex I divide it in different subproblems that can be numbered as:

1. Analyze the needed structures to define an evolutive model.
2. Define how to use the geographical data in a simulation model.
3. Analyze the different formalism used to define simulation models and its applicability to the definition of evolutive models.
4. Construct new tools that allow the practical implementation of these goals.

Different projects have been started in order to achieve these goals that can be divided in different areas: 

1. Environmental simulation.
2. Social simulation.
3. Tools and methodologies development.
4. Sport simulation.

Each one of these area are not independent of the others and represents different aspects of the same objective. In the next section I describe each one of these different areas and the sub goals that they want to achieve.

AREA: Environmental simulation.

During the last years we are involved in the implementation of different models to represent some aspects of the environment, like wildfire simulator (Fonseca et. alt. 2004a, 2004b) slap avalanches simulator (Fonseca and Rodriguez 2007) and black tide simulator among others. Each one of these models has one common denominator; they need geographical structures in order to define the behavior of the model.
The goal of these models is to develop a new paradigm that allows the direct use of geographical data in a simulation model. Up to date methodologies are based on cellular automaton structures (Beneson and Torrens 2004 ), however this approximation lacks in different aspects (Fonseca and Casanovas 2005, Fonseca 2006). We develop a new approximation to this problem, through the m:n-CAk cellular automaton that have some advantages respect the usual cellular automaton (Fonseca and Casanovas 2005, Fonseca 2006). In this area we implement the models using this new structure and we develop the elements that allow its use without any need, from the point of view of the user, of any specific knowledge of simulation. The main goal of this area is to VV&A the slap avalanche model and the black tide model in order to prove that this new methodologies are useful and easy to use.

AREA: Social simulation

Since not all the behaviors are as “simple” as the wildfire spread, we need to develop new structures to allow modeling, for instance, a mammal behavior. In this area the final goal is to allow the representation of the human behavior. The methodologies used in this area are based in MAS systems. In our approach we integrate the MAS systems with the developed methodologies of the environmental simulation, allowing a complete definition of the environment and allowing the interaction between different kinds of models.
Since in a social model the complexity of the behavior can be big, we investigate the formalism needed in order to define unambiguously this behavior, without complicate the understanding of the model for personnel that usually are not used with a mathematical formalizations. In this research we are developing some generic structures that can be used to define intelligent agents in order to simplify the model definition.
Two main projects are related to this area, the simulation of the Tribul behavior (a plant) in collaboration with the Department of Agri Food Engineering and Biotechnology (DEAB) bellowing to Polytechnic University of Catalonia (UPC), and the simulation of the behavior of small societies, like the Lenuria project, that has the main objective of simulate the medieval Girona.

AREA: Tools and methodologies

As we see in the previous area the methodologies are important since allows a complete communication between the different actors defining the project.  Different formalism exists to define a simulation model, Petri nets, DEVS, SDL among others, however only a few tools allow a complete simulation from model formalization.
In this area we are developing a new tool, named SDLPS, from the experience obtained by our group from the development of commercial simulation tools, like LeanSim or VRABox. This system allows the simulation of a model formalized using SDL formalism. Since we develop a method to transform SDL to DEVS and vice versa (Fonseca and Casanovas 2005, 2006) the final objective of this tool is to allow to run a simulation formalized first in SDL,next in DEVS and finally in Petri Nets, since a mechanism to transform a formalism from DEVS to Petri nets exists.
SDLPS allows a distributed execution of a simulation model, without the need of parallelize the model, since SDL formalism take care of this problem. First steps of the development of this tool have been achieved (Fonseca 2008).And some projects, using this tool and the methodologies exposed in the previous areas, started.

AREA: Sport simulation 
 

This area represents a specific use of the knowledge obtained from the other areas. The problem is to simulate the behavior of an athlete in a specific competition. The main goal is to optimize his/her training and give information to the trainer to select the best “player” for a specific competition.
As we see the final objective is double. From the point of view of the trainer, to obtain an expert system that helps to determine what are the best athlete for an specific competition depending on different factors (for instance in a soccer match what are the best players depending on its physical condition and the scheduler, or in a marathon race who can be the athlete that must run the race, to allow the club to achieve more points during the different athletic courses of the schedule).
From the point of view of the athlete the main objective is to improve the training and to help avoiding problems related to the overtraining or injuries, helping the trainer deciding the microcycles, mesocycles and macrocycles of a training season for a specific athlete, depending of course on his/her personal objectives.
To model this system is needed to use GIS, complex behavior defining the athletes, and other methodologies of the O.R.

Concluding remarks

All this areas are widely connected, because in order to step forward in each one of them is needed to step forward in the others. However, the separation in different areas allows defining more specific goals that are more suitable that the final objective. Also the main objective of each one of the different areas is very interesting by itself.

References:

Benenson, Itzhak; Torrens, Paul M. 2004. Geosimulation: automata-based modeling of urban phenomena. Wiley.

Fonseca , Pau; Casanovas, Josep; Montero, Jordi. 2004. GIS and simulation system integration in a virtual reality environment. Proceedings of GISRUK 2004.Pages: 403-408  Editors:: University of East Anglia (pdf) (poster) First prize poster winners in the 2004 conference.

Fonseca i Casas, Pau; Casanovas, Josep; Montero, Jordi. 2004 A cellular automata and intelligent agents use to model natural disasters with discrete simulation Environmental Modelling and Simulation 2004. EMS 2004 ref: 0-88986-443-8 pages: 96-101. Editor: Ubertini 

Fonseca, Pau. Casanovas, Josep. Simplifying Gis Data Use Inside Discrete Event Simulation Model Through m:n-ac Cellular Automaton. A: Procediings. Chiara BRIANCO, Claudia FRYDMAN, Antonio GUASCH, Miquel Angel PIERA, 2005, p. 7-15.

Fonseca, Pau. Casanovas, Josep. Using SDL diagrams in a DEVS specification. A: MSO 2005. IASTED G. Tonella, 2005, p. na-na.
 

Fonseca, Pau. Vectorial Data Use in a m:n-AC Cellular automaton. A: Proceedings of the GIS Reseach UK 14th Annual Conference. Gary Priestnall and Paul Aplin, 2006, p. 350-356.

Fonseca, Pau. Casanovas, Josep. Transforming SDL Diagrams in a DEVS Specification, Modelling, Simulation and Optimization. A: Procediings MSO 2006. H. Nyongesa , 2006, p. ---.

Fonseca, Pau. Rodríguez, Santiago. Using GIS data in a m:n-ACk cellular automaton to perform an avalanche simulation. A: Proceedings of the Geographical Information Science Research UK Conference. Adam C. Winstanley, 2007, p. 45-50.

Fonseca, Pau. SDL Distributed Simulator. Proceedings of the Winter Simulation Conference 2008. Poster session.