Cellular Modeling with Cell-DEVS: a Discrete-Event Cellular Automata formalismSpeaker: Gabriel Wainer – Ottawa, ON, Canada
Topic(s): Applied Computing
In recent years, grid-shaped cellular models have gained popularity to understand physical systems. Complex cell spaces can require large amounts of compute time, mainly due to its synchronous nature; the use of a discrete time base also constrains the precision of the model. The Cell-DEVS formalism was defined in order to deal with these issues. We give a brief introduction to the main characteristics of Cell-DEVS, and show how to use the method to model complex cell spaces.
We will introduce the main characteristics of the Cell-DEVS formalism, and will show how to model complex cell spaces using this methodology. We will present different examples of application, and discuss open research issues in this area. Cell–DEVS allows describing physical and natural systems using an n-dimensional cell-based formalism. Input/output port definitions allow the definition of multiple inter-connections between Cell-DEVS and DEVS models. Complex timing behavior for the cells in the space can be defined using very simple constructions. The CD++ tool im-plements the Cell-DEVS formalism and entitles the definition of complex cell-shaped models. We showed how to develop several Cell-DEVS models using the CD++ toolkit, which provides a general framework to define and simulate complex generic models. Cell-DEVS simplifies the construction of complex simulations, allowing a simple and intuitive model specification.
We will then show some examples of the current use of DEVS, including applications in different fields. We will introduce an integrated environment that deals with these issues, orchestrating a cellular-based simulator (CD++), a GIS and data visualization, to simulate behavior and analyze results supporting the decision making for varied environmental scenarios. The limitations above are solved by adding raw simulation results into the georeferenced maps, associating many sources of information (even if they do not come from the simulation model), providing a more powerful analysis experience. The simulation model is fed by the GIS with updated data, while the model design process enables integrating additional information layers. The methodology uses a cellular modeling approach in which each cell is defined as a discrete event agent, and defines a procedure to couple cells evolving the state of the influenced neighbors. We will also discuss models of spiking neurons, data mining and market evolution, between others.
About this LectureNumber of Slides: 40
Duration: 40 - 120 minutes
Languages Available: English, Spanish
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