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We have developed a novel collision avoidance model and shown how the model can be used as a basic building block to generate various behaviors for intelligent agents.The main difference between our model and other path planning models is that our model is concerned with the execution of a planned path in a dynamic environment rather than the planning of the path itself. As a result, our model reflects more closely the decision process of humans in collision avoidance. We argue that collision avoidance is in fact a highly complex social interaction between two or more agents, and that our model has the potential of creating a truly heterogenous population for realistic crowd simulations. We have also conducted some basic experiments on the model to investigate the resultant behaviors. The results show that the proposed model is effective in generating various human-like collision avoidance behaviours.11th IEEE Symposium on Distributed Simulation and Real-Time Applications 1550-6525/07 $25.00

In silico (on the computer) oncology is a complex and multiscale combination of sciences and technologies that focuses on the study and modelling of biological mechanisms related to the phenomenon of cancer at all levels of biocomplexity. In silico oncology simulation models may be used for evaluating and comparing different therapeutic schemes, while at the same time considering different values of critical parameters which present substantial inter-patient variability. As the number of the involved parameters characterizing both the complex tumour biosystem and possible treatment schemes increases, the resulting exponential increase in computational requirements makes the use of a grid environment for the execution of the simulations a particularly attractive solution. In this paper, a grid-enabled simulation environment for the execution of in silico oncology radiotherapy simulations on grid infrastructures is presented and implementation details are discussed. The environment provides a web portal as the end-user interface and contains advanced features that facilitate the execution of in silico oncology simulations in grid environments. Special consideration has been given during the development of the environment in order to simplify the maintenance and extension of the application, while additional services for Quality of Service provisioning have been applied. The simulation environment has been employed in order to perform several scenarios of glioblastoma multiforme radiotherapy simulations on the Enabling Grids for E-sciencE (EGEE) grid infrastructure. Indicative simulation results, as well as statistics regarding execution times on the grid, are presented.

The purpose of the study presented in this paper is to investigate the performance of a multicelluar Wideband Code Division Multiple Access (WCDMA) network for different multiuser detection (MUD) strategies in terms of maximum achievable capacity. Capacity is evaluated both with system level simulations for different loading, as well as with link level simulations for different MUD techniques. Due to the increased complexity of the problem, a grid-enabled problem solving environment is developed in order to reduce execution time and make feasible the Monte Carlo (MC) simulation of scenarios with up to 4 tiers of cells and increased number of users. Hence, this Grid enabled WCDMA simulator now allows for detailed consideration of the multi-cell multi-user interference with complex techniques like MUD, previously not possible. Results indicate that the parallel interference cancellation (PIC) technique with one stage detection has the best performance in a multicellular network with ideal power control.