Abstract: In general we know that high-level species such as mammals must learn from their environment to survive. We believe that most species evolved over time by ancestors learning the best traits, which allowed them to propagate more than their less effective counterparts. In many instances, learning occurs in a competitive environment, where a species is evolving alongside its food source and/or its predator. We are unaware of work that studies co-evolution of predator and prey through simulation such that each entity learns to survive within its world, and passes that information on to its progeny, without running multiple training runs. We propose an agent-based model of predators and prey with co-evolution through feature-based Q-learning, to allow predators and prey to learn during their lifetime. We show that this learning results in a more successful species for both predator and prey. We suggest that feature-based Q-learning is more effective for this problem than traditional variations on reinforcement learning, and would improve current population dynamics simulations.

Abstract: This paper deals with intelligent autonomous navigation of a vehicle in cluttered environment. We present a control architecture for safe and smooth navigation of a Unmanned Ground Vehicles (UGV). This control architecture is designed to allow the use of a single control law for different vehicle contexts (attraction to the target, obstacle avoidance, etc.). The reactive obstacle avoidance strategy is based on the limit-cycle approach. To manage the interaction between the controllers according to the context, the multi-agents system is proposed. Multi-agents systems are an efficient approach for problem solving and decision making. They can be applied to a wide range of applications thanks to their intrinsic properties such as self-organization/emergent phenomena. Merging approach between control laws is based on their properties to adapt the control to the environment. Different simulations on cluttered environment show the performance and the efficiency of our proposal, to obtain fully reactive and safe control strategy, for the navigation of a UGV.

Abstract: Due to the complexity and crucial role of an Emergency Department(ED) in the healthcare system. The ability to more accurately represent, simulate and predict performance of ED will be invaluable for decision makers to solve management problems. One way to realize this requirement is by modeling and simulating the emergency department, the objective of this research is to design a simulator, in order to better understand the bottleneck of ED performance and provide ability to predict such performance on defined condition. Agent-based modeling approach was used to model the healthcare staff, patient and physical resources in ED. This agent-based simulator provides the advantage of knowing the behavior of an ED system from the micro-level interactions among its components. The model was built in collaboration with healthcare staff in a typical ED and has been implemented and verified in a Netlogo modeling environment. Case studies are provided to present some capabilities of the simulator in quantitive analysis ED behavior and supporting decision making. Because of the complexity of the system, high performance computing technology was used to increase the number of studied scenarios and reduce execution time.

Abstract: In healthcare environments we can find several microorganisms causing nosocomial infection, and of which one of the most common and most dangerous is Methicillin-resistant Staphylococcus Aureus. Its presence can lead to serious complications to the patient. Our work uses Agent Based Modeling and Simulation techniques to build the model and the simulation of Methicillin-resistant Staphylococcus Aureus contact transmission in emergency departments. The simulator allows us to build virtual scenarios with the aim of understanding the phenomenon of MRSA transmission and the potential impact of the implementation of different measures in propagation rates.

Abstract: Among the innovations aimed at tackling the transportation issues in the urban area, one of the most promising solutions is the possibility of making virtual trains of vehicles so as to provide a new kind of transportation system. Even if this kind of solutions is now widespread in the literature, some difficulties still need to be resolved. For instance, one must find solutions to make the crossing of the train possible while maintaining train composition (trains must not be split) and safety conditions. This paper proposes a multi-level decision process aimed at dealing with this issue. This proposal is based on train parameters dynamic adaptation which lead to trains crossing without stopping any of them. Results, obtained in simulations, make the comparison with a classical crossing strategy.