This work aims to simulate the spread of leprosy in Juiz de Fora using the SIR model and considering some of its pathological aspects. SIR models divide the studied population into compartments in relation to the dise...
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ISBN:
(纸本)9783319937137;9783319937120
This work aims to simulate the spread of leprosy in Juiz de Fora using the SIR model and considering some of its pathological aspects. SIR models divide the studied population into compartments in relation to the disease, in which S, I and R compartments refer to the groups of susceptible, infected and recovered individuals, respectively. The model was solved computationally by a stochastic approach using the Gillespie algorithm. Then, the results obtained by the model were validated using the public health records database of Juiz de Fora.
We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) algorithm as an intrinsically motivated goal exploration mechanism which allows a redundant robot to efficiently and act...
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ISBN:
(纸本)9781424466757
We introduce the Self-Adaptive Goal Generation Robust Intelligent Adaptive Curiosity (SAGG-RIAC) algorithm as an intrinsically motivated goal exploration mechanism which allows a redundant robot to efficiently and actively learn its inverse kinematics. The main idea is to push the robot to perform babbling in the goal/operational space, as opposed to motor babbling in the actuator space, by self-generating goals actively and adaptively in regions of the goal space which provide a maximal competence improvement for reaching those goals. Then, a lower level active motor learning algorithm, inspired by the ssa algorithm, is used to allow the robot to locally explore how to reach a given self-generated goal. We present simulated experiments in a 32 dimensional continuous sensorimotor space showing that 1) exploration in the goal space can be a lot faster than exploration in the actuator space for learning the inverse kinematics of a redundant robot;2) selecting goals based on the maximal improvement heuristics is statistically significantly more efficient than selecting goals randomly.
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