Abstract: In modeling swarms of autonomous robots, individual robots may be identified as cognitive agents. We describe a model of population of simple cognitive agents, naïve creatures, learning to safely cross a cellular automaton based highway. These creatures have the ability to learn from each other by evaluating if creatures in the past were successful in crossing the highway for their current situation. The creatures use “observational social learning” mechanism in their decision to cross the highway or not. The model parameters heavily influence the learning outcomes examined through the collected simulation metrics. We study how these parameters, in particular the knowledge base, influence the creatures’ success rate of crossing the highway.

Abstract: This paper describes a change of variables applied to Richards’ equation for steady-state unsaturated seepage flow that makes the numerical representation of the new version of this highly nonlinear partial differential equation (PDE) much easier to solve, and the solution is significantly more accurate. The method is applied to two-dimensional unsaturated steady-state flow in a block of soil that is initially very dry until water is applied at the top. Both a quasi-linear version of relative hydraulic conductivity for which an analytic solution exists and a van Genuchten version of relative hydraulic conductivity are numerically solved using the original and new versions of the governing PDE. Finally, results of this research will be presented in this paper. It was found that for the test problem, the change-of-variables version of the governing PDE was significantly easier to solve and resulted in more accurate solutions than the original version of the PDE.

Abstract: Indian summer monsoon has distinct behaviors in its early and late phase. The influencing climatic factors are also different. In this work we aim to predict the national rainfall in these phases. The predictors used by the forecast models are discovered using a stacked autoencoder deep neural network. A fitted regression tree is used as the forecast model. A superior accuracy to state of art method is achieved. We also observe that the late monsoon can be predicted with higher accuracy than early monsoon rainfall.