A novel method and a framework called Memory-Based Forecasting are proposed to forecast complex and timevarying natural patterns with the goal of supporting experts' decision...
Although computers are widely used to simulate complex physical systems, crafting the underlying models that enable computer analysis remains difficult. When a model is created fo...
: This research investigated the simulation model behaviour of a traditional and combined discrete event as well as agent based simulation models when modelling human reactive and ...
Mazlina Abdul Majid, Peer-Olaf Siebers, Uwe Aickel...
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...