Although various techniques exist for modeling decision making, the analytical hierarchy process was chosen for this study.
As stated in previous section, although various techniques exist for modeling decision making problem (MCDM), the AHP was chosen for this study.
With the development of video technology, video based smart home becomes more attractive, but there are few literatures discussing a complete system including system architecture, feature extraction, event modeling, decision making and final information retrieval.
In this way they have modeled decision making under the influence of cognition, combining the above two approaches.
To model decision making processes, an often used approach is the BDI-model (Rao and Georgeff 1991), which explains the reasoning process people undertake to get to certain actions in terms of B eliefs), D esires), and I ntentions).
Over the past decades, new approaches based on artificial neural networks have been developed aiming to solve real life problems related to optimization, modeling, decision-making, classification, data mining, and nonlinear functions (behavior) approximation.
However, a theoretical framework that is able to model decision-making within complex systems, as well as research methods to empirically identify them, is needed to help the field of STEM education meet these goals.
The question of sustainability is considered using such a traditional Cost-Benefit Analysis approach, as undertaken in 1988 by a CIPM-Yangtze Joint Venture, and the Mauri Model Decision Making Framework (MMDMF).
This majority function task is similar to the visual motion task used in studies of perceptual decision making (e.g., [17]), but here we examine and model decision making on a system level by considering the algorithms potentially adopted by the brain to process discrete information.
Over time or trials, the response model has the form of a state-space model (e.g., [9]), with two components: (i) an evolution function that models perception and learning through surprise minimization and (ii) an observation function that models decision making through risk minimization.
From here, modeling decision-making is as easy as calculating utilities for things and asking which is higher.
