Empirical decision model learning

Learning Markov Decision Processes for Model Checking

Constructing an accurate system model for formal model verification can be both resource demanding and time-consuming. To alleviate this shortcoming, algorithms have been proposed for automatically learning system models based on observed system behaviors. In this paper we extend the algorithm on learning probabilistic automata to reactive systems, where the observed system behavior is in the f...

Factors Affecting Mobile Handset (MH) Buying Decision: An Empirical Study

Mobile technology is getting changed very fast in commensurate with other technological hyper changes. Buyers’ expectation from a new cellular phone is also changing over time. Producers, thereby, have to keep themselves abreast of changes in customer taste and preferences to attract, capture, grow, and retain both existing and potential customers. This study is set to measure factors affecting...

Learning-testing process in classroom: An empirical simulation model

Online model learning in adversarial Markov decision processes

Consider, for example, the well-known game of Roshambo (Figure 1), or rock-paper-scissors, in which two players select one of three actions simultaneously. One may know that the adversary will base its next action on some bounded sequence of the past joint actions, but may be unaware of its exact strategy. For example, one may notice that every time it selects P , the adversary selects S in the...

Sow culling decision-making support system using machine learning model

In recent, Korea hog farm industry is facing FTA and stock farms are experiencing difficulties through increase in production cost such as raw & subsidiary material cost and energy cost. Although there are many elements that influence the productivity of hog farm, plan and execution of sow culling is very important. In majority of farms, there are many cases of not considering importantly of th...