One way of making learning English language interesting is through its gamified instruction, especially nowadays that most teachings are done virtually. Doing so, however, requires a valid framework. To construct such a framework a set of 22 peer reviewed papers on language learning and gamified teaching, from among 94 of such papers, was content analyzed and key concepts, and frequently used e...

J. Andrew Bagnell March 15, 2015 J. A. Bagnell is an associate professor at the Robotics Institute and Machine Learning Department at Carnegie Mellon University. Imitation learning is the study of algorithms that attempt to improve performance by mimicking a teacher’s decisions and behaviors. Such techniques promise to enable effective “programming by demonstration” to automate tasks, such as d...

In this paper, we present a new type of multi-class learning algorithm called a linear-max algorithm. Linear-max algorithms learn with a special type of attribute called a sub-expert. A sub-expert is a vector attribute that has a value for each output class. The goal of the multi-class algorithm is to learn a linear function combining the sub-experts and to use this linear function to make corr...

The solutions or states of optimization problems or simulations are evaluated by using objective functions. The weights for these objective functions usually have to be estimated from experts’ evaluations, which are likely to be qualitative and somewhat subjective. Although such estimation tasks are normally regarded as quite suitable for machine learning, we propose a mathematical programming-...