If you ask kids today why phrases like "hang up" the phone or "roll down" the window exist, chances are they'll have no idea. Fast-forward to the near future and "search the web" may also cause a few head scratches. "We're evolving, but remain electronic'hunters and gatherers,'" explained Ralph Lucci, cofounder and user experience director at Behavior Design. But that's about to change thanks to today's quickly emerging artificial intelligence (AI) technology for practically every industry, including education. "The day will soon come when we'll sardonically ask ourselves: 'Remember when we had to visit a website and look around for what we needed?' Now the data comes to us."

If you ask kids today why phrases like "hang up" the phone or "roll down" the window exist, chances are they'll have no idea. Fast-forward to the near future and "search the web" may also cause a few head scratches. "We're evolving, but remain electronic'hunters and gatherers,'" explained Ralph Lucci, cofounder and user experience director at Behavior Design. But that's about to change thanks to today's quickly emerging artificial intelligence (AI) technology for practically every industry, including education. "The day will soon come when we'll sardonically ask ourselves: 'Remember when we had to visit a website and look around for what we needed?' Now the data comes to us."

We present a unified logical framework for representing and reasoning about both probability quantitative and qualitative preferences in probability answer set programming [Saad and Pontelli, 2006; Saad, 2006; Saad, 2007a], called probability answer set optimization programs. The proposed framework is vital to allow defining probability quantitative preferences over the possible outcomes of qualitative preferences. We show the application of probability answer set optimization programs to a variant of the well-known nurse restoring problem [Bard and Purnomo, 2005], called the nurse restoring with probability preferences problem. To the best of our knowledge, this development is the first to consider a logical framework for reasoning about probability quantitative preferences, in general, and reasoning about both probability quantitative and qualitative preferences in particular.