Question

A robot is dedicated to serving coffee for office employees. But it spills 20% of the time while pouring the coffee and on the way to the employee’s desk, making a mess. There are some decisions the robot can take for a better outcome. For example, improving its precision, or it can do nothing extra for a medium outcome. Complete the following:

1. Draw a Decision Network based on chance nodes, decision nodes, and a utility node.
   
2. Explain your Decision Network. For your response, you must design an approach for decision planning for the robot.  
   
3. Describe what optimization solution you would employ and why?

Answer 1

After several months, he felt a burning in his back. A supervisor sometimes told him to bend his knees more when lifting. When Jake did this his rate dropped, and another supervisor would tell him to speed up. “You’ve got to be kidding me. Go faster?” he recalled saying. “If I go faster, I’m going to have a heart attack and fall on the floor.” Finally, his back gave out completely. He was diagnosed with two damaged discs and had to go on disability. The rate, he said, was “100 percent” responsible for his injury.

Every Amazon worker I’ve spoken to said it’s the automatically enforced pace of work, rather than the physical difficulty of the work itself, that makes the job so grueling. Any slack is perpetually being optimized out of the system, and with it any opportunity to rest or recover. A worker on the West Coast told me about a new device that shines a spotlight on the item he’s supposed to pick, allowing Amazon to further accelerate the rate and get rid of what the worker described as “micro rests” stolen in the moment it took to look for the next item on the shelf.

Rossetti explains that, more than anything else, the technology's speed and precision support the work of humans. At peak hours, a real-life bartender is always at the bar next to the robot; but the technology lets them delegate drinks like a vodka-lemonade, and instead focus on cocktails that require more of a human touch.

More importantly, the robot is utterly incapable of coming up with new cocktails; rather, it repeats the recipes invented by human professionals. "The bartender can invent any recipe and teach the robot how to repeat it," says Rossetti. "The machine has no capacity to decide what goes in a cocktail. It just has the speed and precision to repeat something it was shown by a human."

Things are changing, and the technology is gradually evolving from industrial, automated factory arms, towards resembling the robots typically found in science fiction: more human-like, and a lot more present in familiar settings.

Rather than being shoved away in safe spaces, robots are increasingly sharing our workplaces. Amazon's warehouses are already crowded with 200,000 robotic pickers, who work directly alongside hundreds of thousands of human staff to put together and ship customer orders at speed.