Many decision-making problems feature multiple objectives. In such problems, it is not always possible to know the preferences of a decision-maker for different objectives. However, it is often possible to observe the behavior of decision-makers. In multi-objective decision-making, preference inference is the process of inferring the preferences of a decision-maker for different objectives. This research proposes a Dynamic Weight-based Preference Inference (DWPI) algorithm that can infer the preferences of agents acting in multi-objective decision-making problems, based on observed behavior trajectories in the environment. The proposed method is evaluated on three multi-objective Markov decision processes: Deep Sea Treasure, Traffic, and Item Gathering. The performance of the proposed DWPI approach is compared to two existing preference inference methods from the literature, and empirical results demonstrate significant improvements compared to the baseline algorithms, in terms of both time requirements and accuracy of the inferred preferences. The Dynamic Weight-based Preference Inference algorithm also maintains its performance when inferring preferences for sub-optimal behavior demonstrations. In addition to its impressive performance, the Dynamic Weight-based Preference Inference algorithm does not require any interactions during training with the agent whose preferences are inferred, all that is required is a trajectory of observed behavior.

We develop simple methods for constructing parameter priors for model choice among Directed Acyclic Graphical (DAG) models. In particular, we introduce several assumptions that permit the construction of parameter priors for a large number of DAG models from a small set of assessments. We then present a method for directly computing the marginal likelihood of every DAG model given a random sample with no missing observations. We apply this methodology to Gaussian DAG models which consist of a recursive set of linear regression models. We show that the only parameter prior for complete Gaussian DAG models that satisfies our assumptions is the normal-Wishart distribution. Our analysis is based on the following new characterization of the Wishart distribution: let $W$ be an $n \times n$, $n \ge 3$, positive-definite symmetric matrix of random variables and $f(W)$ be a pdf of $W$. Then, f$(W)$ is a Wishart distribution if and only if $W_{11} - W_{12} W_{22}^{-1} W'_{12}$ is independent of $\{W_{12},W_{22}\}$ for every block partitioning $W_{11},W_{12}, W'_{12}, W_{22}$ of $W$. Similar characterizations of the normal and normal-Wishart distributions are provided as well.

If communication is measurable in terms of number of words, we are the greatest communicators in the history of our species. There's the rub, says President magazine. Listening is the hard part. It's harder than talking -- in part, writes Toshiyuki Goda of the NHK Broadcast Research Center in his contribution to President's feature on "listening ability," because, as speakers, we can keep the dialogue within our comfort zone, while listening may take us outside it -- into the realm of our own ignorance, which is uncomfortable enough; or, worse, into that of the awkward silence. The on-air broadcaster is particularly sensitive to the latter, but it bedevils all social intercourse.

Why you should listen Noriko Arai is the program director of an AI challenge, Todai Robot Project, which asks the question: Can AI get into the University of Tokyo? The project aims to visualize both the possibilities and the limitation of current AI by setting a concrete goal: a software system that can pass university entrance exams. In 2015 and 2016, Todai Robot achieved top 20 percent in the exams, and passed more than 70 percent of the universities in Japan. The inventor of Reading Skill Test, in 2017 Arai conducted a large-scale survey on reading skills of high and junior high school students with Japan's Ministry of Education. The results revealed that more than half of junior high school students fail to comprehend sentences sampled from their textbooks.

True, everyone born before Aug. 4, 1900, has proved mortal (the world's oldest-known living person, a Japanese woman named Nabi Tajima, was born on that date). But the past is only an imperfect guide to the future, as the effervescent present is ceaselessly teaching us. But our children, our grandchildren -- or if not them, theirs -- may, conceivably, be the beneficiaries of the greatest revolution ever: the conquest of death. Immortality is an ancient dream. A Chinese king of the third century B.C. dispatched a sage, Xu Fu by name, on a quest for the elixir of life.

The trouble with machines is, they do things better than we do. "Give me a place to stand and I will move the Earth," said the third-century B.C. Greek inventor Archimedes, lever in hand. The Earth has been moving ever since, ever faster. Still, from his time to ours, through mechanical evolutions and technological revolutions, a machine remained a machine. Lever or electric vacuum cleaner, inclined plane or automobile -- or personal computer or smartphone, for that matter -- humans commanded, machines obeyed.

For the thousands of secondary school students who take Japan's university entrance exams each year, test days are long-dreaded nightmares of jitters and sweaty palms. But the newest test taker can be counted on to keep its cool: AIs don't sweat. At Japan's National Institute of Informatics (NII), in Tokyo, a research team is trying to create an artificial intelligence program that has enough smarts to pass Japan's most rigorous entrance exams. The AI will start by taking the standardized test administered to all secondary school students; once it masters that test, it will move on to the more difficult University of Tokyo exam. "Passing the exam is not really an important research issue, but setting a concrete goal is useful," says Noriko Arai, the team leader and a professor at NII.

Thanks to the relentless work of dedicated engineers, artificial intelligence, or AI, becomes smarter by the day. But while computers become better at replicating human tasks, reading comprehension, an area where machines have yet to catch up, is declining among young people, suggesting a chilling future in which AI may put people out of work. That is why Noriko Arai, a mathematician at the National Institute of Informatics, decided in November to change the direction of her project from teaching an AI to pass the entrance exam for Japan's most prestigious school -- the University of Tokyo, better known as Todai -- to focusing on improving the reading comprehension of future generations using AI technology. "AI engineers have always said that humans don't have to worry because only menial jobs will be taken over by machines. But what about the people who do such jobs?"

The robot has repeatedly flunked the National Center Test since 2013. The team of creators, including members from the National Institute of Informatics, say they are finally quitting their efforts to make the robot smart enough to pass an entrance exam for admission. Mariella Moon, associate editor of Engadget web magazine, said, "Todai Robot's creators have concluded that since they failed to meet their goal this year, the AI can't become smart enough to get into Tokyo U by their March 2022 target date." It turns out the robot is not good at grasping "meaning in a broad spectrum," said Noriko Arai, a professor at the National Institute of Informatics, who heads the team behind Torobo-kun. Torobu-kun, for instance, did not perform well in English, where it had to link phrases to come to logical conclusions. It received scores of 36.2 in listening and 50.5 in written exams.

Former actress Saya Takagi was arrested in Okinawa on Oct. 25 for possession of marijuana, three months after she unsuccessfully ran for a seat in the Upper House election on a platform to legalize pot for medical purposes. She insists the contraband was not hers. Though Takagi, whose real name is Ikue Masudo, is retired from showbiz, reruns of dramas she appeared in are still shown on TV. When a celebrity is involved in a scandal, broadcasters scour their lineups for any ties to the disgraced person. TV Asahi quickly scrubbed from its afternoon schedule old episodes of the popular detective series "Aibo" that featured Takagi.