Is it OK to kill time? Machines used to find this question difficult to answer, but a new study reveals that artificial intelligence can be programmed to judge "right" from "wrong". Published in Frontiers in Artificial Intelligence, scientists have used books and news articles to "teach" a machine moral reasoning. Further, by limiting teaching materials to texts from different eras and societies, subtle differences in moral values are revealed. As AI becomes more ingrained in our lives, this research will help machines to make the right choice when confronted with difficult decisions.

With its reckless and violent driving scenarios, you may not imagine playing Grand Theft Auto would be an ideal way to teach anyone or anything to drive in the real-world. But the popular game is being used to train computers capable of controlling self-driving cars of the future to navigate streets. GTA V is said to give researchers access to a realistic and diverse world for testing and developing artificial intelligence, without putting vehicles on real roads. With its reckless and violent driving scenarios, you may not imagine playing Grand Theft Auto would be an ideal way to teach anyone or anything to drive in the real-world. Video games are the latest of a number of tools being used to test computers that could one day control cars on our roads.

General game players are computer systems able to play strategy games based solely on formal game descriptions supplied at "runtime" (n other words, they don't know the rules until the game starts). Unlike specialized game players, such as Deep Blue, general game players cannot rely on algorithms designed in advance for specific games; they must discover such algorithms themselves. General game playing expertise depends on intelligence on the part of the game player and not just intelligence of the programmer of the game player. GGP is an interesting application in its own right. It is intellectually engaging and more than a little fun.

Artificial systems that think and behave intelligently are one of the most exciting and challenging goals of Artificial Intelligence. Action Programming is the art and science of devising high-level control strategies for autonomous systems which employ a mental model of their environment and which reason about their actions as a means to achieve their goals. Applications of this programming paradigm include autonomous software agents, mobile robots with high-level reasoning capabilities, and General Game Playing. These lecture notes give an in-depth introduction to the current state-of-the-art in action programming. The main topics are knowledge representation for actions, procedural action programming, planning, agent logic programs, and reactive, behavior-based agents.