We describe the deployment of a learning by demonstration capability to support user creation of automated procedures in a collaborative planning environment that is used widely by the U.S. Army. This technology, which has been in operational use since the summer of 2010, has helped to reduce user work loads by automating repetitive and time-consuming tasks. The technology has also provided the unexpected benefit of enabling standardization of products and processes. Recent technical advances have enabled its use for automating increasingly complex tasks (Allen et al. 2007; Blythe et al. 2008; Burstein et al. 2008; Leshed et al. 2008; Cypher et al. 2010). However, fielded applications of the technology have been limited to macro recording capabilities, which can only reproduce the exact behavior demonstrated by the user.

Toward that end, this article presents PROTECT, a game-theoretic system deployed by the United States Coast Guard (USCG) in the Port of Boston for scheduling its patrols. USCG has termed the deployment of PROTECT in Boston a success; PROTECT is currently being tested in the Port of New York, with the potential for nationwide deployment. PROTECT is premised on an attackerdefender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior -- to the best of our knowledge, this is the first real-world deployment of the QR model. Second, to improve PROTECT's efficiency, we generate a compact representation of the defender's strategy space, exploiting equivalence and dominance.

This was the eighth in a regular series that started in 1997. There have been eight workshops in the series. At this year's workshop held March 25-26, 2013, at the NASA Ames Research Center, Moffett Field, California, there were 26 technical papers and two invited talks on a wide range of topic areas relating to planning and scheduling, including: techniques and algorithms, applications to space or aerospace, planning, scheduling, plan execution, knowledge acquisition for planning and scheduling systems, embedded planning and execution systems, and other general topic areas. Applications included International Space Station payloads, space mission operations, human space flight, space observatories, planning for communications, airborne operations, and Earth observation scheduling. International researchers from space agencies, academia, and industry from Europe, America, Canada, and South America participated.

Virtual humans are computer-generated characters designed to look and behave like real people. Studies have shown that virtual humans can mimic many of the social effects that one finds in human-human interactions such as creating rapport, and people respond to virtual humans in ways that are similar to how they respond to real people. We believe that virtual humans represent a new metaphor for interacting with computers, one in which working with a computer becomes much like interacting with a person and this can bring social elements to the interaction that are not easily supported with conventional interfaces. We present two systems that embody these ideas. The first, the twins are virtual docents in the Museum of Science, Boston, designed to engage visitors and raise their awareness and knowledge of science.

Mission planning for space is challenging because of the mixture of goals and constraints. Every space mission tries to squeeze all of the capacity possible out of the spacecraft. For Orbital Express, this means performing as many experiments as possible, while still keeping the spacecraft safe. Keeping the spacecraft safe can be very challenging because we need to maintain the correct thermal environment (or batteries might freeze), we need to avoid pointing cameras and sensitive sensors at the sun, we need to keep the spacecraft batteries charged, and we need to keep the two spacecraft from colliding... made more difficult as only one of the spacecraft had thrusters. Because the mission was a technology demonstration, pertinent planning information was learned during actual mission execution.

The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations. Stringent communications constraints are present, including limited communication windows, long communication latencies, and limited bandwidth. Additionally, limited access and availability of operators, limited crew availability, system complexity, and many other factors often preclude direct human oversight of many functions.

If the artificial intelligence research community is to have a challenge problem as an incentive for research, as many have called for, it behooves us to learn the principles of past successful inducement prize competitions. Those principles argue against the Turing test proper as an appropriate task, despite its appropriateness as a criterion (perhaps the only one) for attributing intelligence to a machine. Gary Marcus in The New Yorker asks "What Comes After the Turing Test?" and wants "to update a sixty-four-year-old test for the modern era" (Marcus 2014). Moshe Vardi in his Communications of the ACM article "Would Turing Have Passed the Turing Test?" opines that "It's time to consider the Imitation Game as just a game" (Vardi 2014). The popular media recommends that we "Forget the Turing Test" and replace it with a "better way to measure intelligence" (Locke 2014).

WWTS (What Would Turing Say?) Turing's Imitation Game was a brilliant early proposed test of machine intelligence -- one that is still compelling today, despite the fact that in the hindsight of all that we've learned in the intervening 65 years we can see the flaws in his original test. And our field needs a good "Is it AI yet?" test more than ever today, with so many of us spending our research time looking under the "shallow processing of big data" lamppost. If Turing were alive today, what sort of test might he propose? If you are reading these words, surely you are already familiar with the Imitation Game proposed by Alan Turing (1950). Turing was heavily influenced by the World War II "game" of allied and axis pilots and ground stations each trying to fool the enemy into thinking they were friendlies.

The Workshop on the Foundations of AI (WFAI) was held at the Holiday Inn, Las Cruces, New Mexico, on 6, 7, and 8 February 1986. Financial support for the workshop came from the National Science Foundation; the American Association for Artificial Intelligence (AAAI); and the Computing Research Laboratory (CRL) at New Mexico State University, which also hosted the meeting. My original vague idea for this workshop was backed enthusiastically by CRL right from the start, first by Roger Schvaneveldt as acting director and later by Yorick Wilks when he took over as director. Andrew Ortony played a leading role in both casting and production for this workshop; hc claims that he doesn't love telephoning people, just doesn't mind it. These three and the rest of the program committee, as well as a number of other people, reviewed the considerable number of submitted papers.

Woodrow Wilson (Woody) Bledsoe died on 4 October 1995 of ALS, more commonly known as Lou Gehrig's disease. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the joy of scientific research and the way to go about it. Woody's enthusiasm, his perpetual sense of optimism, his can-do attitude, and his deep sense of duty to humanity offered those who knew him the hope and comfort that truly good and great men do exist. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the ...